Siamese Network With Multi-Level Features for Patch-Based Change Detection in Satellite Imagery

Rahman, Faiz Ur; Vasu, Bhavan; Cor, Jared Van; Kerekes, John P.; Savakis, Andreas

doi:10.1109/globalsip.2018.8646512

Cited by 50 publications

(24 citation statements)

References 17 publications

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“…To train the model properly with limited labelled samples, we introduce a sampling method based on the strategy of bootstrapping, which is implemented by constructing a number of resamples with replacement of the training samples [45]. Specifically, random sampling can be performed to extracting a certain number of samples, which are reused with new samples in the next iterative training process.…”

Section: Bootstrapping and Sampling Methods For Trainingmentioning

confidence: 99%

“…The average GS of all the feature bands are used to determine the best image segmentation scale, where the optimal segmentation scale is identified as the one with the lowest average GS value. For the experimental data, the segmentation scales of three datasets are set to [30,35,40,45,50], [25,30,35,40,45] and [25,30,35,40,45], respectively. The results on different segmentation scale are shown in Figure 6.…”

Section: Multi-resolution Segmentationmentioning

confidence: 99%

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A Deep Siamese Network with Hybrid Convolutional Feature Extraction Module for Change Detection Based on Multi-sensor Remote Sensing Images

et al. 2020

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Information extraction from multi-sensor remote sensing images has increasingly attracted attention with the development of remote sensing sensors. In this study, a supervised change detection method, based on the deep Siamese convolutional network with hybrid convolutional feature extraction module (OB-DSCNH), has been proposed using multi-sensor images. The proposed architecture, which is based on dilated convolution, can extract the deep change features effectively, and the character of “network in network” increases the depth and width of the network while keeping the computational budget constant. The change decision model is utilized to detect changes through the difference of extracted features. Finally, a change detection map is obtained via an uncertainty analysis, which combines the multi-resolution segmentation, with the output from the Siamese network. To validate the effectiveness of the proposed approach, we conducted experiments on multispectral images collected by the ZY-3 and GF-2 satellites. Experimental results demonstrate that our proposed method achieves comparable and better performance than mainstream methods in multi-sensor images change detection.

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Section: Bootstrapping and Sampling Methods For Trainingmentioning

confidence: 99%

Section: Multi-resolution Segmentationmentioning

confidence: 99%

A Deep Siamese Network with Hybrid Convolutional Feature Extraction Module for Change Detection Based on Multi-sensor Remote Sensing Images

et al. 2020

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“…To generate change maps, the output feature maps in the two periods can be directly classified by the concatenation of channels [91,155,161] or can be used to produce difference maps using a certain distance metric [9], and then used for further change analysis [162,163]. To retain multi-scale change information, feature maps at different depths can be concatenated for change detection [164][165][166][167], and this works well.…”

Section: Siamese Structurementioning

confidence: 99%

Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

et al. 2020

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Change detection based on remote sensing (RS) data is an important method of detecting changes on the Earth’s surface and has a wide range of applications in urban planning, environmental monitoring, agriculture investigation, disaster assessment, and map revision. In recent years, integrated artificial intelligence (AI) technology has become a research focus in developing new change detection methods. Although some researchers claim that AI-based change detection approaches outperform traditional change detection approaches, it is not immediately obvious how and to what extent AI can improve the performance of change detection. This review focuses on the state-of-the-art methods, applications, and challenges of AI for change detection. Specifically, the implementation process of AI-based change detection is first introduced. Then, the data from different sensors used for change detection, including optical RS data, synthetic aperture radar (SAR) data, street view images, and combined heterogeneous data, are presented, and the available open datasets are also listed. The general frameworks of AI-based change detection methods are reviewed and analyzed systematically, and the unsupervised schemes used in AI-based change detection are further analyzed. Subsequently, the commonly used networks in AI for change detection are described. From a practical point of view, the application domains of AI-based change detection methods are classified based on their applicability. Finally, the major challenges and prospects of AI for change detection are discussed and delineated, including (a) heterogeneous big data processing, (b) unsupervised AI, and (c) the reliability of AI. This review will be beneficial for researchers in understanding this field.

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“…In general, the issue of change detection can be attributed to finding and matching pairs of images [50][51][52]. The rule of the method is based on the idea that by determining whether a similar semantic object in the second image exists or not to express unchanged and changed.…”

Section: Semantic Correspondence Mechanismmentioning

confidence: 99%

PGA-SiamNet: Pyramid Feature-Based Attention-Guided Siamese Network for Remote Sensing Orthoimagery Building Change Detection

Jiang

et al. 2020

Remote Sensing

182

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In recent years, building change detection has made remarkable progress through using deep learning. The core problems of this technique are the need for additional data (e.g., Lidar or semantic labels) and the difficulty in extracting sufficient features. In this paper, we propose an end-to-end network, called the pyramid feature-based attention-guided Siamese network (PGA-SiamNet), to solve these problems. The network is trained to capture possible changes using a convolutional neural network in a pyramid. It emphasizes the importance of correlation among the input feature pairs by introducing a global co-attention mechanism. Furthermore, we effectively improved the long-range dependencies of the features by utilizing various attention mechanisms and then aggregating the features of the low-level and co-attention level; this helps to obtain richer object information. Finally, we evaluated our method with a publicly available dataset (WHU) building dataset and a new dataset (EV-CD) building dataset. The experiments demonstrate that the proposed method is effective for building change detection and outperforms the existing state-of-the-art methods on high-resolution remote sensing orthoimages in various metrics.

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Siamese Network With Multi-Level Features for Patch-Based Change Detection in Satellite Imagery

Cited by 50 publications

References 17 publications

A Deep Siamese Network with Hybrid Convolutional Feature Extraction Module for Change Detection Based on Multi-sensor Remote Sensing Images

A Deep Siamese Network with Hybrid Convolutional Feature Extraction Module for Change Detection Based on Multi-sensor Remote Sensing Images

Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

PGA-SiamNet: Pyramid Feature-Based Attention-Guided Siamese Network for Remote Sensing Orthoimagery Building Change Detection

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