Spatial–Spectral Attention Network Guided With Change Magnitude Image for Land Cover Change Detection Using Remote Sensing Images

Lv, Zhiyong; Wang, Fengjun; Cui, Guoqing; Benediktsson, Jón Atli; Lei, Tao; Sun, Weiwei

doi:10.1109/tgrs.2022.3197901

Cited by 95 publications

(39 citation statements)

References 56 publications

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“…To evaluate the accuracy of the proposed MRFPCM, four popular evaluation indicators, including false alarm (F A), missed alarm (M A), total error (T E), and binary classification Kappa coefficient (Ka) are employed [19]. The calculation equations are as follows:…”

Section: E Evaluation Indicatorsmentioning

confidence: 99%

“…To overcome the drawback, CD approaches considering spatial information are proposed. Spatial contextual information extraction based pixel [15], [16] and objectoriented approaches [17], [18] are effective to extract spatial information for RSIs.…”

Section: Introductionmentioning

confidence: 99%

“…The pixel based methods based on spatial contextual information uses an image block [19], network structure [20], and Markov random field (MRF) model [21], etc. to obtain spatial information about the pixel context.…”

Section: Introductionmentioning

confidence: 99%

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Multiresolution-Based Rough Fuzzy Possibilistic C-Means Clustering Method for Land Cover Change Detection

Xiao

Wan

Chen³

et al. 2023

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

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Object-oriented change detection (OOCD) plays an important role in remote sensing change detection. Generally, most of current OOCD methods adopt the highest predicted probability to determine whether objects have changes. However, it ignores the fact that only parts of an object have changes, which will generate the uncertain classification information. To reduce the classification uncertainty, an improved rough-fuzzy possibilistic c-means clustering algorithm combined with multiresolution scales information (MRFPCM) is proposed. Firstly, stacked bitemporal images are segmented using the multiresolution segmentation approach from coarse to fine scale. Secondly, objects at the coarsest scale are classified into changed, unchanged and uncertain categories by the proposed MRFPCM. Thirdly, all the changed and unchanged objects in previous scales are combined as training samples to classify the uncertain objects into new changed, unchanged and uncertain objects. Finally, segmented objects are classified layer by layer based on the MRFPCM until there are no uncertain objects. The MRFPCM method is validated on three data sets with different land change complexity and compared with five widely used change detection methods. The experimental results demonstrate the effectiveness and stability of the proposed approach.

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Section: E Evaluation Indicatorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multiresolution-Based Rough Fuzzy Possibilistic C-Means Clustering Method for Land Cover Change Detection

Xiao

Wan

Chen³

et al. 2023

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

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show abstract

“…Specifically, it is difficult to design useful feature extraction operators for traditional methods, since remote sensing images are usually more complex than other natural images [11]. By contrast, deep learning methods [12][13][14], especially convolutional neural networks (CNNs), have been widely used in various fields due to their strong feature discrimination abilities [15][16][17]. As a result, a large number of deep learning-based approaches [17][18][19][20] have been reported, although they achieve better change detection results by employing various of improved CNNs, they still face some challenges.…”

Section: Introductionmentioning

confidence: 99%

“…By contrast, deep learning methods [12][13][14], especially convolutional neural networks (CNNs), have been widely used in various fields due to their strong feature discrimination abilities [15][16][17]. As a result, a large number of deep learning-based approaches [17][18][19][20] have been reported, although they achieve better change detection results by employing various of improved CNNs, they still face some challenges. On the one hand, the prevailing pooling operation in CNNs easily leads to a difficulty of detail feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Local and Global Feature Learning With Kernel Scale-Adaptive Attention Network for VHR Remote Sensing Change Detection

Lei

Xue

Ning

et al. 2022

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

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Change detection is an important task of identifying changed information by comparing bitemporal images over the same geographical area. Currently, many existing methods based on U-Net and attention mechanism have greatly promoted the development of change detection techniques. However, they still suffer from two main challenges. First, faced with the diversity of ground objects and the flexibility of scale changes, vanilla attention mechanisms cripple spatial flexibility in learning object details due to the same scale convolution kernels at different convolution layers. Second, the complex background and high similarity between changed information and nonchanged information makes it difficult to fuse low-level details and high-level semantic by simple skip-connection in U-Net. To address the above issues, a local and global feature learning with kernel scale-adaptive attention network (LGSAA-Net) is proposed in this paper. The proposed network makes two contributions. First, a scale-adaptive attention (SAA) module has been designed to exploit the relationships between feature maps and convolutional kernel scales. The SAA module can achieve better feature discrimination than vanilla attention mechanism. Second, a multilayer perceptron based on patches embedding (MLPPE) has been employed by skip-connection to learn the local and global pixel association, which is helpful for achieving globally deep fusion of low-level details and high-level semantics. Finally, experiments and ablation studies are conducted on three datasets of LEVIR/WHU/GZ. Experimental results demonstrate that the proposed LGSAA-Net performs favorably against comparative

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A hybrid U‐shaped and transformer network for change detection in high‐resolution remote sensing images

Wu,

Yuan,

Zhan

2024

IET Image Processing

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Deep convolutional neural networks based remote sensing change detection has recently shown significant performance improvement. However, small region changes and global‐local features in high‐resolution remote sensing images are not fully explored. This paper introduces a hybrid U‐shaped and transformer network for change detection in high‐resolution remote sensing images. Specifically, a UNet++‐based backbone to facilitate feature learning across different scales. In addition, we introduce a transformer‐based feature fusion module for extracting long‐range dependencies, which can enhance the representation ability of the network. Furthermore, the introduced efficient channel attention mechanism can efficiently calibrate the feature representation and concentrate on more important feature information. Thanks to the above designs, the proposed method enjoys a strong ability to extract local and global features for remote sensing change detection. Extensive experimental results on different remote sensing images show that our method can achieve superior performance in comparison with state‐of‐the‐art change detection methods.

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Spatial–Spectral Attention Network Guided With Change Magnitude Image for Land Cover Change Detection Using Remote Sensing Images

Cited by 95 publications

References 56 publications

Multiresolution-Based Rough Fuzzy Possibilistic C-Means Clustering Method for Land Cover Change Detection

Multiresolution-Based Rough Fuzzy Possibilistic C-Means Clustering Method for Land Cover Change Detection

Local and Global Feature Learning With Kernel Scale-Adaptive Attention Network for VHR Remote Sensing Change Detection

A hybrid U‐shaped and transformer network for change detection in high‐resolution remote sensing images

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