Marine oil spills are among the most significant sources of marine pollution.Synthetic aperture radar(SAR)has been used to improve oil spill observations because of its advantages in oil spill detection and identifica...Marine oil spills are among the most significant sources of marine pollution.Synthetic aperture radar(SAR)has been used to improve oil spill observations because of its advantages in oil spill detection and identification.However,speckle noise,weak boundaries,and intensity inhomogeneity often exist in the oil spill regions of SAR imagery,which will seriously aff ect the accurate identification of oil spills.To enhance marine oil spill segmentation of SAR images,a fast,edge-preserving framework based on the distance-regularized level set evolution(DRLSE)model was proposed.Specifically,a bilateral filter penalty term is designed and incorporated into the DRLSE energy function(BF-DRLSE)to preserve the edges of oil spills,and an adaptive initial box boundary was selected for the DRLSE model to reduce the operation time complexity.Two sets of RadarSat-2 SAR data were used to test the proposed method.The experimental results indicate that the bilateral filtering scheme incorporated into the energy function during level set evolution improved the stability of level set evolution.Compared with other methods,the proposed improved BF-DRLSE algorithm displayed a higher overall segmentation accuracy(97.83%).In addition,using an appropriate initial box boundary for the DRLSE method accelerated the global search process,improved the accuracy of oil spill segmentation,and reduced computational time.Therefore,the results suggest that the proposed framework is eff ective and applicable for marine oil spill segmentation.展开更多
A method to detect airports in large space-borne synthetic aperture radar（SAR） imagery is studied.First,the large SAR imagery is segmented according to amplitude characteristics using maximum a posteriori（MAP） est...A method to detect airports in large space-borne synthetic aperture radar（SAR） imagery is studied.First,the large SAR imagery is segmented according to amplitude characteristics using maximum a posteriori（MAP） estimator based on the heavytailed Rayleigh model.The attention is then paid on the object of interest（OOI） extracted from the large images.The minimumarea enclosing rectangle（MER） of OOI is created via a rotating calipers algorithm.The projection histogram（PH） of MER for OOI is then computed and the scale and rotation invariant feature for OOI are extracted from the statistical characteristics of PH.A support vector machine（SVM） classifier is trained using those feature parameters and the airport is detected by the SVM classifier and Hough transform.The application in space-borne SAR images demonstrates the effectiveness of the proposed method.展开更多
Segmentation is the key step in auto-interpretation of high-resolution spaceborne synthetic aperture radar（SAR） images. A novel method is proposed based on integrating the geometric active contour（GAC） and the sup...Segmentation is the key step in auto-interpretation of high-resolution spaceborne synthetic aperture radar（SAR） images. A novel method is proposed based on integrating the geometric active contour（GAC） and the support vector machine（SVM）models. First, the images are segmented by using SVM and textural statistics. A likelihood measurement for every pixel is derived by using the initial segmentation. The Chan-Vese model then is modified by adding two items： the likelihood and the distance between the initial segmentation and the evolving contour. Experimental results using real SAR images demonstrate the good performance of the proposed method compared to several classic GAC models.展开更多
基金Supported by the National Key R&D Program of China(No.2017YFC1405600)the National Natural Science Foundation of China(Nos.41776182,42076182)the Natural Science Foundation of Shandong Province(No.ZR2016DM16)。
文摘Marine oil spills are among the most significant sources of marine pollution.Synthetic aperture radar(SAR)has been used to improve oil spill observations because of its advantages in oil spill detection and identification.However,speckle noise,weak boundaries,and intensity inhomogeneity often exist in the oil spill regions of SAR imagery,which will seriously aff ect the accurate identification of oil spills.To enhance marine oil spill segmentation of SAR images,a fast,edge-preserving framework based on the distance-regularized level set evolution(DRLSE)model was proposed.Specifically,a bilateral filter penalty term is designed and incorporated into the DRLSE energy function(BF-DRLSE)to preserve the edges of oil spills,and an adaptive initial box boundary was selected for the DRLSE model to reduce the operation time complexity.Two sets of RadarSat-2 SAR data were used to test the proposed method.The experimental results indicate that the bilateral filtering scheme incorporated into the energy function during level set evolution improved the stability of level set evolution.Compared with other methods,the proposed improved BF-DRLSE algorithm displayed a higher overall segmentation accuracy(97.83%).In addition,using an appropriate initial box boundary for the DRLSE method accelerated the global search process,improved the accuracy of oil spill segmentation,and reduced computational time.Therefore,the results suggest that the proposed framework is eff ective and applicable for marine oil spill segmentation.
文摘A method to detect airports in large space-borne synthetic aperture radar（SAR） imagery is studied.First,the large SAR imagery is segmented according to amplitude characteristics using maximum a posteriori（MAP） estimator based on the heavytailed Rayleigh model.The attention is then paid on the object of interest（OOI） extracted from the large images.The minimumarea enclosing rectangle（MER） of OOI is created via a rotating calipers algorithm.The projection histogram（PH） of MER for OOI is then computed and the scale and rotation invariant feature for OOI are extracted from the statistical characteristics of PH.A support vector machine（SVM） classifier is trained using those feature parameters and the airport is detected by the SVM classifier and Hough transform.The application in space-borne SAR images demonstrates the effectiveness of the proposed method.
基金supported by the National Natural Science Foundation of China(4117132741301361)+2 种基金the National Key Basic Research Program of China(973 Program)(2012CB719903)the Science and Technology Project of Ministry of Transport of People’s Republic of China(2012-364-X11-803)the Shanghai Municipal Natural Science Foundation(12ZR1433200)
文摘Segmentation is the key step in auto-interpretation of high-resolution spaceborne synthetic aperture radar（SAR） images. A novel method is proposed based on integrating the geometric active contour（GAC） and the support vector machine（SVM）models. First, the images are segmented by using SVM and textural statistics. A likelihood measurement for every pixel is derived by using the initial segmentation. The Chan-Vese model then is modified by adding two items： the likelihood and the distance between the initial segmentation and the evolving contour. Experimental results using real SAR images demonstrate the good performance of the proposed method compared to several classic GAC models.
