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<p>In the twenty-first century, urbanization has become one of the most transformative processes, driving significant changes in land use, infrastructure, and environmental conditions. These dynamics underscore the need for accurate urban planning, environmental monitoring, and disaster management. Traditional approaches utilize optical and multispectral remote sensing imagery and often fail to distinguish spectrally similar urban materials. In contrast, hyperspectral imaging improves classification accuracy by providing far more detailed spectral and spatial information. This survey presents a comprehensive and systematic review of hyperspectral image change detection, with a particular focus on the emerging role of vision transformers (ViTs) and transformer-driven architectures. This study analyzes the evolution from traditional pixel-based and machine learning methods to deep learning paradigms, highlighting the limitations of convolutional neural networks (CNNs) in capturing long-range dependencies. The paper provides an in-depth discussion of HSI principles, urban data acquisition constraints, transformer architectures, spatial spectral attention mechanisms, hybrid CNN + ViT models, domain adaptation strategies, and interpretability techniques. Furthermore, we analyze recent advances in spatial-only, spectral-only, and hybrid attention mechanisms, CNN + ViT fusion models, and spatial-spectral tokenization strategies tailored for hyperspectral data. We further summarize benchmark hyperspectral datasets, multimodal data fusion with LiDAR, SAR, and multispectral imagery, synthetic dataset generation using generative adversarial networks, interpretability and explainability techniques, and the computational challenges of cloud and edge deployment. The reviewed deep learning models are organized into six methodological families, i.e., CNN-based, transformer-based, graph-based, domain-adaptive and label-efficient, fusion-based, and lightweight designs, and a unified experimental evaluation compares representative models under a common protocol. By synthesizing experimental trends and comparative performance across extensive benchmark studies, this survey highlights current limitations, open challenges, and future research directions.</p>

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HyperTransUrban: a vision transformer-driven survey of change detection using hyperspectral imaging

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月31日

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  • Priya Mittal,
  • Bhisham Sharma,
  • Dhirendra Prasad Yadav,
  • Panos Liatsis

摘要

In the twenty-first century, urbanization has become one of the most transformative processes, driving significant changes in land use, infrastructure, and environmental conditions. These dynamics underscore the need for accurate urban planning, environmental monitoring, and disaster management. Traditional approaches utilize optical and multispectral remote sensing imagery and often fail to distinguish spectrally similar urban materials. In contrast, hyperspectral imaging improves classification accuracy by providing far more detailed spectral and spatial information. This survey presents a comprehensive and systematic review of hyperspectral image change detection, with a particular focus on the emerging role of vision transformers (ViTs) and transformer-driven architectures. This study analyzes the evolution from traditional pixel-based and machine learning methods to deep learning paradigms, highlighting the limitations of convolutional neural networks (CNNs) in capturing long-range dependencies. The paper provides an in-depth discussion of HSI principles, urban data acquisition constraints, transformer architectures, spatial spectral attention mechanisms, hybrid CNN + ViT models, domain adaptation strategies, and interpretability techniques. Furthermore, we analyze recent advances in spatial-only, spectral-only, and hybrid attention mechanisms, CNN + ViT fusion models, and spatial-spectral tokenization strategies tailored for hyperspectral data. We further summarize benchmark hyperspectral datasets, multimodal data fusion with LiDAR, SAR, and multispectral imagery, synthetic dataset generation using generative adversarial networks, interpretability and explainability techniques, and the computational challenges of cloud and edge deployment. The reviewed deep learning models are organized into six methodological families, i.e., CNN-based, transformer-based, graph-based, domain-adaptive and label-efficient, fusion-based, and lightweight designs, and a unified experimental evaluation compares representative models under a common protocol. By synthesizing experimental trends and comparative performance across extensive benchmark studies, this survey highlights current limitations, open challenges, and future research directions.