Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks

By Maoguo Gong, Jiaojiao Zhao, Jia Liu, Qiguang Miao, L. Jiao. IEEE Transactions on Neural Networks and Learning Systems, 2016

593 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/d1cbceec6bf279ea473dc950e049cb99088711f9

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Citations in the corpus

Change Detection in Multisource VHR Images via Deep Siamese Convolutional Multiple-Layers Recurrent Neural Network (2020, methodology): “In [24], a CD method for multitemporal SAR images are proposed by constructing deep neural network (DNN).”
Fully Convolutional Siamese Networks for Change Detection (2018, background): “…began to be used in conjunction with simple machine learning techniques [6]; recently, more elaborate machine learning techniques (deep learning) are dominating most problems in the image analysis ﬁeld, and this evolution is slowly reaching the problem of change detection [7, 8, 9, 10, 11, 3, 12].”
A Deep Convolutional Coupling Network for Change Detection Based on Heterogeneous Optical and Radar Images (2018, methodology): “images, we compare our method with the wavelet fusion-based method [28] and the deep neural network-based method [38].”
Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture (2018, background): “DEEP structure neural networks and learnings have been applied in many fields and have achieved breakthrough successes in a great number of applications [1], [2], particularly in large scale data processing [3], [4].”
A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection (2020, methodology): “In light of the good performance in computer vision, deep-CNN-based methods have been widely applied in remote sensing tasks [25], such as land-use classification [39], semantic segmentation [40,41], image super-resolution [42], object detection [43,44] and change detection [5].”
Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges (2020, methodology): “, the Bern dataset, the Ottawa dataset, the Yellow River dataset, and the Mexico dataset [24,103], which cannot meet the needs of change detection in areas with complex land cover and various change types.”
A survey of deep neural network architectures and their applications (2017, methodology): “In particular, the synthetic aperture radar (SAR) image processing is a widely used application in change detection [48].”
Deep learning for change detection in remote sensing: a review (2022, methodology): “To date, this method has been widely used for urban land use (Zhang et al. 2016a), water (Gao et al. 2019a), and hazard and vegetation change detections (Gong et al. 2015) (Table 2).”
Self-Supervised Representation Learning for Remote Sensing Image Change Detection Based on Temporal Prediction (2020, methodology): “In order to validate the effectiveness of the proposed DADNN method, six closely related algorithms were implemented as comparison: subtraction, log-ratio operator, wavelet fusion [17], DNN [38], SSCNN [51], and SCCN [30].”
Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis (2020, methodology): “[65]- -Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks2016 (224)”
End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++ (2019, methodology): “Gong et al. [43] proposed a novel CD method for SAR images, where a CM is generated by using the trained Restricted Boltzmann Machine (RBM).”
Deep Learning-Based Change Detection in Remote Sensing Images: A Review (2022, methodology): “Gong et al. [116] presented an innovative CD method using deep learning for multi-temporal SAR images.”
A Deeply Supervised Attention Metric-Based Network and an Open Aerial Image Dataset for Remote Sensing Change Detection (2021, background): “This process is of great signiﬁcance to many ﬁelds, including environmental investigation [2], geological disaster monitoring [3], land cover surveys, and urban planning [4], [5].”
Learning Student Networks via Feature Embedding (2018, background): “DEEP neural networks (DNNs) have provided state-of-theart performance in various fields, such as image classification [1], [2], semantic modeling [3], [4], visual quality evaluation [5], object detection [6], [7], and segmentation [8].”
Lightweight Pyramid Networks for Image Deraining (2018, background): “Convolutional neural networks (CNN) have proven useful for a variety of high-level vision tasks [16], [17], [18], [19], [20] as well as various image processing problems [21], [22], [23], [24], [25].”
Deep Hyperspectral Image Sharpening (2018, unknown): “successfully applied in many automatic control [17]–[20] and computer vision tasks [21]–[25].”