A Statistical-Measure-Based Adaptive Land Cover Classification Algorithm by Efficient Utilization of Polarimetric SAR Observables
A Statistical-Measure-Based Adaptive Land Cover Classification Algorithm by Efficient Utilization of Polarimetric SAR Observables
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DOI：
10.1109/tgrs.2013.2267548
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发表时间：
2014-05
影响因子：
8.2
通讯作者：
P. Mishra;Dharmendra Singh
中科院分区：
文献类型：
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作者：
P. Mishra;Dharmendra Singh
极化合成孔径雷达（SAR）图像中包含的极化信息具有很大的潜力，自然和城市表面的特性。然而，它仍然是具有挑战性的，以确定不同的土地覆盖类与极化数据。前面介绍的大多数分类算法都使用固定值的极化指数，用于将特定的土地覆盖类型与其他类别隔离开来。然而，这些极化指数的值可能会随着观测地点、时间获取、环境条件以及不同系统之间的校准差异的变化而相应地改变。因此，每种土地覆盖类型的分离的极化指数的值必须调整，以科普这些变化。因此，在本文中，基于决策树的自适应土地覆盖分类技术已被提出标记不同的类到自己的类。所提出的方法使用基于空间统计的表达式（即，中值“M”和标准偏差“S”）的最佳选择的极化指数的基础上的可分性指数的标准，用于创建不同类别之间的决策边界。为了使系统在本质上具有自适应性，在表达式中包含了未知项。由于大量的未知数的整体分类精度（OA）的发展的非线性关系的依赖性，遗传算法（GA）的方法已被使用，它提供了最佳值的考虑极化指数自动分离不同的类。该算法在ALOS PALSAR四极SAR数据上进行了成功的测试和验证。
The polarimetric information contained in polarimetric synthetic aperture radar (SAR) images represents great potential for characterization of natural and urban surfaces. However, it is still challenging to identify different land cover classes with polarimetric data. Most of the classification algorithms presented earlier have used a fixed value of polarimetric indexes for segregation of a particular land cover type from other classes. However, the value of these polarimetric indexes may change accordingly with change in observation site, temporal acquisition, environmental conditions, and calibration differences among various systems. Thus, the value of polarimetric indexes for segregation of each land cover type has to be tuned in order to cope with these changes. Therefore, in this paper, a decision-tree-based adaptive land cover classification technique has been proposed for labeling of different clusters to their own classes. The proposed method uses spatial-statistics-based expressions (i.e., median “ M” and standard deviation “ S”) of best-selected polarimetric indexes on the basis of a separability index criterion for creating the decision boundary among various classes. In order to make the system adaptive in nature, unknown terms have been included in the expressions. Due to the dependence of a developed nonlinear relationship of overall classification accuracy (OA) on large number of unknowns, a genetic algorithm (GA) approach has been used, which provides optimum values of considered polarimetric indexes for automatic segregation of different classes. The proposed algorithm is successfully tested and validated on ALOS PALSAR quad-pol data.