A Novel Deep Structure U-Net for Sea-Land Segmentation in Remote Sensing Images

Shamsolmoali, Pourya; Zareapoor, Masoumeh; Wang, Ruili; Zhou, Huiyu; Yang, Jie

doi:10.1109/jstars.2019.2925841

Cited by 101 publications

(50 citation statements)

References 58 publications

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“…the SF method [6] and the change detection (CD) method [10], are employed. Meanwhile, the JHK U-net architecture in [22] and a recently proposed residual dense U-net model in [21] (abbreviated as RU-net in the following) are also used for comparison. The single-channel input of the two U-net network is the absolute value of the degraded SAR image.…”

Section: Log 10mentioning

confidence: 99%

“…Recently, the data-driven deep-learning methods, which are realized by learning from the training data through the deep neural network (DNN), have shown their superority over the stat-of-the-art model-driven methods in many SAR research fileds, such as SAR imaging [15], SAR target recognition [16]- [18], SAR image segmentation [19]- [21] and so on. However, to the authors' knowledge, no related work about the research of the deep learning in the restoration of the SAR spectrum aliasing problem has been reported yet.…”

Section: Introductionmentioning

confidence: 99%

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SAR Image Restoration From Spectrum Aliasing by Deep Learning

Liu

Liao

2020

IEEE Access

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The diversity and the space-variance of the spectrum-aliasing effects in SAR bring challenges to the available model-driven restoration methods. In this paper, a hybrid data-driven and model-driven deep learning scheme is innovatively proposed to deal with this problem. In this scheme, the SAR image restoration is realize via a U-shaped deep neural network (DNN). Meanwhile, the model of the SAR echo data is used to generate the training data to learn the weighting parameters of the network. The DNN in this method is designed to suit the SAR spectrum-aliasing application. Due to the general SAR echo mathematical model is employed for training data construction, the spectrum aliasing features in different SAR systems can be accommodated and learned. Hence, the proposed method can work well for various SAR configurations. Simulation results using the real Radarsat-1 data as well as the synthetic data prove the effectiveness as well as the robustness of the proposed method.

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Section: Log 10mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SAR Image Restoration From Spectrum Aliasing by Deep Learning

Liu

Liao

2020

IEEE Access

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“…Therefore, an algorithm that directly detects TCs from the data or image instead of using the "center" is necessary. The successful applications of remote sensing image object detection [27]- [29] inspire us to introduce deep learning into the TC object detection.…”

Section: Introductionmentioning

confidence: 99%

A Center Location Algorithm for Tropical Cyclone in Satellite Infrared Images

Wang

Yuan

Wang

2020

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

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The precise location of the tropical cyclone (TC) center is critical for intensity estimation and trajectory prediction. Due to the variability of TC morphology and structure, there are still some challenges in locating its center automatically. The ability of the deep convolutional network to capture multilevel structural features of the images is exploited. Furthermore, a two-step scheme for locating the TC center is proposed, which contains the object detection for TCs with deep learning and the comprehensive decision for TC centers. In the object detection, considering the statistical scale distribution of TCs, the global and local features extracted by the network are combined to form the fusion feature maps through the upsampling and concatenation. The changes in the TC scale are accommodated by two different scale outputs. A high detection rate and a low false alarm rate are obtained with the object detection, which provides an initial position for the TC center. Within the scope of the TCs, the final position of the center is obtained through segmentation, edge detection, circle fitting, and comprehensive decision. The experimental results show that the average latitude and longitude error of the proposed method is about 0.237 •. For the TC in the initial phase or dissipation stage, the location results are usually superior to the results of the comparison algorithms.

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“…At present, this method has been applied to many different tasks and also achieved excellent results, such as image segmentation and image conversion. [27,28].…”

Section: Introductionmentioning

confidence: 99%

DBT Masses Automatic Segmentation Using U-Net Neural Networks

Lai

Yang

2020

Computational and Mathematical Methods in Medicine

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To improve the automatic segmentation accuracy of breast masses in digital breast tomosynthesis (DBT) images, we propose a DBT mass automatic segmentation algorithm by using a U-Net architecture. Firstly, to suppress the background tissue noise and enhance the contrast of the mass candidate regions, after the top-hat transform of DBT images, a constraint matrix is constructed and multiplied with the DBT image. Secondly, an efficient U-Net neural network is built and image patches are extracted before data augmentation to establish the training dataset to train the U-Net model. And then the presegmentation of the DBT tumors is implemented, which initially classifies per pixel into two different types of labels. Finally, all regions smaller than 50 voxels considered as false positives are removed, and the median filter smoothes the mass boundaries to obtain the final segmentation results. The proposed method can effectively improve the performance in the automatic segmentation of the masses in DBT images. Using the detection Accuracy (Acc), Sensitivity (Sen), Specificity (Spe), and area under the curve (AUC) as evaluation indexes, the Acc, Sen, Spe, and AUC for DBT mass segmentation in the entire experimental dataset is 0.871, 0.869, 0.882, and 0.859, respectively. Our proposed U-Net-based DBT mass automatic segmentation system obtains promising results, which is superior to some classical architectures, and may be expected to have clinical application prospects.

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A Novel Deep Structure U-Net for Sea-Land Segmentation in Remote Sensing Images

Cited by 101 publications

References 58 publications

SAR Image Restoration From Spectrum Aliasing by Deep Learning

SAR Image Restoration From Spectrum Aliasing by Deep Learning

A Center Location Algorithm for Tropical Cyclone in Satellite Infrared Images

DBT Masses Automatic Segmentation Using U-Net Neural Networks

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