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A SpatialSpectral Combination Method for Hyperspectral Band Selection 期刊论文
Remote Sensing, 2022, 卷号: 14, 页码: 13
作者:  X. Han;  Z. Jiang;  Y. Liu;  J. Zhao;  Q. Sun and Y. Li
收藏  |  浏览/下载：0/0  |  提交时间：2023/06/14
Hyperspectral Band Selections for Enhancing the Discrimination of Difficult Targets Using Local Band Index and Particle Swarm Optimization 期刊论文
Applied Sciences-Basel, 2022, 卷号: 12, 期号: 8, 页码: 25
作者:  H. W. Wang;  C. X. Yan;  J. Yuan and Q. P. Lu
收藏  |  浏览/下载：2/0  |  提交时间：2023/06/14
Nondestructive grading test of rice seed activity using near infrared super-continuum laser spectrum 期刊论文
Chinese Optics, 2020, 卷号: 13, 期号: 5, 页码: 1032-1043
作者:  W.-l. Jin,N.-l. Cao,M.-d. Zhu,W. Chen,P.-g. Zhang,Q.-l. Zhao,J.-q. Liang,Y.-h. Yu,J.-g. Lv and R.-f. Kan
收藏  |  浏览/下载：4/0  |  提交时间：2021/07/06
Theoretical analysis and verification of Ronchi grating auto-collimated focusing for aerial remote camera 期刊论文
Guangxue Jingmi Gongcheng/Optics and Precision Engineering, 2020, 卷号: 28, 期号: 6, 页码: 1236-1244
作者:  X.-J. Liu,Y.-L. Ding,F. Li,Z.-C. Chen,G.-Q. Yuan and Z.-M. Liu
收藏  |  浏览/下载：9/0  |  提交时间：2021/07/06
基于直线特征的异源景象匹配技术研究 学位论文
硕士: 中国科学院大学, 2015
作者:  王力
收藏  |  浏览/下载：44/0  |  提交时间：2016/04/11
TMA空间相机连接可靠性设计 学位论文
硕士: 中国科学院大学, 2015
作者:  邓平
收藏  |  浏览/下载：17/0  |  提交时间：2015/11/30
液晶/聚合物光栅激光器的制备研究 学位论文
博士: 中国科学院大学, 2014
黄文彬
收藏  |  浏览/下载：41/0  |  提交时间：2014/08/21
An auto-focus algorithm of fast search based on combining rough and fine adjustment (EI CONFERENCE) 会议论文
3rd international Conference on Manufacturing Science and Engineering, ICMSE 2012, March 27, 2012 - March 29, 2012, Xiamen, China
Lin Z.; Liu X.; Zhang Y.; Zhang S.
收藏  |  浏览/下载：14/0  |  提交时间：2013/03/25
A coarse and fine combined fast search and auto-focusing algorithm was suggested in this paper. This method can automatically search and find the focal plane by evaluating the image definition. The Krisch operator based edge energy function was used as the big-step coarse focusing  and then the wavelet transform based image definition evaluation function  which is sensitivity to the variation in image definition  was used to realize the small-step fine focusing in a narrow range. The un-uniform sampling function of the focusing area selection used in this method greatly reduces the workload and the required time for the data processing. The experimental results indicate that this algorithm can satisfy the requirement of the optical measure equipment for the image focusing. (2012) Trans Tech Publications.  
Autofocusing technique based on image processing for remote-sensing camera (EI CONFERENCE) 会议论文
International Symposium on Photoelectronic Detection and Imaging 2007 - Image Processing, September 9, 2007 - September 12, 2007, Beijing, China
Wang X.; Sun R.-C.; Xu S.-Y.
收藏  |  浏览/下载：15/0  |  提交时间：2013/03/25
The key to the auto-focusing technique based on image processing is the selection of focus measure reflecting image definition. Usually the measures derived are on the premise of the images acquired with the same scene. As for the remote-sensing camera working in linear CCD push-broom imaging mode  the premise doesn't exist because the scenes shot are different at any moment  which brings about difficulties to the selection of the focus measure. To evaluate the image definition  the focus measure based on blur estimation for rough adjustment is proposed to estimate the focused position by only two different lens positions  which greatly saves the auto-focusing time. Another evaluation function based on edge sharpness is developed to find best imaging position in the narrow range. Simulations show that the combination of the two measures has the advantages of rapid reaction and high accuracy.  

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