SAR Image Change Detection Using PCANet Guided by Saliency Detection

Li, Mengke; Li, Ming; Zhang, Peng; Wu, Yan; An, Lin

doi:10.1109/lgrs.2018.2876616

Cited by 59 publications

(33 citation statements)

References 13 publications

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“…It can be seen that there are two change detection stages in this scheme. The first stage, i.e., preclassification, is usually simple but worth studying, and most of them are unsupervised methods, which can be implemented with difference analysis and clustering [101], such as K-means [162], fuzzy c-means (FCM) [90,99,100,111,151,160,165,[178][179][180][181], spatial FCM [102,154], or hierarchical FCM [21,113]. This stage in some works are implemented by threshold analysis [18,39], saliency analysis [78], or well-designed rules [38,83,84,124,148,182,183].…”

Section: Unsupervised Schemes In Change Detection Frameworkmentioning

confidence: 99%

“…For instance, the region CNN (R-CNN), primitively designed for object detection in CV, contains a region-proposals structure to predict the regions of the changed objects [87,196,197]. The PCANet, with its convolution filter banks chosen from PCA filters, is able to reduce the influence of speckle noise and has been used in SAR image change detection [178,180]. More recent work proposes a kernel PCA convolution to extract representative spatial-spectral features from RS images in an unsupervised manner [163].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

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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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Section: Unsupervised Schemes In Change Detection Frameworkmentioning

confidence: 99%

Section: Convolutional Neural Networkmentioning

confidence: 99%

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

et al. 2020

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“…In summary, the extraction of ROI of video frames can be completed, which solves the problem that the Itti model extracts the incomplete ROI and the positioning edge is blur. In order to verify the effectiveness of the above algorithm, three pictures from MSRA, the 208th frame of the tennis sequences, and the 25th frame of the basketball sequences provided by JCT-VC are used to perform experiments comparatively for the ORC algorithm, the Itti algorithm [18], the GBVS algorithm [19], the SR algorithm [20], the FT algorithm [21], the CAS algorithm [22], and the LC algorithm [23].…”

Section: Feature Fusion Based On Adaptive Weightmentioning

confidence: 99%

Optimized Rate Control Algorithm of High-Efficiency Video Coding Based on Region of Interest

Song

Zhang

2020

Journal of Electrical and Computer Engineering

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Aiming at the problems that the strategy of target bit allocation at the CTU layer has deviations from the human subjective observation mechanism, and the update phase of parametric model has a higher complexity in the JCTVC-K0103 rate control algorithm of ITU-T H.265/high efficiency video coding (HEVC) standard. Optimized rate control (ORC) algorithm of ITU-T H.265/HEVC based on region of interest (ROI) is proposed. Firstly, the algorithm extracts the region of interest of video frames based on time and space domains by using the improved Itti model. Then, the weight of target bits w is recalculated based on space-time domains to improve the rate control accuracy, and the target bits are distributed based on ROI by the adaptive weight algorithm once again to make the output videos more attuned with the human visual attention mechanism. Finally, the quasi-Newton algorithm is used to update the rate distortion model, which reduces the computational complexity in the update phase of the parametric model. The experimental results show that the ORC algorithm can obtain a better subjective quality in the compressed results with less bit error compared with the other two algorithms. Meanwhile, the rate distortion performance of the ORC algorithm is better on the premise of guaranteeing rate control performance.

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“…Recently, change detection in SAR images using DNNs has gained significant research attention mainly due to their impressive performance, thanks to the powerful feature representation of DNNs [17]. For example, a variety of neural networks, like stacked contractive autoencoder [18], deep belief neural network [5], convolutional neural network [19], [20] and PCA-Net [21], [22], have been used to cope with the change detection of SAR images. However, the performance of DNNs highly depends on proper hyper-parameters configuration.…”

Section: Introductionmentioning

confidence: 99%

Application of Data Driven Optimization for Change Detection in Synthetic Aperture Radar Images

Liu

et al. 2020

IEEE Access

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Data-driven optimization is an efficient global optimization algorithm for expensive blackbox functions. In this paper, we apply data-driven optimization algorithm to the task of change detection with synthetic aperture radar (SAR) images for the first time. We first propose an easy-to-implement threshold algorithm for change detection in SAR images based on data-driven optimization. Its performance has been compared with commonly used methods like generalized Kittler and Illingworth threshold algorithms (GKIT). Next, we demonstrate how to tune the hyper-parameter of a (previously available) deep belief network (DBN) for change detection using data-driven optimization. Extensive evaluations are carried out using publicly available benchmark datasets. The obtained results suggest comparatively strong performance of our optimized DBN-based change detection algorithm. INDEX TERMS Hyper-parameter optimization, data-driven optimization, change detection, deep belief network (DBN), synthetic aperture radar (SAR) image.

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SAR Image Change Detection Using PCANet Guided by Saliency Detection

Cited by 59 publications

References 13 publications

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

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

Optimized Rate Control Algorithm of High-Efficiency Video Coding Based on Region of Interest

Application of Data Driven Optimization for Change Detection in Synthetic Aperture Radar Images

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