Deep Learning for Classification of Hyperspectral Data: A Comparative Review

Abstract

In recent years, deep learning techniques revolutionized the way remote sensing data are processed. Classification of hyperspectral data is no exception to the rule, but has intrinsic specificities which make application of deep learning less straightforward than with other optical data. This article presents a state of the art of previous machine learning approaches, reviews the various deep learning approaches currently proposed for hyperspectral classification, and identifies the problems and difficulties which arise to implement deep neural networks for this task. In particular, the issues of spatial and spectral resolution, data volume, and transfer of models from multimedia images to hyperspectral data are addressed. Additionally, a comparative study of various families of network architectures is provided and a software toolbox is publicly released to allow experimenting with these methods. 1 This article is intended for both data scientists with interest in hyperspectral data and remote sensing experts eager to apply deep learning techniques to their own dataset.

References in corpus (4)

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Deep learning in remote sensing: a review
• Dense semantic labeling of sub-decimeter resolution images with convolutional neural networks
• Multiclass feature learning for hyperspectral image classification: sparse and hierarchical solutions

Cited by in corpus (10)

• Towards a Collective Agenda on AI for Earth Science Data Analysis
• Optical Remote Sensing Image Understanding with Weak Supervision: Concepts, Methods, and Perspectives
• Graph Spatio-Spectral Total Variation Model for Hyperspectral Image Denoising
• Unsupervised ore/waste classification on open-cut mine faces using close-range hyperspectral data
• A Multibranch Convolutional Neural Network for Hyperspectral Unmixing
• Graph Neural Networks Extract High-Resolution Cultivated Land Maps from Sentinel-2 Image Series
• Decision Tree Learning with Spatial Modal Logics
• Spatial--spectral FFPNet: Attention-Based Pyramid Network for Segmentation and Classification of Remote Sensing Images
• Multi-Level Graph Convolutional Network with Automatic Graph Learning for Hyperspectral Image Classification
• A Novel Spatial-Spectral Framework for the Classification of Hyperspectral Satellite Imagery