Adaptive Hyperspectral Image Classification Based on the Fusion of Manifolds Filter and Spatial Correlation Features

Abstract

In recent decades, the studies that obtain abundant spatial texture features, using a wide variety of filters for improving the performance of hyperspectral image (HSI) classification, have become a hotspot. However, the classification methods based on various filters are easy to fall into local feature extraction and neglect informative spatial correlation features. This paper presents an adaptive HSI classification method based on the fusion of adaptive manifold filter and spatial correlation feature (AMSCF). In which we use an adaptive manifold filter to extract spatial texture features, and use the domain transform normalized convolution filter and interpolated convolution filter to obtain the spatial correlation features. Besides, the spatial texture features and two correlation features are separately fused and classified by Large Margin Distribution Machine (LDM) to obtain the best classification. The experimental results demonstrate that the proposed AMSCF method is better than the other classification methods.