A spectral gradient difference based approach for land cover change detection
A spectral gradient difference based approach for land cover change detection
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基于光谱梯度差的土地覆盖变化检测方法
DOI：
10.1016/j.isprsjprs.2013.07.009
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发表时间：
2013-11-01
影响因子：
12.7
通讯作者：
Chen, Lijun
中科院分区：
文献类型：
--
作者：
Chen, Jun;Lu, Miao;Chen, Lijun
遥感影像变化检测在土地覆盖制图、过程分析和动态信息服务中具有重要作用。大多数变化检测方法都采用欧几里得距离、光谱曲线之间的相关性等数学度量来计算变化幅度。然而，由于类间光谱差异，也会检测到许多伪变化，这仍然是业务遥感应用的一个重大挑战。一般来说，不同的土地覆盖类型有自己的光谱曲线，以典型的光谱数值和形状为特征。这些谱值被广泛用于设计变化检测算法。然而，光谱曲线的形状还没有被充分考虑。本文提出用光谱梯度差(SGD)来定量描述光谱的形状以及两个光谱之间的形状差异。在新的光谱梯度空间中计算的变化幅度被用来检测变化/无变化区域。然后，使用链模型来定性和定量地表示SGD模式。最后，通过与参考SGD图案库的模式匹配，确定土地覆盖变化类型。通过仿真实验和对Landsat数据的实例分析，验证了该方法的有效性。结果表明，基于SGD的方法优于传统方法。(C)2013国际摄影测量和遥感学会(ISPR)，由Elsevier B.V.出版。保留所有权利。
Change detection with remotely sensed imagery plays an important role in land cover mapping, process analysis and dynamic information services. Euclidean distance, correlation and other mathematic metrics between spectral curves have been used to calculate change magnitude in most change detection methods. However, many pseudo changes would also be detected because of inter-class spectral variance, which remains a significant challenge for operational remote sensing applications. In general, different land cover types have their own spectral curves characterized by typical spectral values and shapes. These spectral values are widely used for designing change detection algorithms. However, the shape of spectral curves has not yet been fully considered. This paper proposes to use spectral gradient difference (SGD) to quantitatively describe the spectral shapes and the differences in shape between two spectra. Change magnitude calculated in the new spectral gradient space is used to detect the change/no-change areas. Then, a chain model is employed to represent the SGD pattern both qualitatively and quantitatively. Finally, the land cover change types are determined by pattern matching with the knowledgebase of reference SGD patterns. The effectiveness of this SGD-based change detection approach was verified by a simulation experiment and a case study of Landsat data. The results indicated that the SGD-based approach was superior to the traditional methods. (C) 2013 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.