A CNN Framework With Slow-Fast Band Selection and Feature Fusion Grouping for Hyperspectral Image Change Detection

Ou, Xianfeng; Liu, Liangzhen; Tu, Bing; Zhang, Guoyun; Xu, Zhi

doi:10.1109/tgrs.2022.3156041

Cited by 40 publications

(20 citation statements)

References 31 publications

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“…Two algebraic analysis methods are CVA [14] and principal component analysis change vector analysis (PCA-CVA) [52]. Three deep learning based methods are GETNET [28], SSA-SiamNet [31], and SFBS-FFGNET [32], where SSA-SiamNet is a supervised method and its code is reproduced from the original paper. The self-supervised pre-trained model is the core of CDSCL, so we design CDSCL without a pretrained model as an ablation experiment.…”

Section: A Experiments Settingmentioning

confidence: 99%

“…In weakly-supervised CD methods, the selection of high-confidence pseudo-labels is crucial for detection performance. First, SFBS [32] is used to reduce the dimensionality of the HSI dataset, retaining the bands that are conducive to change detection. Second, binary pseudolabels are generated using CVA algebraic analysis and k-means clustering.…”

Section: A Experiments Settingmentioning

confidence: 99%

“…We conduct five repeated experiments for each deep learning-based method, and use the obtained mean and variance as the experimental results to intuitively reflect the performance and robustness of the method. To comprehensively evaluate the CD performance of CD-SCL and all comparison methods, a comprehensive index evaluation system was established in this experiment, including overall detection accuracy (OA), OA of changed area (OA CHG), OA of unchanged area (OA UN), and Kappa coefficient for consistency assessment [32]. All evaluation metrics used in the experiments are calculated as follows:…”

Section: A Experiments Settingmentioning

confidence: 99%

“…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. A CNN framework with slow-fast band selection (SFBS) and feature fusion grouping (SFBS-FFGNET) [32] designs SFBS to select slow and fast changing bands in HSIs, and also designs feature fusion grouping (FFG) module to change the direction of the feature vector. A multiscale fully convolutional neural network (MFCN) [33] uses multiscale convolution kernels to extract detailed features of ground features, and also designs a loss function to solve the problem of imbalanced positive and negative samples.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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Section: A Experiments Settingmentioning

confidence: 99%

Section: A Experiments Settingmentioning

confidence: 99%

Section: A Experiments Settingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…The HSI CD method based on deep learning can directly learn robust change features from dual-temporal images, and perform two classifications of pixels using the learned features [27]. Some of the most common deep learning frameworks such as convolutional neural networks (CNN) [28], recurrent neural networks (RNN) [29], deep belief networks (DBN) [30], etc.…”

Section: Introductionmentioning

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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Dimensionality reduction strategies for land use land cover classification based on airborne hyperspectral imagery: a survey

Moharram

Sundaram

2022

Environ Sci Pollut Res

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A CNN Framework With Slow-Fast Band Selection and Feature Fusion Grouping for Hyperspectral Image Change Detection

Cited by 40 publications

References 31 publications

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

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

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

Dimensionality reduction strategies for land use land cover classification based on airborne hyperspectral imagery: a survey

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