FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification

TL;DR

This paper introduces FPGA, a fast, patch-free global learning framework for hyperspectral image classification that leverages an encoder-decoder FCN with a novel sampling strategy, achieving superior speed and accuracy.

Contribution

The paper proposes a novel global stochastic stratified sampling strategy and a new end-to-end network, FreeNet, for hyperspectral image classification, addressing convergence issues and enhancing performance.

Findings

FPGA outperforms patch-based methods in speed and accuracy.

The global stochastic stratified sampling ensures FCN convergence.

FreeNet effectively exploits global spatial information.

Abstract

Deep learning techniques have provided significant improvements in hyperspectral image (HSI) classification. The current deep learning based HSI classifiers follow a patch-based learning framework by dividing the image into overlapping patches. As such, these methods are local learning methods, which have a high computational cost. In this paper, a fast patch-free global learning (FPGA) framework is proposed for HSI classification. In FPGA, an encoder-decoder based FCN is utilized to consider the global spatial information by processing the whole image, which results in fast inference. However, it is difficult to directly utilize the encoder-decoder based FCN for HSI classification as it always fails to converge due to the insufficiently diverse gradients caused by the limited training samples. To solve the divergence problem and maintain the abilities of FCN of fast inference and global spatial information mining, a global stochastic stratified sampling strategy is first proposed by transforming all the training samples into a stochastic sequence of stratified samples. This strategy can obtain diverse gradients to guarantee the convergence of the FCN in the FPGA framework. For a better design of FCN architecture, FreeNet, which is a fully end-to-end network for HSI classification, is proposed to maximize the exploitation of the global spatial information and boost the performance via a spectral attention based encoder and a lightweight decoder. A lateral connection module is also designed to connect the encoder and decoder, fusing the spatial details in the encoder and the semantic features in the decoder. The experimental results obtained using three public benchmark datasets suggest that the FPGA framework is superior to the patch-based framework in both speed and accuracy for HSI classification. Code has been made available at: https://github.com/Z-Zheng/FreeNet.