Multiscale Spectral-Spatial Hyperspectral Image Classification with Adaptive Filtering

Abstract

Hyperspectral images (HSI) contain a wealth of spectral and spatial information, spectral-spatial combination is an effective way in improving the classification accuracy for HSI. To characterize the variability of spatial features at different scales better, a multiscale spectral-spatial classification method with adaptive filtering (MSAF) is proposed. The proposed method consists of the following four steps. Firstly, the spectral features are extracted by a feature selection algorithm. Secondly, the adaptive edge-preserving filtering with different scales are conducted on each feature, and then several stacks of data blocks containing spatial information can be obtained. Thirdly, the combinations of the spectral and spatial data blocks are classified using support vector machine (SVM). Finally, a post-processing is conducted to improve the classification results further. The experiments on the hyperspectral data demonstrate that the proposed method can improve the classification accuracy significantly compared to the SVM classifier, especially need less parameters than the spectral-spatial EPF method.