A Deep Learning Method for Change Detection in Synthetic Aperture Radar Images

Li, Yangyang; Cheng, Peng; Chen, Yanqiao; Jiao, Licheng; Zhou, Linhao; Shang, Ronghua

doi:10.1109/tgrs.2019.2901945

Cited by 150 publications

(85 citation statements)

References 31 publications

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“…Their three key problems include: (1) suppressing speckle noise; (2) designing a change metric or a change indicator; and (3) using a threshold or a classifier based on a change metric to generate a final change map. Change detection methods using AI techniques, especially an autoencoder (AE) [97][98][99][100][101][102][103][104][105][106][107] and a convolutional neural network (CNN) [108][109][110][111][112][113][114], to suppress speckle noise and extract features has been proven to be the state of the art. In the overall process and framework of methods, they are similar to the methods based on optical RS images, and the detailed framework and AI model introduction are analyzed in Sections 4 and 5.…”

Section: Sar Imagesmentioning

confidence: 99%

“…The direct concatenation method can retain all the information of the multi-period data, so the change information is extracted by the subsequent classifier. 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%

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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: Sar Imagesmentioning

confidence: 99%

Section: Direct Classification Structurementioning

confidence: 99%

Section: Unsupervised Schemes In Change Detection Frameworkmentioning

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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“…The learning-based change detection algorithms can be divided into two types: one does not need to generate DI, such as [28,29]. The other, however, needs to generate DI, such as [30,31].…”

Section: Introductionmentioning

confidence: 99%

SAR Image Change Detection Based on Data Optimization and Self-Supervised Learning

Wang

et al. 2020

IEEE Access

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In the SAR change detection algorithm based on self-supervised learning, speckle noise reduces the difference image (DI) quality. Therefore, the contrast of the DI is low, and its change area is not significant. Moreover, the preclassification algorithm with the poor robustness makes the classification results of the low-quality DI inaccurate. When the wrong labels are sent into the classification network, the accuracy of the final detection results is reduced. First, to improve the quality of the initial DI, we design an adaptive gamma correction algorithm that adjusts the contrast according to the mean value of the initial DI and the variation coefficient β. The contrast of the new DI generated by this algorithm is higher. Furthermore, to suppress the noise, we adopt a new algorithm based on popular ranking to obtain the saliency map of the new DI. Combining the initial DI with this saliency map, a high-quality DI with a low noise level is obtained. After that, we introduce the structure tensor into the fuzzy local information c-means clustering algorithm (FLICM) to classify the DI more accurately. The new clustering algorithm improves the accuracy of preclassification, especially its hierarchical version. Besides, we use the structure tensor to generate the structure maps of the original images. Finally, according to the prior information obtained from the preclassification, we use a convolution wavelet neural network (CWNN) to supervise and train the structure maps of the original images. Experimental results show that the DI generated by us is closer to the groundtruth than other methods. Our preclassification algorithm performs better. Our algorithm shows higher detection accuracy for SAR images with strong noise than some advanced change detection algorithms. INDEX TERMS Adaptive gamma correction, convolution wavelet neural network, fuzzy local information c-means clustering, saliency map, structure tensor.

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“…Among numerous image interpretation techniques, scene classification using remote sensing images attracts considerable research interest. For decades, scene classification has been widely applied in many fields such as natural disaster monitoring [2,3,4,5], land use and land cover classification [6,7], target detection [8,9,10], geographical space targets monitoring [11], geographical images search [12], vegetation mapping [13], environment monitoring and city planning [14]. SAR is an active earth observation system that offers all-day and almost all-weather advantages over other sensors.…”

Section: Introductionmentioning

confidence: 99%

A New Deep Learning Algorithm for SAR Scene Classification Based on Spatial Statistical Modeling and Features Re-Calibration

Chen

Cui

et al. 2019

Sensors

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Synthetic Aperture Radar (SAR) scene classification is challenging but widely applied, in which deep learning can play a pivotal role because of its hierarchical feature learning ability. In the paper, we propose a new scene classification framework, named Feature Recalibration Network with Multi-scale Spatial Features (FRN-MSF), to achieve high accuracy in SAR-based scene classification. First, a Multi-Scale Omnidirectional Gaussian Derivative Filter (MSOGDF) is constructed. Then, Multi-scale Spatial Features (MSF) of SAR scenes are generated by weighting MSOGDF, a Gray Level Gradient Co-occurrence Matrix (GLGCM) and Gabor transformation. These features were processed by the Feature Recalibration Network (FRN) to learn high-level features. In the network, the Depthwise Separable Convolution (DSC), Squeeze-and-Excitation (SE) Block and Convolution Neural Network (CNN) are integrated. Finally, these learned features will be classified by the Softmax function. Eleven types of SAR scenes obtained from four systems combining different bands and resolutions were trained and tested, and a mean accuracy of 98.18% was obtained. To validate the generality of FRN-MSF, five types of SAR scenes sampled from two additional large-scale Gaofen-3 and TerraSAR-X images were evaluated for classification. The mean accuracy of the five types reached 94.56%; while the mean accuracy for the same five types of the former tested 11 types of scene was 96%. The high accuracy indicates that the FRN-MSF is promising for SAR scene classification without losing generality.

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A Deep Learning Method for Change Detection in Synthetic Aperture Radar Images

Cited by 150 publications

References 31 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

SAR Image Change Detection Based on Data Optimization and Self-Supervised Learning

A New Deep Learning Algorithm for SAR Scene Classification Based on Spatial Statistical Modeling and Features Re-Calibration

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