Saliency-Guided Deep Neural Networks for SAR Image Change Detection

Geng, Jie; Ma, Xiaorui; Zhou, Xiaojun; Wang, Hongyu

doi:10.1109/tgrs.2019.2913095

Cited by 86 publications

(58 citation statements)

References 49 publications

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“…In general, the one-dimensional input data is directly concatenated [24,42,101,[150][151][152], while the two-dimensional data is concatenated by channel [111,112,153,154]. Moreover, the fusion of original data and difference data [21,99] is another good strategy, which can keep all the information while highlighting the difference information.…”

Section: Direct Classification Structurementioning

confidence: 99%

“…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%

“…The training and testing processes are performed alternately and iteratively. During the training process, the model is optimized according to a learning criterion, which can be a loss function in deep learning (e.g., softmax loss [21], contrastive loss [22], Euclidean loss [23], or cross-entropy loss [24]). By monitoring the training process and test accuracy, the convergence state of the AI model can be obtained, which can help in adjusting its hyperparameters (such as the learning rate), and also in judging whether the model performance has reached the best (i.e., termination) condition; 4.…”

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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: Direct Classification Structurementioning

confidence: 99%

Section: Unsupervised Schemes In Change Detection Frameworkmentioning

confidence: 99%

mentioning

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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“…In recent years, the OWA operator and IOWA operator have received increasing attention [53] and have been used in a wide range of applications including group decision-making [54,55], neural networks [56][57][58][59], database mining [60], etc.…”

Section: Calculate Related Parameters Base On the Obtained Matrixmentioning

confidence: 99%

Combined Conflict Evidence Based on Two-Tuple IOWA Operators

2019

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Due to poor natural factors and human interference, the information that was obtained by sensors tends to have high uncertainty and high conflict with others. A combination of highly conflicting evidence with Dempster’s rule often produces results that run counter to intuition. To solve the above problem, a conflict evidence combination methodology is proposed in this article, which contains the distance of evidence, classical conflict coefficient, and two-tuple IOWA operator. Both the classical conflict coefficient and Jousselme distance indicate the degree of evidence conflict, and it is clear that the two parameters are symmetrical. First, the two-tuple IOWA operator is proposed. Second, the orness is determined by aggregated data; then, the weighting vector is calculated by a maximal entropy method. Finally, the weighted average is the evidence in the system by a two-tuple IOWA operator; then, the Dempster combination rule is utilized to fuse information. Compared with other existing methods, the presented methodology has high performance when dealing with conflict evidence and has strong anti-interference ability.

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“…However, the model of equipment may not be modeled well when the equipment is complicated. The data-driven technique does not require the creation of a physical model of the device; use the monitored data during the operation of the equipment to diagnose the fault type of the equipment, for example, machine learning, [19][20][21][22] multivariate statistical analysis, 23 signal processing, 24 rough set, 25,26 fuzzy set, 27 and multi-sensors or multi-sources information fusion method. [28][29][30][31] In the multi-sensors information fusion based method, in which the data of multiple sensors (or sources) are fused, reflects the diversity, redundancy, and complementarity of multiple information.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis method based on time domain weighted data aggregation and information fusion

Zhang

Jiang

Deng

2019

International Journal of Distributed Sensor Networks

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Fault diagnosis of equipment is a key issue in the industrial field, and it is essential to keep abreast of equipment status. However, previous studies either considered fault data at a single moment or gave the same weight to data over a period of time. In view of the problems above, fault diagnosis method based on time domain weighted data aggregation and information fusion is proposed in this article. First, the monitored data of sensors loaded by the equipment are aggregated utilizing the linear decaying weights. Then, Gaussian models of each fault type under different fault features are established based on aggregated data. And the basic probability assignments are generated by matching aggregated testing samples with the constructed Gaussian model. At last, the basic probability assignments generated under each fault feature are fused by Dempster combination rule. The proposed method is verified and the results show that the total fault recognition rate can reach 97.5%, which increased by 1.9% compared with the method that Gaussian model constructed by original data.

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Saliency-Guided Deep Neural Networks for SAR Image Change Detection

Cited by 86 publications

References 49 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

Combined Conflict Evidence Based on Two-Tuple IOWA Operators

Fault diagnosis method based on time domain weighted data aggregation and information fusion

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