High-Resolution Remote Sensing Image Change Detection Combined With Pixel-Level and Object-Level

Lu, Xu; Jing, Weipeng; Song, Houbing; Chen, Guangsheng

doi:10.1109/access.2019.2922839

Cited by 43 publications

(24 citation statements)

References 23 publications

(21 reference statements)

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“…However, the image processing form of semantic segmentation can classify each pixel on the image to obtain the image classification result of the located pixel. U-Net is an extension of FCN and is currently a widely used semantic segmentation network with good scalability [52]. The excellent characteristics of U-Net make it widely used in remote sensing image classification and change detection and have achieved good results [50,52].…”

Section: Methodsmentioning

confidence: 99%

A Novel Squeeze-and-Excitation W-Net for 2D and 3D Building Change Detection with Multi-Source and Multi-Feature Remote Sensing Data

Zhang

Wang

et al. 2021

Remote Sensing

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Building Change Detection (BCD) is one of the core issues in earth observation and has received extensive attention in recent years. With the rapid development of earth observation technology, the data source of remote sensing change detection is continuously enriched, which provides the possibility to describe the spatial details of the ground objects more finely and to characterize the ground objects with multiple perspectives and levels. However, due to the different physical mechanisms of multi-source remote sensing data, BCD based on heterogeneous data is a challenge. Previous studies mostly focused on the BCD of homogeneous remote sensing data, while the use of multi-source remote sensing data and considering multiple features to conduct 2D and 3D BCD research is sporadic. In this article, we propose a novel and general squeeze-and-excitation W-Net, which is developed from U-Net and SE-Net. Its unique advantage is that it can not only be used for BCD of homogeneous and heterogeneous remote sensing data respectively but also can input both homogeneous and heterogeneous remote sensing data for 2D or 3D BCD by relying on its bidirectional symmetric end-to-end network architecture. Moreover, from a unique perspective, we use image features that are stable in performance and less affected by radiation differences and temporal changes. We innovatively introduced the squeeze-and-excitation module to explicitly model the interdependence between feature channels so that the response between the feature channels is adaptively recalibrated to improve the information mining ability and detection accuracy of the model. As far as we know, this is the first proposed network architecture that can simultaneously use multi-source and multi-feature remote sensing data for 2D and 3D BCD. The experimental results in two 2D data sets and two challenging 3D data sets demonstrate that the promising performances of the squeeze-and-excitation W-Net outperform several traditional and state-of-the-art approaches. Moreover, both visual and quantitative analyses of the experimental results demonstrate competitive performance in the proposed network. This demonstrates that the proposed network and method are practical, physically justified, and have great potential application value in large-scale 2D and 3D BCD and qualitative and quantitative research.

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Section: Methodsmentioning

confidence: 99%

A Novel Squeeze-and-Excitation W-Net for 2D and 3D Building Change Detection with Multi-Source and Multi-Feature Remote Sensing Data

Zhang

Wang

et al. 2021

Remote Sensing

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“…The confusion matrix has calculated in order to evaluate the results of change detection accuracy. Furthermore, the overall accuracy (OA), false alarm rate (FA), missed detection rate (MA) and Kappa coefficient (KC) were calculated by the confusion matrix, which are the commonly used evaluation indexes for the change detection accuracy [32,36,66]. The MA indicates the proportion of the unmeasured area in the real change area to the real change area, which reflects the degree of missed detection of the change detection.…”

Section: Experiments and Analysismentioning

confidence: 99%

Change Detection Method of High Resolution Remote Sensing Image Based on D-S Evidence Theory Feature Fusion

et al. 2021

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“…The information extracted from these features is very crucial for subsequent analysis and application. Xu et al improve pixel-level detection method, which can more accurately detect changes in RS images [13]. Peng et al [14] trained a nonlinear kernel function expression using the Ideal Regularization Kernel, and then combined it with Support Vector Machine for classification.…”

Section: Related Workmentioning

confidence: 99%

Multi-Label Remote Sensing Image Classification with Latent Semantic Dependencies

Jing

Chen

et al. 2020

Remote Sensing

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Deforestation in the Amazon rainforest results in reduced biodiversity, habitat loss, climate change, and other destructive impacts. Hence obtaining location information on human activities is essential for scientists and governments working to protect the Amazon rainforest. We propose a novel remote sensing image classification framework that provides us with the key data needed to more effectively manage deforestation and its consequences. We introduce the attention module to separate the features which are extracted from CNN(Convolutional Neural Network) by channel, then further send the separated features to the LSTM(Long-Short Term Memory) network to predict labels sequentially. Moreover, we propose a loss function by calculating the co-occurrence matrix of all labels in the dataset and assigning different weights to each label. Experimental results on the satellite image dataset of the Amazon rainforest show that our model obtains a better F 2 score compared to other methods, which indicates that our model is effective in utilizing label dependencies to improve the performance of multi-label image classification.

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High-Resolution Remote Sensing Image Change Detection Combined With Pixel-Level and Object-Level

Cited by 43 publications

References 23 publications

A Novel Squeeze-and-Excitation W-Net for 2D and 3D Building Change Detection with Multi-Source and Multi-Feature Remote Sensing Data

A Novel Squeeze-and-Excitation W-Net for 2D and 3D Building Change Detection with Multi-Source and Multi-Feature Remote Sensing Data

Change Detection Method of High Resolution Remote Sensing Image Based on D-S Evidence Theory Feature Fusion

Multi-Label Remote Sensing Image Classification with Latent Semantic Dependencies

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