Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects

TL;DR

This survey reviews the evolution from traditional to deep learning methods for hyperspectral image classification, highlighting challenges, advancements, and future research directions in leveraging deep models for improved accuracy.

Contribution

It systematically analyzes state-of-the-art deep learning frameworks for hyperspectral classification, categorizing them into spectral, spatial, and combined features, and discusses strategies to enhance generalization with limited labeled data.

Findings

Deep learning outperforms traditional methods in HSIC.

Spectral-spatial deep models achieve higher accuracy.

Strategies for limited labeled data improve model robustness.

Abstract

Hyperspectral Imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. Notably, the complex characteristics i.e., the nonlinear relation among the captured spectral information and the corresponding object of HSI data make accurate classification challenging for traditional methods. In the last few years, Deep Learning (DL) has been substantiated as a powerful feature extractor that effectively addresses the nonlinear problems that appeared in a number of computer vision tasks. This prompts the deployment of DL for HSI classification (HSIC) which revealed good performance. This survey enlists a systematic overview of DL for HSIC and compared state-of-the-art strategies on the said topic. Primarily, we will encapsulate the main challenges of traditional machine learning for HSIC and then we will acquaint the superiority of DL to address these problems. This survey breakdown the state-of-the-art DL frameworks into spectral features, spatial features, and together spatial-spectral features to systematically analyze the achievements (future research directions as well) of these frameworks for HSIC. Moreover, we will consider the fact that DL requires a large number of labeled training examples whereas acquiring such a number for HSIC is challenging in terms of time and cost. Therefore, this survey discusses some strategies to improve the generalization performance of DL strategies which can provide some future guidelines.