Building Change Detection for Aerial Images Based on Attention Pyramid Network

Qinglin, 田青林 Tian; Kai, 秦凯 Qin; Jun, 陈俊 Chen; Yao, 李瑶 Li; Xuejiao, 陈雪娇 Chen

doi:10.3788/aos202040.2110002

Cited by 4 publications

(2 citation statements)

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“…Zhang Cuijun et al [14] proposed a change detection model that combines the use of asymmetric convolution blocks and the convolutional block attention module (CBAM) to improve U-Net network and convert change detection into a pixel-level binary classification task, thereby achieving end-to-end detection. Tian Qinglin et al [38] proposed a building change detection method based on an attention pyramid network, adding dilated convolution and pyramid pooling structure to the CNN in the encoder to expand the receptive field and extract multi-scale features, introducing attention mechanism in the decoder, and using the top-down dense connection to calculate the feature pyramid, fully fusing multi-level features to solve the problem of missed detection and indistinct detection boundaries of multi-scale features occurring in detection. Ji Shunping et al [7] proposed the FACNN model using the U-Net framework to obtain finer multi-scale information by replacing the normal convolution in the encoder with the dilated convolution and using atrous spatial pyramid pooling (ASPP).…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Remote Sensing Image Change Detection Method Based on Improved Siamese U-Net

Wang

et al. 2023

Remote Sensing

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Focusing on problems of blurred detection boundary, small target miss detection, and more pseudo changes in high-resolution remote sensing image change detection, a change detection algorithm based on Siamese neural networks is proposed. Siam-FAUnet can implement end-to-end change detection tasks. Firstly, the improved VGG16 is utilized as an encoder to extract the image features. Secondly, the atrous spatial pyramid pooling module is used to increase the receptive field of the model to make full use of the global information of the image and obtain the multi-scale contextual information of the image. The flow alignment module is used to fuse the low-level features in the encoder to the decoder and solve the problem of semantic misalignment caused by the direct concatenation of features when the features are fused, so as to obtain the change region of the image. The experiments are trained and tested using publicly available CDD and SZTAKI datasets. The results show that the evaluation metrics of the Siam-FAUnet model are improved compared to the baseline model, in which the F1-score is improved by 4.00% on the CDD and by 7.32% and 2.62% on the sub-datasets of SZTAKI (SZADA and TISZADOB), respectively; compared to other state-of-the-art methods, the Siam-FAUnet model has improved in both evaluation metrics, indicating that the model has a good detection performance.

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Section: Introductionmentioning

confidence: 99%

High-Resolution Remote Sensing Image Change Detection Method Based on Improved Siamese U-Net

Wang

et al. 2023

Remote Sensing

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“…With the rising demand for urban management, resource investigation, and environmental monitoring, accurate acquisition of the distribution of designated semantic areas in remote sensing images has become an important source of information for management decision-making [1] . Buildings are closely related to human life and are one of the key elements of a city and the dynamic update monitoring of buildings is of great significance to the supervision of the land department [2][3] . The extraction of accurate information from remote sensing images often uses visual interpretation and manual survey methods, which not only consume a lot of manpower and material resources but also have poor accuracy and low efficiency, which cannot meet the requirements for accurate and rapid extraction of building information.…”

Section: Introductionmentioning

confidence: 99%

A semantic segmentation method of buildings in remote sensing image based on improved UNet

Liu²,

Yin³

et al. 2022

2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022)

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Aiming at the problem of model instability and overfitting of deep neural networks with the deepening of the number of network layers, the current mainstream method is to use batch normalization (BN) to alleviate them. However, since the BN method is more sensitive to batch size when the batch size is small, the model performance will be poor. For a relatively large model, due to the limitation of video memory, the batch size cannot take a large value, limiting the model's performance. Because of the dependence of BN on batch size, this paper adopts group normalization (GN) instead of batch normalization (BN) in the UNet network to alleviate the impact of the model on batch size. Then experiments are carried out on the WHUBuilding dataset. The experimental results show that the improved model (UNet-GN) improves the mean intersection over union (MIoU) and mean pixel accuracy (MPA) by 10.66% and 1.65% respectively compared with the original model (UNet-BN).

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Semantic segmentation of buildings in remote sensing images based on dual-path network with rich-scale features

Huang

Sun

et al. 2022

J. Electron. Imag.

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. To solve the problems of low utilization of spatial features and incomplete contour segmentation in building semantic segmentation of remote sensing images, a building semantic segmentation method in remote sensing images based on dual-path with rich-scale features is proposed. In the shallow spatial path of proposed method, Res2Net module and inception module are used to extract shallow rich scale features to avoid improper use of shallow features affecting segmentation results. In the deep semantic path, ResNet50 combined with hybrid dilated convolution is used as the backbone network, and the obtained high-level semantic features are pooled by a spatial pyramid to capture the deeper multi-scale features. Finally, a new feature fusion module is designed to assign weights to feature maps of different levels extracted from two paths. Experimental results on WHU and Massachusetts building datasets show that the proposed method has higher building extraction accuracy and better generalization ability compared with other semantic segmentation networks.

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Building Change Detection for Aerial Images Based on Attention Pyramid Network

Cited by 4 publications

References 0 publications

High-Resolution Remote Sensing Image Change Detection Method Based on Improved Siamese U-Net

High-Resolution Remote Sensing Image Change Detection Method Based on Improved Siamese U-Net

A semantic segmentation method of buildings in remote sensing image based on improved UNet

Semantic segmentation of buildings in remote sensing images based on dual-path network with rich-scale features

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