A Novel Method for Layover Detection in Mountainous Areas with SAR Images

Wu, Lin; Wang, Hongxia; Li, Yuan; Guo, Zhengwei; Li, Ning

doi:10.3390/rs13234882

Cited by 10 publications

(3 citation statements)

References 35 publications

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“…In recent decades, many scholars have explored methods for detecting layover and shadow areas, including the wavenumber shift method [9], the threshold segmentation method [10,11], the edge sharpening method [12], geometric models combined with the morphological method [13], and the multi-stages detection method [14]. These methods are designed to identify layover and shadow areas, followed by masking, and aim to prevent or minimize errors in the geocoding or interpretation of SAR images.…”

Section: Introductionmentioning

confidence: 99%

Multi-View Data-Based Layover Information Compensation Method for SAR Image Mosaic

Liu,

Wang,

Jiao

et al. 2024

Remote Sensing

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Currently, massive Synthetic Aperture Radar (SAR) images acquired from numerous SAR satellites have been widely utilized in various fields, and image mosaicking technology provides important support and assistance for these applications. The traditional mosaic method selects specific SAR images that can cover the region of interest (ROI) from redundant data to produce “One Map”. However, an SAR image suffers from severe geometric distortion, especially in mountainous areas, which inevitably reduces the utilization of mosaic image. Therefore, a multi-view data-based layover information compensation (MDLIC) method for SAR image mosaic is proposed, aiming to take full advantage of multi-view data to compensate for the missing information in the layover area of the SAR image. This is performed to improve the information content of the mosaic image and realize efficient thematic information extraction and analysis. First, the calculation of the object-space extent of all images and the division of object-space grid are completed on the basis of geometric and radiometric preprocessing. Then, according to the transformation relationship between the object-space and the image-space, the sampling rate image of each image corresponding to the object-space grid is generated, which determines the layover area and the layover degree in each image. Finally, the information compensation strategy is implemented in accordance with the sampling rate image to realize the compensation of the layover information. The feasibility and effectiveness of the MDLIC method are verified by using multiple SAR images from the Chinese Gaofen-3 01 satellite as datasets for experiments. The experimental results indicate that the MDLIC method can obtain mosaic images with richer information compared with the traditional method, while still providing satisfactory results.

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Section: Introductionmentioning

confidence: 99%

Multi-View Data-Based Layover Information Compensation Method for SAR Image Mosaic

Liu,

Wang,

Jiao

et al. 2024

Remote Sensing

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“…Therefore, SAR images have been widely used in mapping, geology and other fields [1]. Due to the side-viewing and range imaging characteristics, geometric distortions such as layover always exist in SAR digital ortho maps (DOMs) after ortho-rectification [2,3]. These distortions degenerate the structural information and bring difficulties for target recognition and interpretation [4].…”

Section: Introductionmentioning

confidence: 99%

SAR True Digital Ortho Map Production for Target Geometric Structure Preservation

Jiao,

Wang,

et al. 2023

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

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Geometric structural information of objects in SAR images is of great significance in target detection and analysis. Existed orthorectification methods of SAR images generally follow the traditional routes of optical images, resulting in suffering from the influence of layover and bring difficulties in image analysis or target extraction without sufficient prior knowledge. Therefore, this paper proposes a highest projection principle based SAR true digital ortho maps (TDOM) production method for the restoration of geometric structural information of targets. First, ground control information extracted from the high precision digital surface model (DSM) is applied to calibrate the geolocation error of original SAR images. Then, the highest projection principle and the visibility detection is utilized to reduce the influence of layover. To preserve the structural information of targets, multi-view SAR TDOMs are integrated and compensated to generate a fused SAR TDOM image similar as opticla-view. Two Gaofen-3 (GF-3) SAR Spotlight images are included to verify the performance of our proposed method. Experimental results are compared qualitatively in visual effects and quantitatively in geolocation accuracy. The results indicate that the produced SAR TDOMs can preserve the structural information of targets and provide more similar geometric structural information as optical images, which will effectively improve the efficiency in interpretation of ortho-rectified SAR images. Index Terms-Synthetic aperture radar, true digital ortho map (TDOM), layover area, geometric structural information.

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“…They can directly degrade the performance of SAR image interpretation, especially in the task of weak and small target detection. Many scholars have studied the detection of shadow and layover, such as coherence coefficient maps [6] , the threshold segmentation method based on intensity maps [7], the edge sharpening method using image filtering [8] and multi-stages layover detection method [9]. However, all of these methods have their limitations.…”

Section: Introductionmentioning

confidence: 99%

A Novel Shadow and Layover Segmentation Network for Multi-Angle SAR Images Fusion

Zhang

Yin

et al. 2022

IEEE Access

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Shadow and layover are geometric distortion phenomenons in side-view imaging synthetic aperture radar (SAR) systems, especially in mountainous areas and densely populated urban areas. The shadow can block the target of the observation area, making it impossible to obtain the scattering characteristics of the target. The layover causes phase distortion and alters target characteristics. Shadow and layover severely hinder the interpretation of SAR images. To confront the above problems, a multi-angle fusion algorithm based on unsupervised progressive segmentation network is proposed. Firstly, inspired by mega-constellations of low earth orbit, a spaceborne SAR collaborative observation model is proposed to generate multi-angle images of fluctuant terrain. Secondly, according to the difference of echos in the shadow and layover regions, an unsupervised progressive segmentation network is designed to sequentially segment the shadow and layover regions. Finally, to improve the contrast and brightness of the fused SAR image, a single-scale weighted fusion algorithm is designed. Experiments were conducted using the simulated multi-angle SAR images. Compared with single-angle images, the accuracy of target detection and figure-of-merit of the fused SAR image are significantly higher than those of other methods.INDEX TERMS Shadow and layover, synthetic aperture radar, unsupervised progressive segmentation network, multi-angle fusion.

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A Novel Method for Layover Detection in Mountainous Areas with SAR Images

Cited by 10 publications

References 35 publications

Multi-View Data-Based Layover Information Compensation Method for SAR Image Mosaic

Multi-View Data-Based Layover Information Compensation Method for SAR Image Mosaic

SAR True Digital Ortho Map Production for Target Geometric Structure Preservation

A Novel Shadow and Layover Segmentation Network for Multi-Angle SAR Images Fusion

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