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

By Muhammad Ahmad, Sidrah Shabbir, Swalpa Kumar Roy, Danfeng Hong, Xin Wu, Jing Yao, Adil Mehmood Khan, Manuel Mazzara, Salvatore Distefano, Jocelyn Chanussot. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021

366 citations recorded by the source corpus.

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 of the said topic. Primarily, we will encapsulate the main challenges of TML for HSIC and then we will acquaint the superiority of DL to address these problems. This article breaks down 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.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/ead1963f4963d6a75b189058b4490deacbfc7212

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Citations in the corpus

MambaHSI: Spatial–Spectral Mamba for Hyperspectral Image Classification (2025, background): “Due to the GCNs’ limitations in the graph construction, particularly for large graphs (need expensive computational cost), GCNs fail to classify or identify materials in large-scale hyperspectral scenes [41].”
Spectral–Spatial Morphological Attention Transformer for Hyperspectral Image Classification (2023, methodology): “H YPERSPECTRAL images (HSIs) contain information in contiguous wavelengths [1], [2], [3].”
Multimodal Fusion Transformer for Remote Sensing Image Classification (2022, methodology): “The compared methods include traditional classiﬁers i.e., KNN [66], RF [1], and SVM [27], as well as classical CNN models i.e., CNN1D [33], CNN2D [34], CNN3D [35] and RNN [67].”