期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2000
卷号：XXXIII Part B1
页码：156-163
出版社：Copernicus Publications
摘要：This paper presents classification results using neural networks based on INSAR coherence imagery data for evaluation of the damage of Kobe earthquake in 1995. Coherence derived from multi-temporal SAR data before and after an earthquake presents a temporal decorrelation in disturbed regions. As L-band SAR data is more robust than C-band SAR data for spatial and temporal decorrelation, we used multi-source and temporal SAR coherence images derived from interferomet- ric pairs of JERS-1 and ERS-1 single look complex images (SLCs). Hazard areas can be estimated by classifying two categories defined as the damaged regions and otherwise using set of the coherence images. A neural classifier was used because of requiring no assumption for probability distribution function of each category. A competitive neural network trained by the learning vector quantization (LVQ) was adopted to the neural classifier in consideration of generalization ability and convergence. Total five coherence images were produced using effective interferometric pairs derived from two JERS-1 and four ERS-1 SLCs. The average coherence of JERS-1 is higher and has significantly higher contrast than that of ERS-1 even though the spatial decorrelation and the temporal separation are nearly equal. A hazard survey map was used for assessing extraction results. The LVQ method generated 23% higher kappa coefficient by adding the JERS-1 coherence and produced better results than the maximum likelihood method from the view point of balance of the number of the correctly classified pixels
