Multiple feature fusion using a multiset aggregated canonical correlation analysis for high spatial resolution satellite image scene classification

This paper presents a novel classification method for high-spatial-resolution satellite scene classification introducing multiset aggregated canonical correlation analysis (MACCA)-based feature fusion to fuse and combine multiple features. Firstly, a superpixel representation of the scene is constructed by employing a high-efficiency linear iterative clustering algorithm. After that, three diverse and complementary visual descriptors are extracted to characterize each superpixel. For taking full advantage of multiset features to yield the effective discriminant information and eliminating the redundancy between multiset features to some extent, MACCA is performed on three different feature sets to acquire fused feature for classification. Experimental analysis on high-spatial-resolution satellite scenes reveals that the suggested method achieves exceedingly promising performance and surpasses other off-the-shelf methods in classification accuracy.