Spectral-Spatial Feature Extraction and Classification by ANN Supervised with Center Loss in Hyperspectral Imagery

TL;DR

This paper introduces a spectral-spatial feature extraction and classification framework for hyperspectral imagery using ANN trained with combined softmax and center loss, effectively integrating spectral and spatial data for improved accuracy.

Contribution

It proposes a novel spectral-spatial classification method with joint supervision and adaptive voting, enhancing feature discrimination and spatial information fusion in hyperspectral data.

Findings

Outperforms state-of-the-art methods on three datasets.

Effectively integrates spectral and spatial features.

Demonstrates robustness with limited labeled samples.

Abstract

In this paper, we propose a spectral-spatial feature extraction and classification framework based on artificial neuron network (ANN) in the context of hyperspectral imagery. With limited labeled samples, only spectral information is exploited for training and spatial context is integrated posteriorly at the testing stage. Taking advantage of recent advances in face recognition, a joint supervision symbol that combines softmax loss and center loss is adopted to train the proposed network, by which intra-class features are gathered while inter-class variations are enlarged. Based on the learned architecture, the extracted spectrum-based features are classified by a center classifier. Moreover, to fuse the spectral and spatial information, an adaptive spectral-spatial center classifier is developed, where multiscale neighborhoods are considered simultaneously, and the final label is determined using an adaptive voting strategy. Finally, experimental results on three well-known datasets validate the effectiveness of the proposed methods compared with the state-of-the-art approaches.