%0 Conference Paper %1 Brumby:2001:SPIE %A Brumby, S. P. %A Bloch, J. J. %A Harvey, N. R. %A Theiler, J. %A Perkins, S. %A Young, A. C. %A Szymanski, J. J. %B In Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VII, Proceedings of SPIE %D 2001 %E Shen, Sylvia S. %E Descour, Michael R. %K Aladdin Forest GENIE, Multispectral Supervised Wildfire, algorithms, classification, fire, genetic imagery, programming, %P 236--245 %T Evolving forest fire burn severity classification algorithms for multi-spectral imagery %U http://public.lanl.gov/perkins/webdocs/brumby.aerosense01.pdf %V 4381 %X Between May 6 and May 18, 2000, the Cerro Grande/Los Alamos wildfire burned approximately 43,000 acres (17,500 ha) and 235 residences in the town of Los Alamos, NM. Initial estimates of forest damage included 17,000 acres (6,900 ha) of 70-100per cent tree mortality. Restoration efforts following the fire were complicated by the large scale of the fire, and by the presence of extensive natural and man-made hazards. These conditions forced a reliance on remote sensing techniques for mapping and classifying the burn region. During and after the fire, remote-sensing data was acquired from a variety of aircraft-based and satellite-based sensors, including Landsat 7. We now report on the application of a machine learning technique, implemented in a software package called GENIE, to the classification of forest fire burn severity using Landsat 7 ETM+ multispectral imagery. The details of this automatic classification are compared to the manually produced burn classification, which was derived from field observations and manual interpretation of high-resolution aerial colour/infrared photography.

@inproceedings{Brumby:2001:SPIE, abstract = {Between May 6 and May 18, 2000, the Cerro Grande/Los Alamos wildfire burned approximately 43,000 acres (17,500 ha) and 235 residences in the town of Los Alamos, NM. Initial estimates of forest damage included 17,000 acres (6,900 ha) of 70-100per cent tree mortality. Restoration efforts following the fire were complicated by the large scale of the fire, and by the presence of extensive natural and man-made hazards. These conditions forced a reliance on remote sensing techniques for mapping and classifying the burn region. During and after the fire, remote-sensing data was acquired from a variety of aircraft-based and satellite-based sensors, including Landsat 7. We now report on the application of a machine learning technique, implemented in a software package called GENIE, to the classification of forest fire burn severity using Landsat 7 ETM+ multispectral imagery. The details of this automatic classification are compared to the manually produced burn classification, which was derived from field observations and manual interpretation of high-resolution aerial colour/infrared photography.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Brumby, S. P. and Bloch, J. J. and Harvey, N. R. and Theiler, J. and Perkins, S. and Young, A. C. and Szymanski, J. J.}, biburl = {https://www.bibsonomy.org/bibtex/2380b3b390321596f001b8653671db205/brazovayeye}, booktitle = {In Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VII, Proceedings of SPIE}, editor = {Shen, Sylvia S. and Descour, Michael R.}, interhash = {886082943aef09240b65aa03fb705735}, intrahash = {380b3b390321596f001b8653671db205}, keywords = {Aladdin Forest GENIE, Multispectral Supervised Wildfire, algorithms, classification, fire, genetic imagery, programming,}, notes = {p3 Max size directed acyclic graph, not tree GP. GENIE object-oriented Perl. RSI's IDL language and image processing environment. C. UNIX Linux. Aladdin JAVA. Output is written to one of a number of scratch planes (memory) 'temporary workspaces where an image plane can be stored.' 'the gene [ADDP rD0 rS1 wS2] applies pixel-by-pixel addition to two input planes, read from data plane 0 and from scratch plane 1, and writes its output to scratch plane 2.' 'GENIE performs an analysis of chromosome graphs when they are created and only carries out those processing steps that actually affect the final result. Therefore, the fixed length of the chromosome acts as a maximum effective length.' hamming distance fitness pop=50. 30 gens. max chrome size 20. 3 scratch registers. 'The best evolved image-processing algorithm had the chromosome, [OPEN rD1 wS1 1 1][ADDS rD4 wS3 0.34][NEG rS1 wS1][MULTP rD4 rS3 wS2] [LINCOMB rS1 rD6 wS3 0.11][ADDP rS1 rS3 wS1][SUBP rS1 rD5 wS1]' 'The final values of S1, S2, and S3 are then combined in the linear sum, where the coefficients and intercept have been chosen by the Fisher discriminant, as described in Section 2.3, above, to produce our real-valued answer plane A (Figure 6): A = 0.0147*S1 - 0.0142*S2 + 0.0134*S3 + 1.554' 'Adjusting the threshold on A to fall at the between-peak minimum of the histogram at 0.7930 (a different optimisation criterion for the threshold than that used by default by GENIE) produces a new Boolean mask, Figure 9, in which almost all the false positives have been removed, and the remaining pixels marked as burn correspond very closely to the high severity burn regions in the BAER map'}, pages = {236--245}, size = {10 pages}, timestamp = {2008-06-19T17:37:05.000+0200}, title = {Evolving forest fire burn severity classification algorithms for multi-spectral imagery}, url = {http://public.lanl.gov/perkins/webdocs/brumby.aerosense01.pdf}, volume = 4381, year = 2001 }