Region-Level SAR Image Segmentation Based on Edge Feature and Label Assistance

Shang, Ronghua; Liu, Mengmeng; Jiao, Licheng; Feng, Jie; Li, Yangyang

doi:10.1109/tgrs.2022.3217053

Cited by 14 publications

(6 citation statements)

References 38 publications

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“…Furthermore, SAR benefits from the unique ability to penetrate challenging environments for imaging and to continuously acquire geographical information even in complex weather conditions. These advantages make SAR valuable for applications in various fields, such as target detection and recognition [5] [6], geomorphology and terrain mapping [7] and segmentation and classification [8]- [10].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Multiscale Reversible Column Network for SAR Ship Detection

Wang,

Zeng

2024

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

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Ship detection via Synthetic Aperture Radar(SAR) is widely used in maritime safety and maritime traffic control, et. al. Recently, deep learning methods are employed to improve the performance of SAR ship detection to a large extent. However, the presence of clutter in SAR images and the large scale difference of ships result in the diminished detection performance in complex environments. As such, in this paper, a novel Adaptive Multi-Scale Reversible Column Network is proposed. Firstly, the idea of disentangled feature learning is applied to construct Reversible Column Networks with C2f module to alleviate the problem of large scale difference and the loss of ship information. Secondly, the multiplexed adaptive spatial pyramid pooling module is proposed to alleviate the impact of complex background clutter through multiplexed pooling operations and adaptive fusion. Finally, a novel feature pyramid network with adaptive downsampling module is designed to reduce the information loss caused by downsampling while enhancing the multi-scale ship detection capability. The effectiveness of the proposed method is validated on two public datasets: SSDD and HRSID. The experimental results show that the proposed method is ablet o achieve better results than current state-of-the-art methods for SAR ship detection in complex environments and large scale difference of ships.

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Section: Introductionmentioning

confidence: 99%

Adaptive Multiscale Reversible Column Network for SAR Ship Detection

Wang,

Zeng

2024

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

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“…In the past, traditional image processing methods enabled automatic extraction of buildings from remote sensing images. Building feature extraction predominantly relied on traditional feature extraction algorithms, including corner detection operator [3], edge detection operator [4], image transform [5], and histogram [6]. Some researchers have applied active contour region segmentation methods [7,8] to construct building structural information and segment images into regions with similar and homogeneous properties.…”

Section: Introductionmentioning

confidence: 99%

MDBES-Net: Building Extraction From Remote Sensing Images Based on Multiscale Decoupled Body and Edge Supervision Network

Xu,

Du,

Meng

et al. 2024

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

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The extraction of buildings in aerial remote sensing applications is an important and challenging task. Most existing methods extract buildings based on local area attention, ignoring the loss of accuracy due to the global structure of the building. However, global structural features of buildings with strong coupling relationships in complex scenes are difficult to extract, such as the edges and bodies of buildings, leading to discontinuous results. Therefore, Multiscale Decoupled Body and Edge Supervision Network (MDBES-Net), which can consider both edge optimization and inner consistency, is proposed to solve these problems. MDBES-Net consists of the Body-Mask-Edge Consistency Constraint base network (BMECC), Decoupling the Body and Edge Aware module (DBEA), and the Channel Decoupled Attention module (CDA). First, Body-Mask-Edge consistency constraint supervision is established by body and edge labels to jointly improve the segmentation effect in the BMECC base network. Second, In the mutiscale DBEA module, building features are warped by a learnable flow field to make body parts more consistent and edges more detailed. Finally, the CDA module performs adaptive calibration of the re-coupled feature map channel response to minimize external background noise interference. Experiments on the open Massachusetts Building Dataset, WHU Building Dataset show that the proposed MDBES-Net can accurately extract buildings in complex scenarios, enabling complete building segmentation with refined boundaries and improved internal consistency.

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“…The conventional synthetic aperture radar (SAR) is capable of performing ground detection, which possesses the advantages such as full-time, all-weather, and long-range imaging. Unfortunately, the SAR has certain limitations in detecting moving targets [1][2][3][4][5]. Compared to the conventional SAR, the video SAR (ViSAR) can perform high-framerate imaging and monitoring of the target area, obtaining dynamic observation effects of the area [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

An Embedded-GPU-Based Scheme for Real-Time Imaging Processing of Unmanned Aerial Vehicle Borne Video Synthetic Aperture Radar

Yang,

Zhang,

et al. 2024

Remote Sensing

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The UAV-borne video SAR (ViSAR) imaging system requires miniaturization, low power consumption, high frame rates, and high-resolution real-time imaging. In order to satisfy the requirements of real-time imaging processing for the UAV-borne ViSAR under limited memory and parallel computing resources, this paper proposes a method of embedded GPU-based real-time imaging processing for the UAV-borne ViSAR. Based on a parallel programming model of the compute unified device architecture (CUDA), this paper designed a parallel computing method for range-Doppler (RD) and map drift (MD) algorithms. By utilizing the advantages of the embedded GPU characterized with parallel computing, we improved the processing speed of real-time ViSAR imaging. This paper also adopted a unified memory management method, which greatly reduces data replication and communication latency between the CPU and the GPU. The data processing of 2048 × 2048 points took only 1.215 s on the Jetson AGX Orin platform to form a nine-consecutive-frame image with a resolution of 0.15 m, with each frame taking only 0.135 s, enabling real-time imaging at a high frame rate of 5 Hz. In actual testing, continuous mapping can be achieved without losing the scenes, intuitively obtaining the dynamic observation effects of the area. The processing results of the measured data have verified the reliability and effectiveness of the proposed scheme, satisfying the processing requirements for real-time ViSAR imaging.

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Region-Level SAR Image Segmentation Based on Edge Feature and Label Assistance

Cited by 14 publications

References 38 publications

Adaptive Multiscale Reversible Column Network for SAR Ship Detection

Adaptive Multiscale Reversible Column Network for SAR Ship Detection

MDBES-Net: Building Extraction From Remote Sensing Images Based on Multiscale Decoupled Body and Edge Supervision Network

An Embedded-GPU-Based Scheme for Real-Time Imaging Processing of Unmanned Aerial Vehicle Borne Video Synthetic Aperture Radar

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