Deep Learning for Hyperspectral Image Classification: An Overview

By Shutao Li, Weiwei Song, Leyuan Fang, Yushi Chen, Pedram Ghamisi, Jon Atli Benediktsson. IEEE Transactions on Geoscience and Remote Sensing, 2019

1,595 citations recorded by the source corpus.

Hyperspectral image (HSI) classification has become a hot topic in the field of remote sensing. In general, the complex characteristics of hyperspectral data make the accurate classification of such data challenging for traditional machine learning methods. In addition, hyperspectral imaging often deals with an inherently nonlinear relation between the captured spectral information and the corresponding materials. In recent years, deep learning has been recognized as a powerful feature-extraction tool to effectively address nonlinear problems and widely used in a number of image processing tasks. Motivated by those successful applications, deep learning has also been introduced to classify HSIs and demonstrated good performance. This survey paper presents a systematic review of deep learning-based HSI classification literatures and compares several strategies for this topic. Specifically, we first summarize the main challenges of HSI classification which cannot be effectively overcome by traditional machine learning methods, and also introduce the advantages of deep learning to handle these problems. Then, we build a framework that divides the corresponding works into spectral-feature networks, spatial-feature networks, and spectral–spatial-feature networks to systematically review the recent achievements in deep learning-based HSI classification. In addition, considering the fact that available training samples in the remote sensing field are usually very limited and training deep networks require a large number of samples, we include some strategies to improve classification performance, which can provide some guidelines for future studies on this topic. Finally, several representative deep learning-based classification methods are conducted on real HSIs in our experiments.

Subfields: Artificial Intelligence, Computer Networks and Communications, Media Technology

View Paper: https://www.semanticscholar.org/paper/4f71ab367eb37cfd145d41327f7bb14077e5e7c5

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Citations in the corpus

Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects (2021, methodology): “For instance, [194] introduced a DBN framework with the logistics regression layer and verified that the joint exploitation of spectral-spatial features leads to improved classification accuracy.”
Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities (2020, background): “In addition, there have been several hyperspectral image classification surveys [27]–[29].”
Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis (2020, background): “These layers are exploited to extract more high-level information by reshaping feature maps into an n-dimension vector [42].”
Multiscale Dynamic Graph Convolutional Network for Hyperspectral Image Classification (2019, methodology): “To address this defect, deep learning [15]–[19] has been extensively employed for hyperspectral image classifi-”
A survey: Deep learning for hyperspectral image classification with few labeled samples (2021, background): “Further, it should be noted that different from the previous review papers [12, 29], this paper mainly focuses on the few labeled sample issue, which is considered as the most challenging problem in the HSI classification scenario.”
Feature Extraction for Hyperspectral Imagery: The Evolution From Shallow to Deep: Overview and Toolbox (2020, background): “Due to their powerful feature representation ability, deep learning techniques have been widely used to extract features from hyperspectral images in recent years [88], [89].”
Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review (2020, background): “Studies, such as [57] which compared the capability of deep learning architectures with support vector machine (SVM) for RS image classiﬁcation, and [58] which focused on the clas-siﬁcation of hyperspectral data using deep learning techniques, are other examples of RS deep learning review papers.”
A Review on Deep Learning in UAV Remote Sensing (2021, methodology): “Similar to [40], Li et al. [115] conducted a literature revision while presenting an experimental analysis with DNNs’ methods.”
Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review (2020, background): “Reference [29] also presents an overview of deep learning methods for hyperspectral image classification and compare the effectiveness of these methods on common well-known datasets.”
Attention-Based Adaptive Spectral–Spatial Kernel ResNet for Hyperspectral Image Classification (2020, neutral): “In the last decade, deep learning-based methods have proved very effective [14]–[16].”
SpectralFormer: Rethinking Hyperspectral Image Classification With Transformers (2021, background): “In recent years, many well-recognized backbone networks have been widely and successfully applied in the HS image classification task [23], such as autoencoders (AEs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), capsule networks (Ca”
RSVQA: Visual Question Answering for Remote Sensing Data (2020, background): “, classification [2], [3]), the ability to use remote sensing data as a direct source of information is currently limited to experts within the remote sensing and computer vision communities.”