A Novel Deep Framework for Change Detection of Multi-source Heterogeneous Images

Liu, Hongying; Wang, Zhongshu; Shang, Fanhua; Zhang, Mingyang; Gong, Maoguo; Ge, Feihang; Jiao, Licheng

doi:10.1109/icdmw.2019.00034

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…For example, Qi Zhixin et al combine change vector analysis and support vector machines to detect land cover changes using polarimetric synthetic aperture radar (PolSAR) images (Qi et al, 2015). Inspired by the theory of metalearning, Liu Hongying et al improved the convolutional neural network and combined it with a graph neural network to achieve change detection in synthetic aperture radar and multispectral images (Liu et al, 2019). In order to explore the role that deep learning play in building damage detection, Francesco Nex et al tested and evaluated the structural damage of buildings by using a convolutional neural network based on unmanned aerial vehicle (UAV) captured images (Nex et al, 2019).…”

Section: Change Detection Of Satellite Imagerymentioning

confidence: 99%

Assessment of Buildings and Electrical Facilities Damaged by Flood and Earthquake From Satellite Imagery

Zhou

Wen

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Natural disasters cause considerable losses to people’s lives and property. Satellite images can provide crucial information of the affected areas for the first time, conducive to relieving the people in disaster and reducing the economic loss. However, the traditional satellite image analysis method based on manual processing drains workforce and material resources, which slowed the government’s response to the disaster. Aiming at the natural disasters like floods and earthquakes that often happen in the south of China, we propose a dual-stage damage assessment method based on LEDNet and ResNet. Our method detects the changes between the satellite images captured before and after a disaster of the same area, segments the buildings, and evaluates the damage level of affected buildings. In addition, we calculate influence maps based on the damage scale to the building and estimate the damage situation for electrical facilities. We used images related to earthquakes and floods in the xBD dataset to train the network model. Moreover, qualitative and quantitative evaluations demonstrated that our method has higher accuracy than the xBD baseline.

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Section: Change Detection Of Satellite Imagerymentioning

confidence: 99%

Assessment of Buildings and Electrical Facilities Damaged by Flood and Earthquake From Satellite Imagery

Zhou

Wen

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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show abstract

“…By optimizing the distance between extracted features, the final CM can be obtained with the learned CDMs. In [26], a novel framework for CD based on meta-learning was proposed, which used a convolutional neural network (CNN) to map two images to the same feature space and a graph convolutional network (GCN) to compare samples in the feature space. Besides, one can also transfer the first image from its original feature space to the feature space where the second image is.…”

Section: Introductionmentioning

confidence: 99%

A Feature Space Constraint-Based Method for Change Detection in Heterogeneous Images

et al. 2020

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With the development of remote sensing technologies, change detection in heterogeneous images becomes much more necessary and significant. The main difficulty lies in how to make input heterogeneous images comparable so that the changes can be detected. In this paper, we propose an end-to-end heterogeneous change detection method based on the feature space constraint. First, considering that the input heterogeneous images are in two distinct feature spaces, two encoders with the same structure are used to extract features, respectively. A decoder is used to obtain the change map from the extracted features. Then, the Gram matrices, which include the correlations between features, are calculated to represent different feature spaces, respectively. The squared Euclidean distance between Gram matrices, termed as feature space loss, is used to constrain the extracted features. After that, a combined loss function consisting of the binary cross entropy loss and feature space loss is designed for training the model. Finally, the change detection results between heterogeneous images can be obtained when the model is trained well. The proposed method can constrain the features of two heterogeneous images to the same feature space while keeping their unique features so that the comparability between features can be enhanced and better detection results can be achieved. Experiments on two heterogeneous image datasets consisting of optical and SAR images demonstrate the effectiveness and superiority of the proposed method.

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Identification of Building Damages using Satellite Images based on CNN- Recurrent Neural Network Approach

Chavhan,

Singh,

Sahebrao Doke

et al. 2023

2023 8th International Conference on Communication and Electronics Systems (ICCES)

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A Novel Deep Framework for Change Detection of Multi-source Heterogeneous Images

Cited by 4 publications

References 12 publications

Assessment of Buildings and Electrical Facilities Damaged by Flood and Earthquake From Satellite Imagery

Assessment of Buildings and Electrical Facilities Damaged by Flood and Earthquake From Satellite Imagery

A Feature Space Constraint-Based Method for Change Detection in Heterogeneous Images

Identification of Building Damages using Satellite Images based on CNN- Recurrent Neural Network Approach

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