Special Section on Advances in Remote Sensing for Monitoring Global Environmental Changes

Comparative analysis of classification algorithms and multiple sensor data for land use/land cover classification in the Brazilian Amazon

[+] Author Affiliations
Guiying Li

Indiana University, Anthropological Center for Training and Research on Global Environmental Change, Bloomington, Indiana 47405

Dengsheng Lu

Michigan State University, Center for Global Change and Earth Observations, 1405 S. Harrison Road, East Lansing, Michigan 48823

Emilio Moran

Indiana University, Anthropological Center for Training and Research on Global Environmental Change, Bloomington, Indiana 47405

Sidnei João Siqueira Sant’Anna

National Institute for Space Research, Av. dos Astronautas, 1758, 12245-010 São Jose dos Campos, SP, Brazil

J. Appl. Remote Sens. 6(1), 061706 (Dec 14, 2012). doi:10.1117/1.JRS.6.061706
History: Received July 18, 2012; Revised November 24, 2012; Accepted November 27, 2012
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Abstract.  A comparative analysis of land use/land cover (LULC) classification results in the Brazilian Amazon based on four classification algorithms and four remote sensing datasets was conducted in order to better understand the selection of a classification algorithm suitable for a specific remote sensing data. It is shown that maximum likelihood classifier (MLC) provided reasonably good classification accuracy when Landsat Thematic Mapper (TM) or the TM and Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) data-fusion images were used, but nonparametric algorithms such as classification tree analysis for TM multispectral bands and K-nearest neighbor for the combination of TM and PALSAR data provided better classification than MLC. Individual PALSAR dataset is not suitable for detailed LULC classification and has much poorer classification accuracy (47.6% to 59.4%) than Landsat TM image (79.7% to 84.9%). However, integration of TM and PALSAR data through the wavelet-merging technique improved classification accuracy. It is implied that the importance of selecting a suitable classification algorithm for a specific dataset by considering such factors as overall classification accuracy and time and labor involved in a classification procedure. Important information for guiding the selection of remote sensing dataset and associated classification algorithms for LULC classification in the moist tropical regions is also provided.

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© 2012 Society of Photo-Optical Instrumentation Engineers

Citation

Guiying Li ; Dengsheng Lu ; Emilio Moran and Sidnei João Siqueira Sant’Anna
"Comparative analysis of classification algorithms and multiple sensor data for land use/land cover classification in the Brazilian Amazon", J. Appl. Remote Sens. 6(1), 061706 (Dec 14, 2012). ; http://dx.doi.org/10.1117/1.JRS.6.061706


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