Comparative analysis of classification algorithms and multiple sensor data for land use/land cover classification in the Brazilian Amazon.
Comparative analysis of classification algorithms and multiple sensor data for land use/land cover classification in the Brazilian Amazon.
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DOI：
10.1117/1.jrs.6.061706
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发表时间：
2012-01-01
影响因子：
1.7
通讯作者：
Sant'Anna, S. J. S.
中科院分区：
文献类型：
--
作者：
Li, G. Y.;Lu, D. S.;Sant'Anna, S. J. S.
为了更好地了解适用于特定遥感数据的分类算法的选择，基于4种分类算法和4个遥感数据集，对巴西亚马逊地区的土地利用/土地覆盖(LULC)分类结果进行了比较分析。结果表明，对于陆地卫星专题图(TM)或TM与先进陆地观测卫星相控阵L波段合成孔径雷达(ALOS PALSAR)数据融合图像，最大似然分类器(MLC)具有较好的分类精度，但非参数算法，如对TM多光谱波段的分类树分析和对TM与PALSAR数据组合的K最近邻等算法提供了比MLC更好的分类效果。单独的PALSAR数据集不适合进行详细的LULC分类，分类精度(47.6%~59.4%)远低于Landsat TM图像(79.7%~84.9%)。然而，通过小波融合技术融合TM和PALSAR数据，提高了分类精度。研究表明，通过综合考虑分类精度、分类过程中所涉及的时间和人力等因素，为特定的数据集选择合适的分类算法具有重要意义。为湿润热带地区LULC分类遥感数据集的选择和相关分类算法的选择提供了重要信息。
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.