Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects (2101.06116v3)

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.

• The paper offers a comprehensive review comparing traditional ML with advanced DL approaches for hyperspectral image classification.
• It demonstrates that deep models automate feature extraction, significantly enhancing accuracy in analyzing spectral and spatial data.
• The study identifies future directions including data augmentation, transfer learning, and efficient architectures for real-world applications.

An Expert Overview of Hyperspectral Image Classification: From Traditional to Deep Models

The paper "Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects" provides an extensive review and analysis of hyperspectral image classification (HSIC) methodologies, contrasting traditional approaches with modern deep learning (DL) frameworks. As hyperspectral imaging (HSI) continues to play a crucial role in remote sensing and various real-world applications, this survey elucidates the evolution and potential future directions of HSIC.

Traditional HSIC Approaches

Initially, the paper discusses traditional HSIC methods that rely on ML algorithms. These techniques often employ hand-crafted features to capture spectral and spatial information from hyperspectral data. Classic methods like Support Vector Machines (SVM), Random Forests, and K-Nearest Neighbors (KNN) have been foundational, leveraging texture descriptors, global and local feature extractors such as GIST, and various statistical measures to classify hyperspectral data.

Despite their foundational status, traditional methods have limitations, notably in handling high-dimensional data and capturing complex feature interdependencies. These methods typically require expert knowledge for feature engineering and struggle with the non-linear relationships inherent in HSI data.

The Shift to Deep Learning

The paper details the transformative shift towards DL for HSIC. Deep learning models, particularly Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Autoencoders (AE), and Deep Belief Networks (DBN), have demonstrated remarkable efficacy in handling the intricate spectral and spatial characteristics of HSI data. These methods automate feature extraction, providing significant improvements in classification accuracy.

The survey categorizes DL methods into three main types: spectral, spatial, and spectral-spatial frameworks. Spectral frameworks leverage 1D CNNs to process individual pixel spectra. Spatial methods, using 2D CNNs, focus on spatial patterns by treating hyperspectral images as collections of spatial contexts. Spectral-spatial approaches, often employing 3D CNNs and hybrid models, jointly exploit spectral and spatial data to enhance classification tasks.

Challenges and Future Directions

Despite their success, DL models face challenges related to high computational costs, the need for extensive labeled datasets, and the potential for overfitting given the high dimensionality of HSI data. The paper discusses approaches to mitigate these challenges, such as data augmentation, transfer learning, and semi-supervised learning. These techniques aim to enhance model generalization while reducing dependency on large labeled datasets.

Looking forward, the authors speculate on future research directions, emphasizing the need for robust models that can efficiently harness both spectral and spatial information. The integration of DL with advanced hardware such as GPUs and FPGAs is identified as crucial for managing computational demands. The exploration of lightweight and computationally efficient architectures is also highlighted as a necessary step to make such models practically feasible for large datasets and real-time applications.

Conclusion

In conclusion, the survey by Ahmad et al. provides a comprehensive examination of HSIC, charting the progression from traditional methods to state-of-the-art DL techniques. It highlights the promising future of deep learning in HSI, contingent on overcoming current limitations related to data requirements and computational complexity. This survey serves as a valuable resource for researchers in the field, encapsulating the key developments and future prospects in hyperspectral image classification.