A SURVEY ON HYPERSPECTRAL IMAGE CLASSIFICATION USING ADAPTIVE SPATIAL-SPECTRAL FEATURE LEARNING

Abstract

The combination of spectral and spatial information provides an effective way to improve the hyperspectral image (HSI) classification.Hyperspectral image processing has been a very dynamic area in remote sensing and other applications in recent years.Traditional methods, have shown promising results in hyperspectral image classification.Such methodologies, nevertheless, can lead to information loss in representing hyperspectral pixels, which intrinsically have a sequence-based data structure.In the implementation of the proposed method, a convolution neural network (CNN) is first applied to learn weight features for each pixel within a hyperspectral patch and adaptive weights can be obtained based on a softmax normalization.Then, the shallow joint adaptive features can be acquired according to these weights.After that, a stacked auto-encoder (SAE) is proposed to further extract deeper hierarchical features for the final classification.