COLLAPSED BUILDING CLASSIFICATION WITH OPTICAL AND SAR DATA BASED ON MANIFOLD LEARNING
The collapse of buildings is a major factor in the casualties and economic losses of earthquake disasters, and the degree of building collapse is an important indicator for disaster assessment. In order to improve the classification of collapsed building coverings (CBC), a new fusion technique was proposed to integrate optical data and SAR data at the pixel level based on manifold learning.Three typical manifold learning models, namely, Isometric Mapping(ISOMAP), Local Linear Embedding (LLE) and principle component analysis (PCA), were used, and their results were compared. Feature extraction were employed from SPOT-5 data with RADARSAT-2 data. Experimental results showed that 1) the most useful features of the optical and SAR data were contained in manifolds with low-intrinsic dimensionality, while various CBC classes were distributed differently throughout the low- dimensionality spaces of manifolds derived from different manifold learning models; 2) in some cases, the performance of Isomap is similar to PCA, but PCA generally performed the best in this study, yielding the best accuracy of all CBC classes and requiring the least amount of time to extract features and establish learning; and 3) the LLE-derived manifolds yielded the lowest accuracy, mainly by confusing soil with collapsed building and rock. These results show that the manifold learning can improve the effectiveness of CBC classification by fusing the optical and SAR data features at the pixel level, which can be applied in practice to support the accurate analysis of earthquake damage.