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Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

：Objectives: In order to improve the accuracy of SAR （synthetic aperture radar） image change detection, this paper proposes a method of SAR image change detection based on variational method and Markov random field fuzzy local information C‐means clustering （MRFFLICM） method.Methods: Firstly, we fuse the logarithmic ratio images and logarithmic mean ratio images to construct the difference image. Secondly, variational denoising model is established to remove the noise from difference images. Finally, the spatial neighborhood information is introduced into fuzzy local information C ‐means clustering method by using Markov random field to improve the clustering performance.Results: Experiments on two real SAR datasets show that the proposed variational denoising method can avoid removing the small change region and effectively suppress speckle noise of SAR image. Conclusions: The MRFFLICM method can effectively improve the precision of change detection, thus enhancing the adaptability of change detection method. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the region proposal network (RPN) module couldn't adequately extract target features when performing target detection in the traditional Faster-RCNN method, a method for vehicle target detection on the SAR image based on the improved RPN in Faster-RCNN was proposed. First, the multi-layer features of the image were extracted based on the VGG-16 network. Then the deepest three feature layers were further extracted and regularized using convolution kernels. Finally, the information fusion was performed on the three processed feature layers. Using the vehicle target SAR image and the natural background SAR image in the MSTAR data set, the SAR scene data set was created by image segmentation and texture, and the improved network was trained and tested. Experimental results show that in the detection of the vehicle targets on SAR images, compared with the traditional RPN, the improved RPN has a faster convergence speed, improving the accuracy of the test results from 97.7% to 99.7%, with a lower false alarm rate, and has stronger generalization performance. For targets outside the training range, the accuracy can be increased from 98.0% to 99.0%. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Image & Graphics is the property of Editorial Office of Journal of Image & Graphics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Image & Graphics is the property of Editorial Office of Journal of Image & Graphics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Image & Graphics is the property of Editorial Office of Journal of Image & Graphics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to estimate the parameters of the log-normal distribution of radar cross section (RCS ) model of synthetic aperture radar (SAR) images from the observed image, this paper proposed the parameter estimator for intensity image and amplitude image. First, it derived the first two log-cumulants of speckle. Second, it derived the first two log-cumulants of log-normal distribution. Last, it obtained the parameter estimator of the log-normal distribution of RCS model based on the second-kind statistics and the multiplicative model of speckle. The proposed estimator was easy to implement due to the analytical expressions. Parameter estimation experiments on Monte Carlo simulations and SAR images demonstrate that the proposed estimator can effectively estimate the parameters of the log-normal distribution of RCS model from the observed image. [ABSTRACT FROM AUTHOR]