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Satellite Imagery FAQ - 3/5

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Archive-name: sci/Satellite-Imagery-FAQ/part3

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   This document is part of the Satellite Imagery FAQ
   


Subject: Image Basics Image Basics _Contributed by Wim Bakker (bakker@itc.nl)_ What is an image? A digital image is a collection of digital samples. The real world scene is measured at regular distances (=digital). One such measurement is limited in * Space One sample covers only a very small area from the real scene. * Time The sensor needs some integration time for one measurement (which is usually very short). * Spectral coverage The sensor is only sensitive for a certain spectral range. Furthermore, the sample is quantized, which means that the physical measure in the real world scene is represented by a limited number of levels only. Usually 256 levels of "grey" are sufficient for digital images; 256 levels can be represented by an eight bit unsigned Digital Number (DN). "Unsigned" because the amount of light is always positive. More levels will need more bits; the quantization determines the amount of bits per pixel on the image storage. Image samples are usually called _pixel_ or _pel_ after the combination of "picture" and "element". A pixel is the smallest unit of a digital image. The size of this unit determines the resolution of an image. The term _resolution_ is used for the detail that can be represented by a digital image. As discussed before the resolution is limited in four ways:
Subject: Resolution * Spatial resolution. If one pixel is a ground cell sample of 20 by 20 meter then no objects smaller than 20 meter can be distinguished from their background. This doesn't necessarily mean they cannot be _detected_! Note that if the spatial resolution doubles, the amount of image data increases by a factor 4! * Temporal resolution. A distinction can be made between + Temporal resolution of one image. Fast moving objects will appear blurred on one image. E.g. the temporal resolution of one TV image is about 1/25 of a second. + Temporal resolution of a time series of images. If the images are taken sparsely in time then the possibility exists that some phenomena will be missed. The resolution of Landsat is 16 days, of SPOT 26 days and of NOAA 4 hours. So the latter satellite is said to have a _high_ temporal resolution even though the spatial resolution is _low _compared to the two other satellites! (1.1 km and 20-30 m) * Spectral resolution. Current imaging satellites usually have a broad band spectral response. Some airborne spectrometers exist that have a high spectral resolution; AVIRIS Airborne Visible/Infrared Imaging Spectrometer (from NASA/JPL) has 224 bands, GERIS Geophysical and Environmental Research Imaging Spectrometer has 63 bands. * Quantization. E.g. if 100 Lux light gives DN 200 and 110 Lux yields DN 201 then two samples from the original scene having 101 and 108 Lux will both get the DN 200. Values from the range 100 up to 110 Lux can not be distinguished. ======================== Image Formats (HTML) ====================== _Contributed by Wim Bakker (bakker@itc.nl)_
Subject: Image Formats Image data on tape Looking at the images stored on tape there's three types of information * Volume Directory, which is actually meta-information about the way the headers/trailers and image data itself are stored * Information about the images This information can be stored in separate files or together with the image data in one file. This information can be virtually anything related to the image data + Dimensions. Number of lines, pixels per line and bands etc. + Calibration data + Earth location data + Orbital elements from the satellite + Sun elevation and azimuth angle + Annotation text + Color Lookup tables + Histograms + Etc. etc... The information is often called a _header_, information _after_ the image data is called a _trailer_ * The pure image data itself The image data can be arranged inside the files in many ways. Most common ones are * BIP, Band Interleaved by Pixel * BIL, Band Interleaved by Line * BSQ, Band SeQuential If the pixels of the bands A, B, C and D are denoted a, b, c and d respectively then _BIP_ is organized like abcdabcdabcdabcdabcdabcdabcdabcdabcd... line 1 abcdabcdabcdabcdabcdabcdabcdabcdabcd... line 2 abcdabcdabcdabcdabcdabcdabcdabcdabcd... line 3 ... abcdabcdabcdabcdabcdabcdabcdabcdabcd... abcdabcdabcdabcdabcdabcdabcdabcdabcd... BIP can be read with the following pseudo-code program FOR EACH line FOR EACH pixel FOR EACH band I[pixel, line, band] = get_pixel(input); _BIL_ looks like aaaaaaaaaaaa... band 1, line 1 bbbbbbbbbbbb... band 2 cccccccccccc... band 3 dddddddddddd... band 4 aaaaaaaaaaaa... band 1, line 2 ... BIL can be read with the following pseudo-code program FOR EACH line FOR EACH band FOR EACH pixel I[pixel, line, band] = get_pixel(input); _BSQ_ shows aaaaaaaaaaaa... line 1, band 1 aaaaaaaaaaaa... line 2 aaaaaaaaaaaa... line 3 ... bbbbbbbbbbbb... line 1, band 2 bbbbbbbbbbbb... line 2 bbbbbbbbbbbb... line 3 ... cccccccccccc... line 1, band 3 cccccccccccc... line 2 cccccccccccc... line 3 ... dddddddddddd... line 1, band 4 dddddddddddd... line 2 dddddddddddd... line 3 ... BSQ can be read with the following pseudo-code program FOR EACH band FOR EACH line FOR EACH pixel I[pixel, line, band] = get_pixel(input); Of course others are possible, like the old _EROS BIP2_ format (for four band MSS images) where the image is first divided into four strips. EROS BIP2 strips Then each strip is stored like aabbccddaabbccddaabbccddaabbccdd... line 1 aabbccddaabbccddaabbccddaabbccdd... line 2 ... To decode one strip the following pseudo-code can be used /* The '%' character is the modulo operator */ /* Note that operations on 'i' are integer operations! */ /* Copyright 1994 by W.H. Bakker - ITC */ FOR EACH line FOR i=0 TO BANDS*WIDTH I[(i/8)*2+i%2, line, (i/2)%4] = get_pixel(input); Subsequently, the strips must be glued back together. _________________________________________________________________
Subject: Basic Processing Levels What are the different types of image I can download/buy? _Very brief - needs a proper entry_ Raw data (typically Level 0) (as with other levels, annotated with appropriate metadata). Only useful if you're studying the RS system itself, or data processing systems Processed Images (typically Level 1, 2) Processing includes: + Radiometric correction - compensating for known characterisitcs of the sensor. + Atmospheric correction - compensating for the distortion (lens effect) of the atmosphere. + Geometric correction - referencing the image to Lat/Long on the Earth's surface, based on the satellite's position and viewing angle at the time of the acquisition. Uses either a spheriod model of Earth or a detailed terrain model; the latter enables higher precision in hills/mountains. Requires Ground Control Points (GCPS: points in the image which can be accurately located on Earth) for high precision. The various part-processed levels are suitable for a image processing studies. Most Remote Sensing and GIS applications will benefit from the highest level of processing available, including geocoding. Geocoded Projected Imagery (typically Level 3) The image is mapped to a projection of the Earth, and in some cases also composited (ie several images are mosaiced to show a larger scene). Browse Images Images you can download from the net are likely to be browse images. These are typically GIF or JPEG format, although a number of others exist. Whilst providing a good idea of what is in an image, they are not useful for serious applications. They have the advantage of being a manageable size - typically of the order of 100Kb-1Mb (compared to 100Mb for a full scene) and are often available free. A browse version of any image (except raw data) can be made. Stereopairs Multitemporal Images
Subject: Is there a non-proprietary format for geographical/RS images? Is there a non-proprietary format for geographical/RS images? The GeoTIFF format adds geographic metadata to the standard TIFF format. Geographic data is embedded as tags within an image file. For a detailed description, see the spec. at http://www-mipl.jpl.nasa.gov/cartlab/geotiff/geotiff.html
Subject: Do I need geocoded imagery? Do I need geocoded imagery? In a recent discussion of mountain areas, John Berry (ej10jlbs@shell.com) wrote: The problem that Frank has is that he is working in an area without adequate maps: therefore, he cannot geocode his Landsat using a DTM, because the data available is neither detailed enough or accurate enough to use as an input. He can georegister the imagery using using one or two accurately located ground control points and the corner-point positions given in the image header: these are calculated from ephemeris data of, usually, unknown accuracy (within +/- 1 km), but internal image geometry is good so an x,y shift and a (usually) very small rotation can take care of everything to better than the accuracy of his maps. Positions used should be topographically low, and at the same elevation. GPS is the best solution, as someone else pointed out, if Frank can get in the field. The next problem is the parallax error introduced by the high relief. In his situation, the only answer* is to get SPOT stereopairs and make a DTM or DEM from them. Except in the case of very narrow gorges or slopes steeper than 60 deg. there should be few problems with carefully chosen images (high sun angles, etc). ERDAS has an excellent module for doing this. However, I doubt that Frank has the budget. I believe ERDAS`s Ortho module would then allow Frank to make an Ortho image that would be a perfectly good map. *there may be some LFC or Russian stereo coverage in this area, which would be a lot cheaper than SPOT but would require the use of analog stereo comparators (probably). Even if there were good topographic contour maps for all of Frank's area, the cost of digitising these and turning them into a usable DTM would probably be prohibitive (though there are outfits in Russia who might be able to quote a price affordable to a large western company).
Subject: Imaging Instruments Imaging Instruments How do Remote Sensing Instruments work? If you put a camera into orbit and point it at the Earth, you will get images. If it is a digital camera, you will get digital images. Of course, this simplistic view is not the whole story. Digital images comprise two-dimensional arrays of pixels. Each pixel is a sensor's measurement of the albedo (brightness) of some point or small area of the Earth's surface (or atmosphere, in the case of clouds). Hence a two-dimensional array of sensors will yield a two-dimensional image. However, this design philosophy presents practical problems: a useful image size of 1000x1000 pixels requires an array of one million sensors, along with the corresponding circuitry and power supply, in an environment far from repair and maintenence! Such devices (charge coupled deices) do exist, and are essentially similar to analogue film cameras. However, the more usual approach for Earth Observation is the use of tracking instruments: Tracking Instruments 1. A tracking instrument may use a one-dimensional array of sensors - one thousand rather than one million - perpendicular to the direction of the satellite's motion. Such instruments, commonly known as pushbroom sensors, instantaneously view a line. A two-dimensional image is generated by the satellite's movement, as each line is offset from its predecessor. If the sampling frequency is equal to the satellite's velocity divided by the sensor's field of view, lines scanned will be contiguous and non-overlapping (although this is of course not an essential property). _btw, would the above be better expressed in some ASCII representation of mathematical notation?_ 2. Another approach is to use just a single sensor. It is now not sufficient to use the satellite's motion to generate an image: cross-track scanning must also be synthesised. This is accomplished by means of a rotating mirror, imaging a line perpendicular to the satellite motion. These are known as scanning instruments. This is somewhat analagous to the synthesis of television pictures by CRT, although the rotating mirror is a mechanical (as opposed to electromagnetic) device. As the sensor now requires a large number of samples per line, the sampling frequency necessary for unbroken coverage is proportionally increased, to the extent that it becomes a design constraint. A typical Earth Observation satellite moves at about 6.5 Km/sec, so a 100m footprint requires 65 lines per second, and higher resolution imagery proportionally more. This in turn implies a sampling rate of 65,000 per second for a 1000-pixel swath. This may be alleviated by scanning several lines simultaneously. Either design of scanning instrument may have colour vision (ie be sensitive to more wavelength of light) by using multiple sensors in parallel, each responding to one of the wavelengths required. List of Imaging Spectrometers http://www.geo.unizh.ch/~schaep/research/apex/is_list.html
Subject: What is a Sounding Instrument? What is a Sounding Instrument? _Answer posted by Wayne Boncyk (boncyk@edcsgw4.cr.usgs.gov) to IMAGRS-L_ Satellite-borne remote sensing instruments may be used for more than imaging; it is possible to derive information about the constituents of the local atmosphere above a ground target, for example. One common area of study is to observe atmospheric emissions in the spectral neighborhood of the 183GHz water absorption line (millimeter-wave; in-between microwave and thermal IR). These channels can be monitored by an appropriate collection of narrow passband radiometers, and the data that are returned can be analyzed to deduce the amount of water vapor present at different levels (altitude layers) in the atmosphere. The reference to "sounding" is an application of an old nautical term, the investigation of the state of a medium at different depths (original application: the ocean - specifically determination of the depth of the ocean floor).
Subject: Orbits Orbits _Need a general entry here!_ Where can I learn about satellite orbits? Wim Bakker has compiled a list of online references at http://www.itc.nl/~bakker/orbit.html. Wim adds the question _"When can *I* see a specific satellite"_, and suggests the following pointers from his list: * Visual Satellite Observer's Home Page: http://www.rzg.mpg.de/~bdp/vsohp/satintro.html * Satellite Observing Resources: http://www-leland.stanford.edu/~iburrell/sat/sattrack.html Satellite Orbital Elements _Thanks to Peter Bolton (pbolton@clyde.pc.my) for this one!_ Jonathan's Space Report is at http://hea-www.harvard.edu/QEDT/jcm/jsr.html. The introduction: The Space Report ("JSR") is issued about once a week. It describes all space launches, including both piloted missions and automated satellites. Back issues are available by FTP from sao-ftp.harvard.edu in directory pub/jcm/space/news. To receive the JSR each week by direct email, send a message to the editor, Jonathan McDowell, at jcm@urania.harvard.edu. Feel free to reproduce the JSR as long as you're not doing it for profit. If you are doing so regularly, please inform Jonathan by email. Comments, suggestions, and corrections are encouraged. How do I convert Landsat Path/Row to Lat/Long? In response to this question, Wim Bakker wrote: The SATCOV program is available by anonymous FTP from sun_01.itc.nl (192.87.16.8). Here's how to get it: $ ftp 192.87.16.8 Name: ftp Password: your-email-address ftp> bin ftp> idle 7200 ftp> prompt ftp> cd /pub/satcov ftp> mget * ftp> bye $ If you can't use FTP, drop me a line and I will send a uuencoded version by email. Those of you who prefer a WWW interface can obtain it from the following URL: http://www.itc.nl/~bakker/satcov Don't forget to set the "Load to local disk" option. SATCOV is a PC program for converting Path/Row numbers of Landsat and K/J of SPOT to Lat/Lon and vice versa. Furthermore it can predict the orbits of the NOAA satellites, although I wouldn't recommend it for this purpose! But that's an other can of worms....
Subject: Ground Stations How is satellite data recieved on the ground? _Intro to Ground Recieving Stations contributed by Peter Bolton <pbolton@clyde.pc.my>_ 1. GROUND RECEIVING STATIONS This document is an introduction to Ground Receiving Station (GRS) acquisition and processing of remote sensing satellites data such as SPOT, LANDSAT TM and ERS-1 SAR. Ground receiving stations regularly receive data from various satellites so as to provide data over a selected areas (a footprints approximately covers a radius of 2500 km at an antennae elevation angle of 5 degrees.) on medium such as computer tape, diskette or film, and/or at a specific scale on photographic paper. GRS are normally operated on a commercial basis of standard agreements between the satellite operators and the Governments of the countries in which they are situated. Subject to the operating agreements, local GRSs sell products adapted to end users needs, and provide remote sensing training, cartography, and thematic applications. 2. GROUND RECEIVING STATION ARCHITECTURE A Ground Receiving Station consists of a Data Acquisition System (DAS), a Data Processing (DPS) and a Data Archive Center (DAC). 2.1. DATA ACQUISITION SYSTEM DAS provides a complete capability to track and receive data from the remote sensing satellite using an X/S-band receiving and autotracking system on a 10 to 13meter antenna in cassegranian configuration. DAS normally store fully demodulated image data and auxiliary data on High Density Digital Tapes (HDDTs). However, in one small UNIX based system, data storage can be stored directly on disk and/or electronically transmitted to distant archives. 2.2. DATA PROCESSING SYSTEM DPS keeps an inventory of each satellite pass, with quality assessment and catalog archival, and by reading the raw data from HDDTs, radiometrically and geometrically corrects the satellite image data. 2.3.DATA ARCHIVE CENTRE The Data Archive closely related to DPS offers a catalog interrogation system and image processing capabilities through an Image Processing System (IPS). 3. GROUND RECEIVING STATION PRODUCTS The GRS products can either be standard or value added products. Both are delivered on Computer Compatible Tapes (CCTs), CD ROM, cartridges, photographic films or photographic paper prints at scales of 1:250 000, 1:100 000, 1:50 000 and 1:25000. i. Standard products - SPOT-1 and 2/HRV : data of CNES levels 0, 1A, 1B, 2A - Landsat TM : data of LTWG levels 0, 5, - ERS-1 SAR : Fast Delivery and Complex products. ii. Value added products - For SPOT . P + XS : Panchromatic plus multi-spectral, . SAT : a scene shifted along the track, . RE : a product made of 2 consecutively acquired scenes, . Bi-HRV : Digital mosaic produced by assembling 2 sets of 2 scenes acquired in the twin-HRV configuration. . Stereoscopy : Digital terrain model (DTM) generation, . Levels 2B, S and level 3B using DTMs. - For Landsat TM: levels 6, S and 7. - For ERS-1 SAR : geocoded data. - For any instrument: . Image enhancement and thematic assistance, . Geocoded products on an area of interest defined by the customer (projection, scale, geocoding and mosaicking according to the local map grid). 4. GROUND RECEIVING STATION OPERATION Persons needing images for thematic applications in the field of cartography, geology, oceanography or intelligence, etc, will refer to the station catalog in order to find out if the data are available over the area concerned. There are two possibilities : The data exists. The customer fills in a purchase order and is then provided with the product on a medium such as CCT, film or paper print. If the data are available in the GRS catalog, a list of the related scenes and their hardcopies (named "quick looks") are provided. The data does not exist. a) For SPOT, the customer fills in a programming request form which is sent by GRS to the Mission Control Centre (MCC) in Toulouse, France. MCC returns a Programming Proposal to be submitted for approval. Upon approval, the confirmation is returned to MCC which in turn sends a programming order to the satellite for emitting the data during its pass over the GRS antenna. At the same time, MCC sends to GRS, the satellite ephemerides for antenna pointing and satellite tracking. In the case of SPOT, if the data does not exist within the Station catalog but are listed in the SPOT IMAGE worldwide catalog, GRS may request the level O product from SPOT IMAGE in TOULOUSE in order to process it locally. b) For other sensors, LANDSAT TM or ERS-1, the satellite ephemerides are known at GRS and the antenna is pointed accordingly in order to track all selected passes. Within the GRS, the raw satellite data are received by the Data Acquisition System (DAS), and recorded on High Density Digital Tapes (HDDTs). HDDTs are then sent to the Data Processing System (DPS), where an update of the Station catalog is made as well as a quick look processing. DPS is also in charge of automatic processing of selected raw data in order to produce images of standard level. Value added products with cartographic precision are produced within DPS using interpretation workstations which must be part of an operational Geographic Information System (GIS) combined to an Image Processing System (IPS). Once processed, the data, on CCT, are sent to the Data Archive Center (DAC) where they are delivered to the customers after a quality checking. At DAC, further processing may be applied to the data such as image stretching, statistical analysis, DTM, or a conversion from tape to film and paper prints in the photographic laboratory; "customized services" may also be offered. _________________________________________________________________ Image Interpretation
Subject: How can I assess my results? How can I assess my results? _(for basics, see Russell Congalton's review paper In Remote Sens. Environ. 37:35-46 (1991). Think we should have a basics entry here too!)_ Michael Joy (mjoy@geog.ubc.ca) posted a question about Contingency table statistics and coefficients, and subsequently summarised replies: Second, a summary of responses to my posting about contingency table statistics and coefficients. Basically, I need to come up with a single statistic for an error matrix, along the lines of PCC or Kappa, but which takes into account the fact that some miscalssifications are better or worse than others. Tom Kompare suggested readings on errors of omission or commission. Chris Hermenson suggested Spearman's rank correlation. Nick Kew suggested information-theoretic measures. Others expressed interest in the results; I'll keep them posted in future. The responses are summarized below. =============================================================================== Michael: Your thinking is halfway there. Check out how to use an error matrix to get + errors of Omission and Commission. Good texts that explain it are: Introduction to Remote Sensing, James Campbell, 1987, Gulliford Press start reading on page 342 Introductory Digital Image Processing, John Jensen, 1986, Prentice-Hall start reading on page 228 or so. These are the books where I learned how to use them. Sorry if you don't have + access to them, I don't know how Canadian libraries are. Tom Kompare GIS/RS Specialist Illinois Natural History Survey Champaign, Illinois, USA email: kompare@sundance.igis.uiuc.edu WWW: http://www.inhs.uiuc.edu:70/ ============================================================================ Excerpt from my response to Tom Kompare (any comments welcome...) These are useful readings describing error matrices and various measures we can get from them, eg PCC, Kappa, omission/commission errors. But from these + readings I do not see a single statistic I can use to summarize the whole matrix, which takes into account the idea that some misclassifications are worse than others (at least for me). For example, if I have two error matrices with the same PCC, but with tendencies to confuse different categories , I'd like to get a ststistic which selects the 'best' matrix (ie the best image) . One simple way I can think of to do this is to supply a matrix which gives a 'score' for each classification or misclassification, and then multiply each number in the error matrix by the corresponding number in the 'score' matrix. So a very simple example of such a matrix might look like this: Deciduous Conifer Water Decid 1.0 0.5 0.0 Conifer 0.5 1.0 0.0 Water 0.0 0.0 1.0 In this notation, the 'score' matrix for a PCC statistic would be a diagonal matrix of "1". Obviously there are a number of issues for me to think about in using such a matrix, eg can you 'normalize' the score matrix? Can you use it to compare different matrices with different numbers of categories? An obvious extension to this would be to apply this idea to the Kappa statistic as well. =========================================================================== Hi Michael; Spearman's rank correlation is often used to test correlation in a situation where you are scoring multiple test results. You might be able to adapt it to your problem. Chris Hermansen Timberline Forest Inventory Consultants Voice: 1 604 733 0731 302 - 958 West 8th Avenue FAX: 1 604 733 0634 Vancouver B.C. CANADA clh@tfic.bc.ca V5Z 1E5 C'est ma facon de parler. ========================================================================= Hi, Your question touches on precisely the field of research I'd like to be pursuing, if only someone would fund it:) > Hi, > I'm comparing different datasets using contingency tables, and I would > like to come up with summary statistics for each comparison. I am using > the standard PCC and Kappa, but I'd also like to come up with a measure > which somehow takes into account different 'degrees' of misclassification. > For example, a deciduous stand misclassified as a mixed stand is not as > bad as a deciduous stand misclassified as water. I would strongly suggest you consider using information-theoretic measures. The basic premise is to measure information (or entropy) in a confusion matrix. I can send you a paper describing in some detail how I did this in the not-totally-unrelated field of speech recognition. This does not directly address the problem of 'degrees of misclassification' - just how well it can be used to do so is one of the questions wanting further research. However, there are several good reasons to use it: 1) It does address the problem to the extent that it reflects the statistical distribution of misclassifications. Hence in two classifications with the same percent correct, one in which all misclassifications are between deciduous and mixed stands will score better than one in which misclassifications are broadly distributed between all classes. Relative Information is probably the best general purpose measure here. 2) By extension of (1), it will support detailed analysis of hierarchical classification schemes. This may be less relevant to you than it was to me, but consider two classifiers: A: Your classifier - which for the sake of argument I'll assume has deciduous, coniferous and mixed woodland classes. B: A coarser version of A, having just a single woodland class. Now using %correct, you will get a higher score for B than for A - the comparison is meaningless. By contrast, using information (Absolute, not Relative in this case), A will score higher than B. You can directly measure the information in the refinement from B to A. > In effect I guess I'm > thinking that each type of misclassification would get a different 'score', > maybe ranging from 0 (really bad misclassification) to 1 (correct > classification). I've thought a little about this, as have many others. The main problem is, you're going to end up with a lot of arbitrary numerical coefficients, and no objective way to determine whether they are 'sensible'. Fuzzy measures can be used, but these are not easy to work with, and have (AFAIK) produced little in the way of results in statistical classification problems. > I can invent my own 'statistic' to measure this, but if there are any such > measures available I'd like to use them. Any ideas? Take the above or leave it, but let me know what you end up doing! Nick Kew nick@mail.esrin.esa.it ============================================================================ -- Michael Joy mjoy@geog.ubc.ca University of British Columbia, Vancouver, B.C., Canada
Subject: Is there a program to compute Assessment measures, including Kappa coe fficients? Is there a program to compute Assessment measures, including Kappa coefficients? Nick Kew's assess.c (ANSI C source code to compute several assessment measures, including PCC, Kappa, entropy and Mutual and Relative Information) is available for download from the WebThing site, http://pobox.com/%7Esatfaq/ or from the satfaq autoresponder (mail to satfaq@pobox.com with subject line "send assess.c"). _Old reference to Dipak Ram Paudyal's kappa program deleted, as the FTP server is apparently no longer available._
Subject: How good are classification results in practice? How good are classification results in practice? The following detailed commentary was posted by Chris Hermansen (clh@tfic.bc.ca). Mike Joy posted a question regarding irregularities between two classifications, one derived from manual interpretation of large-scale aerial photography, the other from a supervised and enhanced spectral classification of Landsat TM imagery. I've read several of the responses, and I just thought it time to kick in my $0.02 worth, since I am quite familiar with both of the classifications with which Mike is working. First, Peter Bolton rattles off his experience in tropical forests and chastises Mike for discovering what should have been obvious. Well, Peter, the boreal forest is a much different beast than what you're used to in Malaysia (I can attest from firsthand experience in both cases). Classification from remotely sensed data is generally quite reliable in the boreal forest, especially given the vegetative nature of the TM-derived classification that is Mike's second dataset. Detecting predominantly deciduous from predominantly coniferous stands is (spectrally speaking) pretty straightforward. Problems arise in mixedwood stands, however, since the nature of the classification of proportion is not necessarily the same and in any case any aggregative techniques applied to the TM image prior to classification (eg smoothing) could significantly alter the proportional balance. Also, depending on the proportion of deciduous in a predominantly coniferous stand, and the spatial distribution of deciduous trees within that stand, the classifier may have difficulty detecting the differences between mixedwood and younger pure coniferous types. Furthermore, deciduous stands with coniferous understory are classified as deciduous in Mike's first dataset but may easily be interpreted as mixedwood stands in the TM image. Secondly, on the subject of incorporation of field data, Mike's second dataset has some ground truthing incorporated in the classification. Thirdly, on the subject of large numbers of classes in some people's TM-derived classifications, remember that in many cases these additional classes are derived by incorporating other datasets (field measurements, other digital map data, DEM information, etc). The people I've seen most test this envelope are the folks at Pacific Meridan Resources; their TM-derived datasets form only the first step of several. As Vincent Simonneaux points out, most people stop at the first step. So, in response to Mike's original questions: > 1) Is it reasonable to expect a TM-based classification to accurately > distinguish Coniferous and Deciduous forest? The area I am dealing > with is boreal mixedwood forest in northeren Alberta, Canada. I had > expected that the classification should at least be able to do this. On the face of it, yes. But! You must ensure that your definition of Coniferous and Deciduous forest is exactly the same in both cases (and the prevailing definitions in use in Alberta don't exactly help out in this case). > 2) Do people out there have similar experiences, i.e. the actual >classification > accuracy being very much lower than the reported results, or major > differences when comparing with different source of information? Of course, this is a possibility; the most unreliable classes may interfere in a nasty way between to datasets. You really need to ensure that you are sampling the same population in both cases; then you need to examine the distribution of errors among classes in both cases. In your first dataset, you don't really have error estimates with which to work. > I > understand that an air-photo-based forest inventory and a TM satellite >image > are measuring different things, and that I shouldnt expect perfect >agreement, > but I would have thought they could agree roughly on the overall area of > Coniferous or Deciduous forest. Ditto for two similar TM-based > + classifications. Once more, not necessarily. See the points above on coniferous understory in deciduous stands and the basic definitions of coniferous/deciduous split. There are, of course, really obvious errors that can occur, like using pre-leaf or post-leaf images when trying to locate deciduous stands... Sorry to go on at such length about this; I hope that my comments are of interest to some of you.
Subject: I need to classify a mosaic of several images. How best to do it? I need to classify a mosaic of several images. How best to do it? David Schaub (dschaub@dconcepts.com) posted a question on this. Here is his summary of replies: Dear Netters, Some time ago I posed a question to this list with regards to classification, rectification, and mosaicking. My original question was as follows-- >Hello, >We need to georectify, mosaic, and classify several (3 or 4) Landsat TM >scenes using ERDAS Imagine. The classification will need to show major >land cover categories, such as bare ground, grassland, shrubby range, >built-up, coniferous forest, broad-leaf forest, water, etc. In the past >when we have done this the seams between images are quite evident in the >classification. We would like to minimize differences between images, yet >be asaccurate as possible in the classification of each image. >My main questions are these -- Should we classify each image separately >and then mosaic them, or should we mosaic the images first and then >classify them? Can georectifying the images effect the classification? >You can assume that images along a path will have the same acquisition date, >however scenes on adjacent paths will have different dates (at least by two >weeks). I will post a summary. Thanks in advance for your opinions :-) This quickly generated a flood of responses. While there wasn't complete agreement, the majority of respondents believed that I should first classify the images, then do the rectification and mosaicking. Nearest neighbor should be used when rectifying the classified image (or if the image data are rectified before classification). Thanks to all who responded!! Comments are summarized below: David Schaub dschaub@dconcepts.com ******************************************************************* I have done the same things you are attempting to do for my thesis work. I think the best course of action would be to classify the images first, then rectify the images and then merge or mosaic the images. Rectifying the images before you classify may distort the spectral characteristics of pixels and thereby influence your classification. Furthermore, the smaller the area you are classifying, the more accurate the classification will be, so if you mosaic a large area and then attempt to classify the mosaiced image, there will be more confusion possible based on the heterogeneity of a larger area. I hope this helps, contact me if i can be of further assistance. David Smith ************************************************************* Here's my 2c for what it's worth... I classify TM scenes separately and then mosaic the classifications. My classifications almost never have a seam in them...If there is a seam it is usually due to the difference in the date of the scene. You have to be careful though... you need to use the same method of classification (plotting out feature spaces and elipses helps) for overlapping scenes. Sometimes this is why people use the other method... If you're going to do this the other way round...i.e. mosaic and then classify scenes you will have to calibrate the scenes to radiance and then use some kind of atmospheric correction before mosaicking them. This should in theory minimize the difference in the spectral information between scenes....I would avoid using any kind of histogram equalization ...although it may look nice, you are loosing the original pixel information. \\. _\\\_____ \\\ /ccccccc x\ Fiona Renton, GIS and remote sensing analyst >>Xccccccc( < CALMIT, Conservation and Survey Division /// \ccccccc\_/ University of Nebraska-Lincoln '' ~~~~ renton@fish.unl.edu ********************************************************************** What sort of classification? Pixels? Clusters? Polygons? Higher-level features? If your classification units are homogenous and shape is not important, you should clearly do it before mosaicing. If not, you have a genuinely interesting problem, and will probably have to your own research (starting at your local academic library, assuming there is one :-) Nick. ********************************************************************* Geo-rectification will have a small effect on classification due to the resampling process. I can't help to much on classification part, because that is not my area, but my feeling is that mosaicking non-classified images may be easier than trying to match features in a classified image. Ok, this is my area. You can not assume that images on the same path are imaged on the same day, However, they could be. You should be able to check the meta-data to find out if they were. The next path west could have been imaged 7 days after the path of interest or 9 days before and the next path east could have been imaged 9 days after the path of interest or 7 days before, again check the meta-data. The next chances are to add 16 days on to those numbers (i.e. 7 + 16). This is true for Landsat 4 and 5 only (will be true for Landsat 7). Chuck wivell@edcsnw38.cr.usgs.gov ************************************************************ Yes the georectification process will affect the classification results. My suggestion is to classify each individual image first and then mosaic them together. I have done this before and it works well. If you mosaic first and then classify you have to calibrate the data, apply radiometric corrections etc... Not worth the trouble in my opinion, and you probably won't get any good results. The resampling technique (convolution) will affect the radiometric value of the image and may not be suitable for adequate identification aftrewards. To avoid visible seams, just go around the areas, try to contour the natural groupings (classes after classification) To resume, in my opinion, if you want good accurate results: Classify first and after mosaic. Francois Beaulieu ************************************************************ You definitely want to mosiac the 4 images first (into one file) and then run the classification on that. Because of subtle differences in the radiometric characteristics of each image, the classes in separate classifications will rarely "line up" perfectly when mosaicked afterward. > Can georectifying the images effect the classification? Yes it can, depending on the resampling technique you use. When rectifying the images, use Nearest Neighbor resampling as that will ensure that original pixel values are used to create the new rectified dataset. (Bilinear or Cubic will average the original data, resulting in slight degradation.) I would: 1) Rectify the four images (use Nearest Neighbor) 2) Contrast balance them, using for example Histogram Matching or another technique. 3) Mosaic the four contrast balanced scenes into one file. 4) Run the classification. I hope this helps. Eric Augenstein Manager of Training Services ************************************************************* In general you can't depend on the DN values from one image to the next to be related. You should classify before your mosaic - in other words mosaic the classification, not the images. Otherwise you mix unrelated DN values into a signal classification which would be wrong. Classification may be affected by geo-rectification. If the geo-rectified image has the same pixels and pixel values as the original, the classification should not be affected. However, this is an unreal assumption. A geo-rectified image will almost always have resampling - which means that pixels are either dropped or replicated - unless a filter is applied (like bilinear or cubic convolution) in which case the pixel values change as well. If the classifier is single pixel based (like isodata) then the classification is only affected by the resampling as the sigatures are affected by the replication or dropping of values. If the classifier is regional or global (like multi-resolution/multi-scale classifiers, or region linking) then the classifiers may be affected to a greater degree. You can classify before or after geo-rectifiction and the results will not be vastly different. But the bottom line to mosaic at the very end. Michael Shapiro mshapiro@ncsa.uiuc.edu NCSA (217) 244-6642 605 E Springfield Ave. RM 152CAB fax: (217) 333-5973 Champaign, IL 61820 ******************************************************** Re Michael Shapiro's posting, There is no doubt that that you cannot depend on the DN values from one image to the next (especially with images from adjacent paths which are taken on different dates (see Chuck Wivell's posting). However mosaicing images which have been classified seperately may produce unusual results ie trying to match classes from different images. A suggestion would be to first try some kind of atmospheric correction on the images, mosaic them and then classify them together. Assuming i) you can do a credible atmospheric condition (using Dark Pixel Substraction, Band Regression etc) plus, perhaps, correct the images to a constant solar elevation angle ii) the images from different paths were not taken on widely different dates and iii) (linked to ii) the ground conditions are similar for the images from different paths then the DN values between images should be comparable. Euan ************************************************************ We are currently doing a statewide land cover classification for Mississippi using TM scenes (10 of them). My responses for your questions: 1. We classified each scene separately - mainly because the dates differed and in the cases where we had adjoining scenes taken on the same day, it was decided that classifying a full scene was a big enough task in both computer and human resources. If you had subscenes, it would not be too bad. I would advise against mosaicking scenes before classifying - your signatures for the same landcover class in the other scene(s) would be different and it would be a nightmare. Matching techniques that changed image pixel values would change your original data and corrupt your classification. 2. We also georeferenced each scene before classification for the following reasons: - georeferenced ancillary data sources (roads, streams, NWI, etc) were used - including leaf-off TM scenes already in-house. - the need to have maps to take into the field for pre and post classification checks. We used nearest neighbor. This doesn't change pixel values but just moves them to a different location. In our case the image statistics were unchanged after georectification although it is probable that some pixels may be dropped or replicated (but when you georeference the classified image, those same pixels are going to be affected anyway). Bottom line would be to classify each scene separately. I would georeference each TM scene first - when the classiciations are completed, stitching is easy. Jim ************************************************************ Our lab has had luck using regression techniques to mosaic the three bands together. Using ERDAS imagine, the steps are: 1) create an image where the two scenes overlap (this is best done with modeller, not layerstack: layerstack only uses the geographical boundaries, whereas you want to have the area where there are values in both images 2) Use the Accuracy Assessment module to create random points on the image and remove those points which lie in cloud or shadow. 3) Export the X,Y coordinates from the random points and use these as a point file in the Pixel-to-Table function. Use the overlap image as the output image (make sure you have all the bands you want to regress (ie. image one's band 3,4,5 on top of image 2's 3,4,5 4) You now have a set of points that can be imported into any standard statistical package. You need to have the values from the "larger" or primary image be the Y values and the other image be the X value (I'm told the correct statistical term is that the Y is the master and the X is the slave). This should create a seamless image. Obviously, the closer the B number in the Y= bx + constant equation is to 1, the less you are transforming the values of your slave image. We have also tried doing classifications of each image first, but the results have been disappointing. Regards, Sean Murphy University of Maine ********************************************************************

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