Objective Evaluation-based High-efficiency Learning Framework for Hyperspectral Image Classification

TL;DR

This paper introduces a novel, efficient hyperspectral image classification framework that combines a leakage-free sampling strategy with a modified fully convolutional network, improving speed and accuracy while reducing redundancy.

Contribution

It presents a new balanced sampling method and an optimized FCN architecture tailored for tiny HSI classification, enhancing evaluation objectivity and computational efficiency.

Findings

Outperforms state-of-the-art methods in speed and accuracy.

Provides objective evaluation via leakage-free sampling.

Demonstrates effectiveness on four datasets.

Abstract

Deep learning methods have been successfully applied to hyperspectral image (HSI) classification with remarkable performance. Because of limited labelled HSI data, earlier studies primarily adopted a patch-based classification framework, which divides images into overlapping patches for training and testing. However, this approach results in redundant computations and possible information leakage. In this study, we propose an objective evaluation-based high-efficiency learning framework for tiny HSI classification. This framework comprises two main parts: (i) a leakage-free balanced sampling strategy, and (ii) a modified end-to-end fully convolutional network (FCN) architecture that optimizes the trade-off between accuracy and efficiency. The leakage-free balanced sampling strategy generates balanced and non-overlapping training and testing data by partitioning an HSI and the ground truth image into small windows, each of which corresponds to one training or testing sample. The proposed high-efficiency FCN exhibits a pixel-to-pixel architecture with modifications aimed at faster inference speed and improved parameter efficiency. Experiments conducted on four representative datasets demonstrated that the proposed sampling strategy can provide objective performance evaluation and that the proposed network outperformed many state-of-the-art approaches with respect to the speed/accuracy tradeoff. Our source code is available at https://github.com/xmzhang2018.