SAR Sea Ice Image Segmentation Using Watershed with Intensity-Based Region Merging

Ijitona, Tolulope; Ren, Jinchang; Hwang, Byongjun

doi:10.1109/cit.2014.19

Cited by 17 publications

(10 citation statements)

References 12 publications

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“…Over-segmentation problems; sensitive to the noise [29,30] Object-oriented Object-oriented segmentation Fully express the semantic information between objects, and extracts feature information at different scales Manually establishes optimal segmentation scale and appropriate classification rules [31,32] Deep learning U-Net-based deep learning Independent of auxiliary data and has a high degree of automation…”

Section: Fast and Parallel Computing Produces Full Boundariesmentioning

confidence: 99%

“…Superpixel segmentation can effectively preserve the statistical characteristics of the image and segment image into many small homogeneous regions with similar sizes or with different scales [27,28]. To deal with the over-segmentation problems, solutions were based on a morphological approach that combined the watershed segmentation with average contrast maximisation [29,30]. Meanwhile, some object-oriented classification methods have also been developed to map glacial lakes [31,32].…”

Section: Introductionmentioning

confidence: 99%

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Comparing Methods for Segmenting Supra-Glacial Lakes and Surface Features in the Mount Everest Region of the Himalayas Using Chinese GaoFen-3 SAR Images

Chen

2021

Remote Sensing

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Glaciers and numerous glacial lakes that are produced by glacier melting are key indicators of climate change. Often overlooked, supra-glacial lakes develop in the melting area in the low-lying part of a glacier and appear to be highly variable in their size, shape, and location. The lifespan of these lakes is thought to be quite transient, since the lakes may be completely filled by water and burst out within several weeks. Changes in supra-glacial lake outlines and other surface features such as supra-glacial rivers and crevasses on the glaciers are useful indicators for the direct monitoring of glacier changes. Synthetic aperture radar (SAR) is not affected by weather and climate, and is an effective tool for study of glaciated areas. The development of the Chinese GaoFen-3 (GF-3) SAR, which has high spatial and temporal resolution and high-precision observation performance, has made it possible to obtain dynamic information about glaciers in more detail. In this paper, the classical Canny operator, the variational B-spline level-set method, and U-Net-based deep-learning model were applied and compared to extract glacial lake outlines and other surface features using different modes and Chinese GF-3 SAR imagery in the Mount Everest Region of the Himalayas. Particularly, the U-Net-based deep-learning method, which was independent of auxiliary data and had a high degree of automation, was used for the first time in this context. The experimental results showed that the U-Net-based deep-learning model worked best in the segmentation of supra-glacial lakes in terms of accuracy (Precision = 98.45% and Recall = 95.82%) and segmentation efficiency, and was good at detecting small, elongated, and ice-covered supra-glacial lakes. We also found that it was useful for accurately identifying the location of supra-glacial streams and ice crevasses on glaciers, and quantifying their width. Finally, based on the time series of the mapping results, the spatial characteristics and temporal evolution of these features over the glaciers were comprehensively analyzed. Overall, this study presents a novel approach to improve the detection accuracy of glacier elements that could be leveraged for dynamic monitoring in future research.

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Section: Fast and Parallel Computing Produces Full Boundariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparing Methods for Segmenting Supra-Glacial Lakes and Surface Features in the Mount Everest Region of the Himalayas Using Chinese GaoFen-3 SAR Images

Chen

2021

Remote Sensing

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“…These studies cover ice-water segmentation [25][26][27], ice concentration estimation [28][29][30], ice thickness estimation [31,32], ice type classification [33,34], and sea ice feature detection [35,36]. Methods using superpixel segmentation [37,38], watershed segmentation [39,40], and active contours [41,42] have been actively employed in SAR image segmentation. There are several related studies in the area of ship detection in ice-covered waters [43][44][45].…”

Section: Introductionmentioning

confidence: 99%

RUF: Effective Sea Ice Floe Segmentation Using End-to-End RES-UNET-CRF with Dual Loss

et al. 2021

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Sea ice observations through satellite imaging have led to advancements in environmental research, ship navigation, and ice hazard forecasting in cold regions. Machine learning and, recently, deep learning techniques are being explored by various researchers to process vast amounts of Synthetic Aperture Radar (SAR) data for detecting potential hazards in navigational routes. Detection of hazards such as sea ice floes in Marginal Ice Zones (MIZs) is quite challenging as the floes are often embedded in a multiscale ice cover composed of ice filaments and eddies in addition to floes. This study proposes a segmentation model tailored for detecting ice floes in SAR images. The model exploits the advantages of both convolutional neural networks and convolutional conditional random field (Conv-CRF) in a combined manner. The residual UNET (RES-UNET) computes expressive features to generate coarse segmentation maps while the Conv-CRF exploits the spatial co-occurrence pairwise potentials along with the RES-UNET unary/segmentation maps to generate final predictions. The whole pipeline is trained end-to-end using a dual loss function. This dual loss function is composed of a weighted average of binary cross entropy and soft dice loss. The comparison of experimental results with the conventional segmentation networks such as UNET, DeepLabV3, and FCN-8 demonstrates the effectiveness of the proposed architecture.

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“…Early attempts include grey level co-occurrence probability texture features [5], function-based Markov Random Field Model algorithm [6] and k-means clustering [7]. In addition, region based segmentation is also focused, which include watershed and iterative region growing with semantics [8], incidence angle effect correction and region merging [1], and watershed with intensity-based region merging method [9] et. al.…”

Section: Introductionmentioning

confidence: 99%

Texture-Sensitive Superpixeling and Adaptive Thresholding for Effective Segmentation of Sea Ice Floes in High-Resolution Optical Images

Chai

Ren

Hwang

et al. 2021

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

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Efficient and accurate segmentation of sea ice floes from high-resolution optical (HRO) remote sensing images is crucial for understanding of sea ice evolutions and climate changes, especially in coping with the large data volume. Existing methods suffer from noise interference and mixture of water and ice caused high segmentation error and less robustness. In this study, we propose a novel sea ice floe segmentation algorithm from HRO images based on texture-sensitive superpixeling and twostage thresholding. First, sparse components are extracted from the HRO images using the Robust Principal Component Analysis (RPCA), and noise is removed by the bilateral filter. The enhanced image is obtained by combining the low-rank matrix and the sparse components. Second, a texture-sensitive Simple Linear Iterative Clustering (SLIC) superpixel algorithm is introduced for pre-segmentation of the enhanced HRO image. Third, a learning based adaptive thresholding in the two-stages is employed to generate the refined segmentation from the derived superpixels blocks. The efficacy of the proposed method is validated on two HRO images using visual assessment, quantitative evaluation (with seven metrics) and histogram comparison. The superior performance of the proposed method has demonstrated its efficacy for sea ice floe segmentation.

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SAR Sea Ice Image Segmentation Using Watershed with Intensity-Based Region Merging

Cited by 17 publications

References 12 publications

Comparing Methods for Segmenting Supra-Glacial Lakes and Surface Features in the Mount Everest Region of the Himalayas Using Chinese GaoFen-3 SAR Images

Comparing Methods for Segmenting Supra-Glacial Lakes and Surface Features in the Mount Everest Region of the Himalayas Using Chinese GaoFen-3 SAR Images

RUF: Effective Sea Ice Floe Segmentation Using End-to-End RES-UNET-CRF with Dual Loss

Texture-Sensitive Superpixeling and Adaptive Thresholding for Effective Segmentation of Sea Ice Floes in High-Resolution Optical Images

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