机构

• 长春光学精密机械与物... [2]
• 地理科学与资源研究所 [1]

采集方式

• OAI收割 [3]

内容类型

• 会议论文 [2]
• 期刊论文 [1]

发表日期

• 2024 [1]
• 2012 [1]
• 2010 [1]

学科主题

筛选

浏览/检索结果: 共3条，第1-3条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序作者升序作者降序 Multi-scale analysis of satellite, reanalysis and muti-source precipitation estimates over the Tibetan Plateau 期刊论文  OAI收割 ATMOSPHERIC RESEARCH, 2024, 卷号: 309, 页码: 107484 作者:  Feng, Yuqiao;  Qi, Youcun;  Chen, Deliang;  Li, Donghuan;  Li, Zhe   |  收藏  |  浏览/下载：13/0  |  提交时间：2024/10/21 Tibetan Plateau  Spatiotemporal variability of precipitation  Satellite precipitation estimates  Reanalysis precipitation estimates  Multi-scale comparison   A fast target recognition algorithm based on MSA and MSR (EI CONFERENCE) 会议论文  OAI收割 2012 International Conference on Industrial Control and Electronics Engineering, ICICEE 2012, August 23, 2012 - August 25, 2012, Xi'an, China 作者:  Wang Y.;  Liu G.;  Wang Y.;  Wang Y.;  Wang Y. 收藏  |  浏览/下载：32/0  |  提交时间：2013/03/25 This paper presents a new fast target recognition algorithm  the proposed method is based on Multi-scale Auto convolution(MSA) and Multi-scale Retinex(MSR). As shown by the comparison with original MSA  it appears that this new technique solves the problem that MSA algorithm is sensitive to illumination and the computational load is significantly reduced to 1/8th of that of the original MSA algorithm  it is also robust to affine transform  light projective transform  noise  thin fog  occlusion and illumination change. the performed experiments show that it has fast searching speed  and can accurately recognize and locate target in real scenes. 2012 IEEE.   Destriping method using lifting wavelet transform of remote sensing image (EI CONFERENCE) 会议论文  OAI收割 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering, CMCE 2010, August 24, 2010 - August 26, 2010, Changchun, China 作者:  He B. 收藏  |  浏览/下载：32/0  |  提交时间：2013/03/25 Based on the characteristic of striping noise in remote sensing images  a new destriping noise technique for the improved multi-threshold method using lifting wavelet transform applied to remote sensing imagery is presented in this letter. Have used the lifting wavelet decomposition algorithm  the thresholds are determined by corresponding wavelet coefficients in every scale. Remote sensing imagery is so large that the algorithm must be fast and effective. The lifting wavelet transform is easily realized and inexpensive in computer time and storage space compared with the traditional wavelet transform. We also compare the method with some traditional destriping methods both by visual inspection and by appropriate indexes of quality of the denoised images. From the comparison we can see that the adaptive threshold method can preserve the spectral characteristic of the images while effectively remove striping noise and it did better than the existed ones. 2010 IEEE.