摘要 :
This paper presents an improved method of speckle filtering in SAR image based on structure detection to solve some problems of the Lopes' structure detection and statistical adaptive speckle filtering. The method uses the probabi...
展开
This paper presents an improved method of speckle filtering in SAR image based on structure detection to solve some problems of the Lopes' structure detection and statistical adaptive speckle filtering. The method uses the probability iterative to segment SAR image and detect the edges, then combines the strong scatterer detection result to label the SAR image as structure area and non-structure area. In non-structure area, the Lee filter is used, and in the structure area, the intensity of the image is preserved. Experimental results with RADARSAT images verify the practicability and the advantage of the improved method.
收起
摘要 :
This paper presents an improved method of speckle filtering in SAR image based on structure detection to solve some problems of the Lopes' structure detection and statistical adaptive speckle filtering. The method uses the probabi...
展开
This paper presents an improved method of speckle filtering in SAR image based on structure detection to solve some problems of the Lopes' structure detection and statistical adaptive speckle filtering. The method uses the probability iterative to segment SAR image and detect the edges, then combines the strong scatterer detection result to label the SAR image as structure area and non-structure area. In non-structure area, the lee filter is used, and in the structure area, the intensity of the image is preserved. Experimental results with RADARSAT images verify the practicability and the advantage of the improved method.
收起
摘要 :
This paper presents an improved method of speckle filtering in SAR image based on structure detection to solve some problems of the Lopes' structure detection and statistical adaptive speckle filtering. The method uses the probabi...
展开
This paper presents an improved method of speckle filtering in SAR image based on structure detection to solve some problems of the Lopes' structure detection and statistical adaptive speckle filtering. The method uses the probability iterative to segment SAR image and detect the edges, then combines the strong scatterer detection result to label the SAR image as structure area and non-structure area. In non-structure area, the lee filter is used, and in the structure area, the intensity of the image is preserved. Experimental results with RADARSAT images verify the practicability and the advantage of the improved method.
收起
摘要 :
Concerning the detection of ground moving targets in dual-channel SAR images, a novel detector based on sample covariance matrix is proposed. The method makes use of the principle of orthogonal projection, for obtaining the compon...
展开
Concerning the detection of ground moving targets in dual-channel SAR images, a novel detector based on sample covariance matrix is proposed. The method makes use of the principle of orthogonal projection, for obtaining the component of the sample covariance matrix energy vector that is perpendicular to the clutter covariance matrix energy vector, then construct effective metric by this orthogonal component. Compared with traditional DPCA, this new metric can achieve better clutter rejection, eliminate influences from moving targets' sidelobes, set threshold more easily and get lower false alarm probability. The simulated results prove the effectiveness of this metric.
收起
摘要 :
Utilizing MSTAR measured target data, we analyze statistical trend in the entire measurement training set every 1 deg in azimuth based on correlation algorithm. After segmentation and normalization, each test image was correlated ...
展开
Utilizing MSTAR measured target data, we analyze statistical trend in the entire measurement training set every 1 deg in azimuth based on correlation algorithm. After segmentation and normalization, each test image was correlated with all the training images to generate correlation and classification statistics and correlation plots. The correlation plots varied approximately sinusoidally with aspect, and all the training sets show that a target was highly correlated at both the correct aspect angle and the correct angle rotated 180 deg, and this two correlation scores corresponded to the two local amplitudes of the correlation plot.
收起
摘要 :
Utilizing MSTAR measured target data, we analyze statistical trend in the entire measurement training set every 1 deg in azimuth based on correlation algorithm. After segmentation and normalization, each test image was correlated ...
展开
Utilizing MSTAR measured target data, we analyze statistical trend in the entire measurement training set every 1 deg in azimuth based on correlation algorithm. After segmentation and normalization, each test image was correlated with all the training images to generate correlation and classification statistics and correlation plots. The correlation plots varied approximately sinusoidally with aspect, and all the training sets show that a target was highly correlated at both the correct aspect angle and the correct angle rotated 180 deg, and this two correlation scores corresponded to the two local amplitudes of the correlation plot.
收起
摘要 :
Utilizing MSTAR measured target data, we analyze statistical trend in the entire measurement training set every 1 deg in azimuth based on correlation algorithm. After segmentation and normalization, each test image was correlated ...
展开
Utilizing MSTAR measured target data, we analyze statistical trend in the entire measurement training set every 1 deg in azimuth based on correlation algorithm. After segmentation and normalization, each test image was correlated with all the training images to generate correlation and classification statistics and correlation plots. The correlation plots varied approximately sinusoidally with aspect, and all the training sets show that a target was highly correlated at both the correct aspect angle and the correct angle rotated 180 deg, and this two correlation scores corresponded to the two local amplitudes of the correlation plot.
收起
摘要 :
3D surface reconstruction has been widely used in reverse engineering, remanufacturing and automatic measurement. Binocular stereo imaging technology is the main method to obtain 3D information. Images are acquired from different ...
展开
3D surface reconstruction has been widely used in reverse engineering, remanufacturing and automatic measurement. Binocular stereo imaging technology is the main method to obtain 3D information. Images are acquired from different positions of the object. The 3D coordinates of the point can be calculated according to the point's coordinates in left and right cameras. The key to ensure the accuracy of 3D reconstruction is high precision and fast matching method of two images. As the image feature was not obvious, structured light is used. For its easy identification and extraction, the method has been more and more widely used. But the former research cannot ensure the matching exactness or matching algorithm takes too much time. In order to improve the precision and efficiency of 3D measurement, a fast and simple structured light matching method is designed based on epipolar geometry. This method can ensure 100% matching accuracy. It does not need other assistant tool except for structured light projector. On the beginning of the measurement, structured light is projected onto the object to be measured. Two images of the object with structured light are captured by two cameras in different positions. Structured lights extracted from the two images are matched by the new algorithm. The 3D coordinate of this structured light is calculated. The position of structured light is changed on the object till completed. From a lot of experiments, the proposed grating matching method is proved and it is a technique with high precision, low costs, easy operation, and an automatically matching method. Furthermore, it can be widely used in most of 3D reconstruction system.
收起
原文获取