A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers

• URL: http://arxiv.org/abs/2404.14955v3
• Date: Wed, 12 Jun 2024 06:21:59 GMT
• Title: A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers
• Authors: Muhammad Ahmad, Salvatore Distifano, Adil Mehmood Khan, Manuel Mazzara, Chenyu Li, Jing Yao, Hao Li, Jagannath Aryal, Gemine Vivone, Danfeng Hong,
• Abstract summary: Hyperspectral Image Classification (HSC) is a challenging task due to the high dimensionality and complex nature of HS data. Traditional Machine Learning approaches while effective, face challenges in real-world data due to varying optimal feature sets, subjectivity in human-driven design, biases, and limitations. In recent years, DL techniques have emerged as powerful tools for addressing these challenges.
• Score: 25.46596944661226
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hyperspectral Image Classification (HSC) is a challenging task due to the high dimensionality and complex nature of Hyperspectral (HS) data. Traditional Machine Learning approaches while effective, face challenges in real-world data due to varying optimal feature sets, subjectivity in human-driven design, biases, and limitations. Traditional approaches encounter the curse of dimensionality, struggle with feature selection and extraction, lack spatial information consideration, exhibit limited robustness to noise, face scalability issues, and may not adapt well to complex data distributions. In recent years, DL techniques have emerged as powerful tools for addressing these challenges. This survey provides a comprehensive overview of the current trends and future prospects in HSC, focusing on the advancements from DL models to the emerging use of Transformers. We review the key concepts, methodologies, and state-of-the-art approaches in DL for HSC. We explore the potential of Transformer-based models in HSC, outlining their benefits and challenges. We also delve into emerging trends in HSC, as well as thorough discussions on Explainable AI and Interoperability concepts along with Diffusion Models (image denoising, feature extraction, and image fusion). Additionally, we address several open challenges and research questions pertinent to HSC. Comprehensive experimental results have been undertaken using three HS datasets to verify the efficacy of various conventional DL models and Transformers. Finally, we outline future research directions and potential applications that can further enhance the accuracy and efficiency of HSC. The Source code is available at \url{https://github.com/mahmad00/Conventional-to-Transformer-for-Hyperspectral-Image-Classification-Surve y-2024}.

Related papers

• A Simple Background Augmentation Method for Object Detection with Diffusion Model [53.32935683257045]
  In computer vision, it is well-known that a lack of data diversity will impair model performance. We propose a simple yet effective data augmentation approach by leveraging advancements in generative models. Background augmentation, in particular, significantly improves the models' robustness and generalization capabilities.
  arXiv  Detail & Related papers  (2024-08-01T07:40:00Z)
• From CNNs to Transformers in Multimodal Human Action Recognition: A Survey [23.674123304219822]
  Human action recognition is one of the most widely studied research problems in Computer Vision. Recent studies have shown that addressing it using multimodal data leads to superior performance. Recent rise of Transformers in visual modelling is now also causing a paradigm shift for the action recognition task.
  arXiv  Detail & Related papers  (2024-05-22T02:11:18Z)
• GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross Appearance-Edge Learning [50.7702397913573]
  The rapid advancement of photorealistic generators has reached a critical juncture where the discrepancy between authentic and manipulated images is increasingly indistinguishable. Although there have been a number of publicly available face forgery datasets, the forgery faces are mostly generated using GAN-based synthesis technology. We propose a large-scale, diverse, and fine-grained high-fidelity dataset, namely GenFace, to facilitate the advancement of deepfake detection.
  arXiv  Detail & Related papers  (2024-02-03T03:13:50Z)
• Comprehensive Exploration of Synthetic Data Generation: A Survey [4.485401662312072]
  This work surveys 417 Synthetic Data Generation models over the last decade. The findings reveal increased model performance and complexity, with neural network-based approaches prevailing. Computer vision dominates, with GANs as primary generative models, while diffusion models, transformers, and RNNs compete.
  arXiv  Detail & Related papers  (2024-01-04T20:23:51Z)
• FactoFormer: Factorized Hyperspectral Transformers with Self-Supervised Pretraining [36.44039681893334]
  Hyperspectral images (HSIs) contain rich spectral and spatial information. Current state-of-the-art hyperspectral transformers only tokenize the input HSI sample along the spectral dimension. We propose a novel factorized spectral-spatial transformer that incorporates factorized self-supervised pretraining procedures.
  arXiv  Detail & Related papers  (2023-09-18T02:05:52Z)
• Towards General Visual-Linguistic Face Forgery Detection [95.73987327101143]
  Deepfakes are realistic face manipulations that can pose serious threats to security, privacy, and trust. Existing methods mostly treat this task as binary classification, which uses digital labels or mask signals to train the detection model. We propose a novel paradigm named Visual-Linguistic Face Forgery Detection(VLFFD), which uses fine-grained sentence-level prompts as the annotation.
  arXiv  Detail & Related papers  (2023-07-31T10:22:33Z)
• A Transformer Framework for Data Fusion and Multi-Task Learning in Smart Cities [99.56635097352628]
  This paper proposes a Transformer-based AI system for emerging smart cities. It supports virtually any input data and output task types present S&CCs. It is demonstrated through learning diverse task sets representative of S&CC environments.
  arXiv  Detail & Related papers  (2022-11-18T20:43:09Z)
• Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A Comprehensive Review [85.36368666877412]
  hyperspectral (HS) remote sensing (RS) imaging has provided a significant amount of spatial and spectral information for the observation and analysis of the Earth's surface. The recent advancement and even revolution of the HS RS technique offer opportunities to realize the full potential of various applications. Due to the maintenance of the 3-D HS inherent structure, tensor decomposition has aroused widespread concern and research in HS data processing tasks.
  arXiv  Detail & Related papers  (2022-05-13T00:39:23Z)
• Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing [57.52865154829273]
  Hyperspectral imaging, also known as image spectrometry, is a landmark technique in geoscience remote sensing (RS) In the past decade efforts have been made to process analyze these hyperspectral (HS) products mainly by means of seasoned experts. For this reason, it is urgent to develop more intelligent and automatic approaches for various HS RS applications.
  arXiv  Detail & Related papers  (2021-03-02T03:32:10Z)
• Hyperspectral Image Classification -- Traditional to Deep Models: A Survey for Future Prospects [0.6091702876917281]
  Hyperspectral Imaging (HSI) has been extensively utilized in many real-life applications. In the last few years, deep learning (DL) has been substantiated as a powerful feature extractor. This survey enlists a systematic overview of DL for HSIC and compared state-of-the-art strategies of the said topic.
  arXiv  Detail & Related papers  (2021-01-15T13:59:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.