SSCNN-S: A Spectral-Spatial Convolution Neural Network with Siamese Architecture for Change Detection

Zhan, Tianming; Song, Bo; Yang, Xu; Wan, Minghua; Wang, Xin; Yang, Guowei; Wu, Zebin

doi:10.3390/rs13050895

Cited by 38 publications

(16 citation statements)

References 51 publications

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“…The Farmland dataset were used in [47], which had an accuracy of 92.32%, and [34], which achieved 93.74%. Furthermore, an OA of 96% was obtained with the Hermiston dataset [48], and [46] achieved OA of 94.46% respectively. Our proposed method, SSViT, obtained the highest accuracies for similar datasets such as River, Farmland, and Hermiston, as shown in Table IV.…”

Section: Discussionmentioning

confidence: 94%

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

Shafique

Seydi

Alipour-Fard

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In recent decades, the wide use of deep learningbased methods has consistently improved the performance of remote sensing images and is widely used for hyperspectral change detection (HCD) tasks. However, most of the existing HCD method is based on the convolutional neural network (CNN), which shows limitations in long-range dependencies and also cannot mine sequence features well. The CD performance still has margins for improvement. In this study, inspired by the excellent performance of transformers in computer vision and which has shown a significant ability to model global dependencies to attenuate the loss of long-range information, we built a hybrid spatial-spectral convolutional vision transformer (SSViT) for HCD. Our proposed method combines the merits of CNN and transformer to fulfill effective and efficient HCD. This study focused on highly reliable pseudo-sample data generation by selection scenario. To generate a pseudo sample, we have used different methods: (1) we predict change and no-change areas by using Euclidean distance, (2) thresholding by Chan-Vese segmentation method for determining change and no-change pixels for intensity maps, (3) sorting of change and no-change pixels, and (4) selection of the minimum value of initial no-change pixels as pseudo change sample data, in addition to, choosing the maximum intensity value for change candidate pixels as change sample data. The highly reliable change pixels were selected, and then pseudo-training data was used to train the SSViT model. At last, the change map is generated by training the SSViT network based on pseudo-training data. The performance of the SSViT model is evaluated for real-world hyperspectral (HS) datasets with different change landcover types. Furthermore, a new series of HS images is introduced for CD purposes. The results of CD show that the HS images have a high potential for detecting subtle changes. The experimental results demonstrate that the proposed SSViT could outperform the advanced HCD methods.

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Section: Discussionmentioning

confidence: 94%

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

Shafique

Seydi

Alipour-Fard

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…To solve the challenge of CD caused by artificial objects such as clouds and shadows, Negri et al [47] proposed a novel spectral-spatial-aware unsupervised change detection framework. Zhan et al [48] proposed a spectral-spatial convolution neural network with Siamese architecture (SSCNN-S) for HSI CD, which extracts the spectral-spatial vector from dual-temporal images, and then uses a Siamese network based on contrast loss to train and optimize the network. To find a feature space that can best express spectral-spatial features, Song et al [49] proposed a bidirectional reconstruction coding network and enhanced residual network (BRCN-ERN) for HSI CD.…”

Section: Spectral-spatial Information Methodsmentioning

confidence: 99%

CBW-MSSANet: A CNN Framework With Compact Band Weighting and Multiscale Spatial Attention for Hyperspectral Image Change Detection

Liu

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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Change detection (CD), aims to detect the changing area of the same scene at different times, which is an important application of remote sensing images. As the key data source of CD, hyperspectral image (HSI) is widely used in CD technology because of its rich spectral-spatial information. However, how to mine the multi-level spatial information of dual-temporal hyperspectral images (HSIs) and focus on the features of the pixels to be classified individually remains a problem in the spatial attention mechanism (SAM). To make full use of the spectral-spatial information of HSIs, in this paper we propose a CNN framework with compact band weighting and multiscale spatial attention (CBW-MSSANet) for HSI pixel-level CD. The main contributions of this article are as follows: 1) a new method of pseudo-label training sample selection based on kmeans (KM) centroid distance is designed; 2) apply the compact band weighting (CBW) module to HSI CD to take full advantage of the spectral information of HSIs; 3) a multi-scale spatial attention (MSSA) module is developed for pixel-level CD, which can mine multi-level spatial information and pay more attention to the features of the pixels to be classified, and combine the spatial information of adjacent pixels to make it more conducive to pixel-level CD. Experimental results on four real HSI datasets demonstrated that the performance of MSSA surpasses the classical single-scale SAM, and CBW-MSSANet is superior to some representative CD methods.

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“…Deeply supervised image fusion network (IFN) [29] utilises the attention module to fuse the multi-level deep features of the original image with the image difference features, improve the boundary integrity and internal compactness of the objects in the output change map, and reconstruct the change map. Spectral-spatial convolution neural network with siamese architecture (SSCNN-S) [30] obtains two spectral space vectors by extracting tensor pairs in dual-temporal (HSIs) and merging them into a spectral space network, and then calculating the similarity between the vectors to obtain a CD map. End-to-end siamese CNN (SiamNet) with a spectral-spatial-wise attention mechanism (SSA-SiamNet) [31] has a spectral-spatial attention mechanism, emphasizing information-rich spectral channels and locations, which can effectively improve the performance of CD.…”

Section: Introductionmentioning

confidence: 99%

A Hyperspectral Image Change Detection Framework With Self-Supervised Contrastive Learning Pretrained Model

Liu

Tan

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Hyperspectral images (HSIs) have high spatial resolution and spectral resolution, and using HSI as a change detection (CD) data source is crucial for detecting surface changes. However, there is a large amount of real noise in HSIs, and most deep learning-based CD methods require a large number of ground-truth labels for training, which is difficult and expensive to label manually. To reduce the dependence of CD on ground-truth labels and weaken the interference of noise on CD in HSIs, in this paper we propose a hyperspectral image change detection framework with self-supervised contrastive learning pre-trained model (CDSCL). CDSCL consists of two parts: self-supervised contrastive learning pre-trained model and CD classification network. The main contributions of this article are as follows: 1) a data augmentation strategy based on Gaussian noise is proposed to improve the ability of the model to extract variation information from HSIs with different random Gaussian noises; 2) based on Information Bottleneck (IB) theory, a progressive feature extraction module (PFEM) is developed to remove redundant or irrelevant details in changing information spectrum; 3) a contrastive loss function based on Pearson correlation coefficient and negative cosine correlation is designed to make the features extracted by the two branches of the siamese network close to each other. Experimental results on four real hyperspectral datasets demonstrate that the CD performance of CDSCL outperforms the most representative CD methods.

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SSCNN-S: A Spectral-Spatial Convolution Neural Network with Siamese Architecture for Change Detection

Cited by 38 publications

References 51 publications

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

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

CBW-MSSANet: A CNN Framework With Compact Band Weighting and Multiscale Spatial Attention for Hyperspectral Image Change Detection

A Hyperspectral Image Change Detection Framework With Self-Supervised Contrastive Learning Pretrained Model

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