SSViT-HCD: A Spatial–Spectral Convolutional Vision Transformer for Hyperspectral Change Detection

Shafique, Ayesha; Seydi, Seyd Teymoor; Alipour-Fard, T.; Cao, Guo; Yang, Di

doi:10.1109/jstars.2023.3251646

Cited by 12 publications

(6 citation statements)

References 45 publications

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“…Global tendency is contextual information, while local details are marked variances. The comparison between this method and other spectral matching algorithms and minimum 165,169,176,187,189,190,197,200,202,206,209] pursuing the discriminating features for HSI classification.…”

Section: Common Spectral Featuresmentioning

confidence: 99%

“…The SA module is also one of the core components of transformer architectures [200][201][202]. A series of SA modules were integrated to construct MHSA modules [197][198][199], which can assist transformers to describe different modes of spatial attention in separate feature subspaces.…”

Section: Similarity-based Spatial Attentionmentioning

confidence: 99%

“…However, the above-mentioned spatial attention modules generally deduce a few modes of attention. To express possible spatial dependency sufficiently, transformers [194,195], which originate from the field of natural language processing and have been the core component of the ChatGPT model [196], adopt multi-head SA (MHSA) modules [181,[197][198][199] to integrate various types of attention from different subspaces into a linear representation [200][201][202]. Transformer is also good at handling long-distance spectral dependency.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Discriminating Spectral–Spatial Feature Extraction for Hyperspectral Image Classification: A Review

Li,

Wang,

Cheikh

2024

Sensors

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Hyperspectral images (HSIs) contain subtle spectral details and rich spatial contextures of land cover that benefit from developments in spectral imaging and space technology. The classification of HSIs, which aims to allocate an optimal label for each pixel, has broad prospects in the field of remote sensing. However, due to the redundancy between bands and complex spatial structures, the effectiveness of the shallow spectral–spatial features extracted by traditional machine-learning-based methods tends to be unsatisfying. Over recent decades, various methods based on deep learning in the field of computer vision have been proposed to allow for the discrimination of spectral–spatial representations for classification. In this article, the crucial factors to discriminate spectral–spatial features are systematically summarized from the perspectives of feature extraction and feature optimization. For feature extraction, techniques to ensure the discrimination of spectral features, spatial features, and spectral–spatial features are illustrated based on the characteristics of hyperspectral data and the architecture of models. For feature optimization, techniques to adjust the feature distances between classes in the classification space are introduced in detail. Finally, the characteristics and limitations of these techniques and future challenges in facilitating the discrimination of features for HSI classification are also discussed further.

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Section: Common Spectral Featuresmentioning

confidence: 99%

Section: Similarity-based Spatial Attentionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Discriminating Spectral–Spatial Feature Extraction for Hyperspectral Image Classification: A Review

Li,

Wang,

Cheikh

2024

Sensors

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“…RS data track changes over time between objects within specific regions [7], providing a valuable data source with several advantages, including frequent updates, the ability to monitor vast areas, and cost-effectiveness [8]. RS data are utilized in a wide range of CD applications, such as fire monitoring [9,10], climate change studies [11][12][13][14][15], and flood mapping [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

A Hyperspectral Change Detection (HCD-Net) Framework Based on Double Stream Convolutional Neural Networks and an Attention Module

Seydi,

Boueshagh,

Namjoo

et al. 2024

Remote Sensing

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Human activities and natural phenomena continually transform the Earth’s surface, presenting ongoing challenges to the environment. Therefore, the accurate and timely monitoring and prediction of these alterations are essential for devising effective solutions and mitigating environmental impacts in advance. This study introduces a novel framework, called HCD-Net, for detecting changes using bi-temporal hyperspectral images. HCD-Net is built upon a dual-stream deep feature extraction process, complemented by an attention mechanism. The first stream employs 3D convolution layers and 3D Squeeze-and-Excitation (SE) blocks to extract deep features, while the second stream utilizes 2D convolution and 2D SE blocks for the same purpose. The deep features from both streams are then concatenated and processed through dense layers for decision-making. The performance of HCD-Net is evaluated against existing state-of-the-art change detection methods. For this purpose, the bi-temporal Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral dataset was utilized to assess the change detection performance. The findings indicate that HCD-Net achieves superior accuracy and the lowest false alarm rate among the compared methods, with an overall classification accuracy exceeding 96%, and a kappa coefficient greater than 0.9.

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“…To address this challenge and achieve better representation capabilities of deep features, designing deeper and more complex feature extraction networks has gotten significant attention as a primary research focus. Many researchers have put forward several enhanced models to achieve more discriminative feature representations, such as combining Generative Adversarial Networks (GAN) [38][39][40] or Recurrent Neural Networks (RNN) [41,42], or using feature extraction models based on the Transformer architecture [43][44][45] to expand the receptive field. Some studies focus on the effective utilization of features, such as using spatial or channel attention mechanisms [30][31][32]36,46] or employing multi-scale feature fusion for feature enhancement [25,29,[47][48][49].…”

Section: Introductionmentioning

confidence: 99%

MFSFNet: Multi-Scale Feature Subtraction Fusion Network for Remote Sensing Image Change Detection

Huang

You

2023

Remote Sensing

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Change detection plays a crucial role in remote sensing by identifying surface modifications between two sets of temporal remote sensing images. Recent advancements in deep learning techniques have yielded significant achievements in this field. However, there are still some challenges: (1) Existing change feature fusion methods often introduce redundant information. (2) The complexity of network structures leads to a large number of parameters and difficulties in model training. To overcome these challenges, this paper proposes a Multi-Scale Feature Subtraction Fusion Network (MFSF-Net). It comprises two primary modules: the Multi-scale Feature Subtraction Fusion (MFSF) module and the Feature Deep Supervision (FDS) module. MFSF enhances change features and reduces redundant pseudo-change features. FDS provides additional supervision on different scales of change features in the decoder, improving the training efficiency performance of the network. Additionally, to address the problem of imbalanced samples, the Dice loss strategy is introduced as a means to mitigate this issue. Through comprehensive experiments, MFSF-Net achieves an F1 score of 91.15% and 95.64% on LEVIR-CD and CDD benchmark datasets, respectively, outperforming six state-of-the-art algorithms. Moreover, it attains an improved balance between model complexity and performance, showcasing the efficacy of the proposed approach.

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SSViT-HCD: A Spatial–Spectral Convolutional Vision Transformer for Hyperspectral Change Detection

Cited by 12 publications

References 45 publications

Discriminating Spectral–Spatial Feature Extraction for Hyperspectral Image Classification: A Review

Discriminating Spectral–Spatial Feature Extraction for Hyperspectral Image Classification: A Review

A Hyperspectral Change Detection (HCD-Net) Framework Based on Double Stream Convolutional Neural Networks and an Attention Module

MFSFNet: Multi-Scale Feature Subtraction Fusion Network for Remote Sensing Image Change Detection

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