Change Detection Combining Spatial-spectral Features and Sparse Representation Classifier

In this paper, we propose a spatial-spectral one-class sparse representation classifier (OCSRC) method to solve the multi-temporal change detection problem for identifying disaster-affected areas. The OCSRC method is adapted from the classical multi-class sparse representation classifier (SRC) from an earlier work. Based on the spectral based OCSRC, the spectral-spatial OCSRC is brought up by applying the spatial-spectral features to the one class sparse representation process instead of the original spectral bands. The spectral-spatial features discussed in this paper includes Gabor filter, adaptive weighted filter (AWF) and collaborative representation filter (CRF). These features are calculated from the original image with a convolution process to combine the information from the neighboring pixels. Performances of OCSRC with these three features and original spectral feature are tested and compared with multi-temporal multispectral HJ-1A images acquired in Heilongjiang province before and after the flood in 2013, with detailed discussion with two sub-images and massive application with the entire image. Receiver-operating-characteristics (ROC) curve, which is widely used to evaluate accuracy for two class problems such as target detection, is employed to evaluate the results. It shows that OCSRC combined with spatial and temporal characteristics outperform the cases with only spectral feature by a lower false positive rate (FPR) at defined true positive rate (TPR), namely less detection errors, and lead to better change detection result.