ABSTRACT This letter presents a supervised classification method for synthetic aperture radar (SA... more ABSTRACT This letter presents a supervised classification method for synthetic aperture radar (SAR) images based on multiscale region connection calculus (RCC) and conditional random fields (CRF). Using this method, first, a SAR image is oversegmented into multisuperpixels via the image pyramid. We then use the multiscale RCC model to describe the spatial logic relationships among these superpixels. To complete the process, multiscale RCC relationships are learned and reasoned under the CRF reasoning framework. This method employs iteration strategy for CRF reasoning to get better details in the classification results as well. We illustrate the proposed method by experiments conducted on DLR ESAR image. The results reveal efficient performance.
Detection of coastline in synthetic aperture radars (SARs) is difficult due to the presence of sp... more Detection of coastline in synthetic aperture radars (SARs) is difficult due to the presence of speckle effect and strong signal return from wind-roughened, wave-modulated sea. This paper presents a new approach to detect coastlines from SAR images by integrating watershed transformation and gradient vector flow (GVF) snake model. Several improvements have been made to improve the accuracy and efficiency of coastline detection. First, ratio of averages edge detector is used to produce gradient maps suitable for watershed transformation. Second, an improved GVF snake model is presented, which exploits two external constraint forces to make the curve evolution more controllable. We name it controllable GVF (CGVF) snake model. Third, a coarse-fine processing scheme is employed, in which watershed transformation is performed on a coarse-resolution image to obtain the initial contours for CGVF snake model, and then CGVF snake model is used to refine the roughly detected coastline at fine resolution. Experimental results on Envisat-ASAR and TerraSAR-X images show that with only a modest computational burden, the new approach produces a good match between the detected coastline and the true one.
ABSTRACT This letter presents a supervised classification method for synthetic aperture radar (SA... more ABSTRACT This letter presents a supervised classification method for synthetic aperture radar (SAR) images based on multiscale region connection calculus (RCC) and conditional random fields (CRF). Using this method, first, a SAR image is oversegmented into multisuperpixels via the image pyramid. We then use the multiscale RCC model to describe the spatial logic relationships among these superpixels. To complete the process, multiscale RCC relationships are learned and reasoned under the CRF reasoning framework. This method employs iteration strategy for CRF reasoning to get better details in the classification results as well. We illustrate the proposed method by experiments conducted on DLR ESAR image. The results reveal efficient performance.
Detection of coastline in synthetic aperture radars (SARs) is difficult due to the presence of sp... more Detection of coastline in synthetic aperture radars (SARs) is difficult due to the presence of speckle effect and strong signal return from wind-roughened, wave-modulated sea. This paper presents a new approach to detect coastlines from SAR images by integrating watershed transformation and gradient vector flow (GVF) snake model. Several improvements have been made to improve the accuracy and efficiency of coastline detection. First, ratio of averages edge detector is used to produce gradient maps suitable for watershed transformation. Second, an improved GVF snake model is presented, which exploits two external constraint forces to make the curve evolution more controllable. We name it controllable GVF (CGVF) snake model. Third, a coarse-fine processing scheme is employed, in which watershed transformation is performed on a coarse-resolution image to obtain the initial contours for CGVF snake model, and then CGVF snake model is used to refine the roughly detected coastline at fine resolution. Experimental results on Envisat-ASAR and TerraSAR-X images show that with only a modest computational burden, the new approach produces a good match between the detected coastline and the true one.
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Papers by Xinping Deng