Robust Matching for SAR and Optical Images Using Multiscale Convolutional Gradient Features

Zhou, Liang; Ye, Yuanxin; Tang, Tengfeng; Nan, Ke; Qin, Yao

doi:10.1109/lgrs.2021.3105567

Cited by 69 publications

(32 citation statements)

References 15 publications

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“…Ye et al [28] constructed the Steerable Filters of first-and second-Order Channels (SFOC) to addressed the nonlinear radiometric differences. Zhou et al [29] extracted multiorientated gradient features to depict the structure properties of images, then the gradient feature maps are convolved in a multiscale manner, which produces the multiscale convolutional gradient features (MCGFs). The CNN method of feature detection is to directly transfer the satellite image into the neural network, and obtain the image depth feature through the network.…”

Section: B Image Feature Extraction Based On Deep Learningmentioning

confidence: 99%

“…For the learning method of feature matching, many other end-to-end image level learning-based registration methods are presented [26][27][28][29][30][31]. MatchNet is adopted to learn the descriptor and metric simultaneously [35].Wang et al [36] proposed a network that learns the mapping, and realizes the matching relationship between patch image pairs and matching labels through an end-to-end structure.…”

Section: B Image Feature Extraction Based On Deep Learningmentioning

confidence: 99%

See 1 more Smart Citation

SARPointNet: An Automated Feature Learning Framework for Spaceborne SAR Image Registration

Wang

Hao

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Accurate registration between SAR images is the basis for high-precision geometric correction of SAR images. The feature points extracted by conventional feature extraction methods is unsatisfactory, which is affected by imaging geometric characteristics and speckle noise of SAR images. This paper innovatively proposes a spaceborne SAR image feature learning framework to realize automatic sample generation and model training. It mainly includes two modules: The feature sample generation module based on the initial geometric information of spaceborne SAR. The initial RPC parameters of the spaceborne SAR are adopted to realize the initial positioning of SAR image, and a variety of feature extraction operators are used to match the overlapping areas to obtain high-precision matching points, which are employed as training samples for image-pairs; Pseudo-Siamese feature learning network SARPointNet for SAR image feature learning. The pseudo-siamese network is used to extract the feature points and descriptors of the sample image-pairs. The feature optimization process is realized through the descriptor constraints between image pairs, which promotes the network to improve the accuracy of feature extraction. The proposed method has been tested in mountain, hilly, flatland and urban scenarios, respectively. The results demonstrate that the correspondence points extracted by SARPointNet are evenly distributed, large quantities (at least 10 times that of other methods) and achieve high precision (The root mean square error is less than 1 pixels), which shows great advantages over other methods.

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Section: B Image Feature Extraction Based On Deep Learningmentioning

confidence: 99%

Section: B Image Feature Extraction Based On Deep Learningmentioning

confidence: 99%

SARPointNet: An Automated Feature Learning Framework for Spaceborne SAR Image Registration

Wang

Hao

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Further still, traditional detection methods often require the designing of different detection schemes for different datasets because of a lack 2 of 18 in learning ability. For the past few years, deep learning has achieved remarkable results in the fields of image matching [8,9], image fusion [10], and object detection [11]. Recently, deep-learning-based object detection methods have made a huge breakthrough.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Attention Fusion Mechanism Convolutional Network for Object Detection in Remote Sensing Images

Ren

Zhu

et al. 2022

Remote Sensing

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For remote sensing object detection, fusing the optimal feature information automatically and overcoming the sensitivity to adapt multi-scale objects remains a significant challenge for the existing convolutional neural networks. Given this, we develop a convolutional network model with an adaptive attention fusion mechanism (AAFM). The model is proposed based on the backbone network of EfficientDet. Firstly, according to the characteristics of object distribution in datasets, the stitcher is applied to make one image containing objects of various scales. Such a process can effectively balance the proportion of multi-scale objects and handle the scale-variable properties. In addition, inspired by channel attention, a spatial attention model is also introduced in the construction of the adaptive attention fusion mechanism. In this mechanism, the semantic information of the different feature maps is obtained via convolution and different pooling operations. Then, the parallel spatial and channel attention are fused in the optimal proportions by the fusion factors to get the further representative feature information. Finally, the Complete Intersection over Union (CIoU) loss is used to make the bounding box better cover the ground truth. The experimental results of the optical image dataset DIOR demonstrate that, compared with state-of-the-art detectors such as the Single Shot multibox Detector (SSD), You Only Look Once (YOLO) v4, and EfficientDet, the proposed module improves accuracy and has stronger robustness.

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“…In recent years, with its unique deep feature expression capacity, deep learning has provided new ideas for remote sensing image processing including semantic segmentation [12,13], object detection [14,15], image matching [16,17], etc. Many remote sensing image change detection methods based on deep learning have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Feature Decomposition-Optimization-Reorganization Network for Building Change Detection in Remote Sensing Images

Zhou

Zhu

et al. 2022

Remote Sensing

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Building change detection plays an imperative role in urban construction and development. Although the deep neural network has achieved tremendous success in remote sensing image building change detection, it is still fraught with the problem of generating broken detection boundaries and separation of dense buildings, which tends to produce saw-tooth boundaries. In this work, we propose a feature decomposition-optimization-reorganization network for building change detection. The main contribution of the proposed network is that it performs change detection by respectively modeling the main body and edge features of buildings, which is based on the characteristics that the similarity between the main body pixels is strong but weak between the edge pixels. Firstly, we employ a siamese ResNet structure to extract dual-temporal multi-scale difference features on the original remote sensing images. Subsequently, a flow field is built to separate the main body and edge features. Thereafter, a feature optimization module is designed to refine the main body and edge features using the main body and edge ground truth. Finally, we reorganize the optimized main body and edge features to obtain the output results. These constitute a complete end-to-end building change detection framework. The publicly available building dataset LEVIR-CD is employed to evaluate the change detection performance of our network. The experimental results show that the proposed method can accurately identify the boundaries of changed buildings, and obtain better results compared with the current state-of-the-art methods based on the U-Net structure or by combining spatial-temporal attention mechanisms.

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Robust Matching for SAR and Optical Images Using Multiscale Convolutional Gradient Features

Cited by 69 publications

References 15 publications

SARPointNet: An Automated Feature Learning Framework for Spaceborne SAR Image Registration

SARPointNet: An Automated Feature Learning Framework for Spaceborne SAR Image Registration

An Adaptive Attention Fusion Mechanism Convolutional Network for Object Detection in Remote Sensing Images

Feature Decomposition-Optimization-Reorganization Network for Building Change Detection in Remote Sensing Images

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