SAR Image Segmentation Using Region Smoothing and Label Correction

Shang, Ronghua; Lin, Junkai; Jiao, Licheng; Li, Yangyang

doi:10.3390/rs12050803

Cited by 28 publications

(22 citation statements)

References 32 publications

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“…This poses another issue of noisy or wrong labels, which leads to a second potential direction, label refinement. Recent studies have highlighted the needs of label refinement in the presence of noisy or wrong labels in large-scale datasets [36], or in remote sensing data [37]. From our studies, geolocation features alone already inform substantial prior knowledge about the damage likelihood.…”

Section: Discussionmentioning

confidence: 72%

Post-Hurricane Damage Assessment Using Satellite Imagery and Geolocation Features

Cao,

Choe

2020

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Gaining timely and reliable situation awareness after hazard events such as a hurricane is crucial to emergency managers and first responders. One effective way to achieve that goal is through damage assessment. Recently, disaster researchers have been utilizing imagery captured through satellites or drones to quantify the number of flooded/damaged buildings. In this paper, we propose a mixed data approach, which leverages publicly available satellite imagery and geolocation features of the affected area to identify damaged buildings after a hurricane. The method demonstrated significant improvement from performing a similar task using only imagery features, based on a case study of Hurricane Harvey affecting Greater Houston area in 2017. This result opens door to a wide range of possibilities to unify the advancement in computer vision algorithms such as convolutional neural networks and traditional methods in damage assessment, for example, using flood depth or bare-earth topology. In this work, a creative choice of the geolocation features was made to provide extra information to the imagery features, but it is up to the users to decide which other features can be included to model the physical behavior of the events, depending on their domain knowledge and the type of disaster. The dataset curated in this work is made openly available

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Section: Discussionmentioning

confidence: 72%

Post-Hurricane Damage Assessment Using Satellite Imagery and Geolocation Features

Cao,

Choe

2020

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“…The robust K-means algorithm [53] is used to classify Φ(i) into two categories, one of which indicates the presence of RFI and the other indicates the absence. The operation of threshold segmentation can be expressed as…”

Section: Rfi Detection Based On Kurtosismentioning

confidence: 99%

An Improved RFI Mitigation Approach for SAR Based on Low-Rank Sparse Decomposition: From the Perspective of Useful Signal Protection

Zhang

Lin

et al. 2022

Remote Sensing

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As an open system, synthetic aperture radar (SAR) inevitably receives radio frequency interference (RFI) generated by electromagnetic equipment in the same band. The existence of RFI seriously affects SAR signal processing and image interpretation. In recent years, many algorithms and models related to RFI mitigation have been proposed. However, most of that focus on effectively mitigating the RFI is insufficient to protect the useful signals. This article proposes a mitigation method of RFI with a signal-protected capability. (1) The kurtosis coefficient is used to detect RFI pulse-by-pulse, and the echoes containing RFI are stored in matrix form. (2) The preliminary extraction of RFI is complete by low-rank sparse decomposition of the echo matrix containing RFI. (3) For the secondary separation of RFI, the accurate position of RFI in the preliminary extraction results is located by the fuzzy C-means clustering; then, we separate the RFI and the remaining useful signals again and reconstruct the useful signals to complete the mitigation work. The proposed method can further protect useful signals while effectively removing interference through the secondary separation of RFI. Experimental results based on simulated and measured data verify the performance and potential of the proposed method.

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“…A larger λ indicates that d 2 ij is more significant than d 1 ij . Usually, the value of λ is chosen in [2,6]. Figure 4 intuitively shows the similarity measurement.…”

Section: Nislicmentioning

confidence: 99%

“…Synthetic aperture radar (SAR) is a widely used remote sensing imaging system, which can produce high-resolution images and work under all time and all-weather conditions [1]. SAR images have found wide applications in resources, environment, archeology, military and so on [2]. SAR image have a variety of landforms, such as rivers, crops and residential areas.…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation for SAR Image Based on Texture Complexity Analysis and Key Superpixels

et al. 2020

Self Cite

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In recent years, regional algorithms have shown great potential in the field of synthetic aperture radar (SAR) image segmentation. However, SAR images have a variety of landforms and a landform with complex texture is difficult to be divided as a whole. Due to speckle noise, traditional over-segmentation algorithm may cause mixed superpixels with different labels. They are usually located adjacent to two areas or contain more noise. In this paper, a new semantic segmentation method of SAR images based on texture complexity analysis and key superpixels is proposed. Texture complexity analysis is performed and on this basis, mixed superpixels are selected as key superpixels. Specifically, the texture complexity of the input image is calculated by a new method. Then a new superpixels generation method called neighbourhood information simple linear iterative clustering (NISLIC) is used to over-segment the image. For images with high texture complexity, the complex areas are first separated and key superpixels are selected according to certain rules. For images with low texture complexity, key superpixels are directly extracted. Finally, the superpixels are pre-segmented by fuzzy clustering based on the extracted features and the key superpixels are processed at the pixel level to obtain the final result. The effectiveness of this method has been successfully verified on several kinds of images. Comparing with the state-of-the-art algorithms, the proposed algorithm can more effectively distinguish different landforms and suppress the influence of noise, so as to achieve semantic segmentation of SAR images.

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SAR Image Segmentation Using Region Smoothing and Label Correction

Cited by 28 publications

References 32 publications

Post-Hurricane Damage Assessment Using Satellite Imagery and Geolocation Features

Post-Hurricane Damage Assessment Using Satellite Imagery and Geolocation Features

An Improved RFI Mitigation Approach for SAR Based on Low-Rank Sparse Decomposition: From the Perspective of Useful Signal Protection

Semantic Segmentation for SAR Image Based on Texture Complexity Analysis and Key Superpixels

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