机构

• 长春光学精密机械与物... [1]
• 遥感与数字地球研究所 [1]

采集方式

• OAI收割 [2]

内容类型

• 会议论文 [1]
• 期刊论文 [1]

发表日期

• 2015 [1]
• 2006 [1]

学科主题

筛选

浏览/检索结果: 共2条，第1-2条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 Glacier surface motion pattern in the Eastern part of West Kunlun Shan estimation using pixel-tracking with PALSAR imagery 期刊论文  OAI收割 ENVIRONMENTAL EARTH SCIENCES, 2015, 卷号: 74, 期号: 3, 页码: 948-960 作者:  Yan, Shiyong;  Liu, Guang;  Wang, Yunjia;  Perski, Zbigniew;  Ruan, Zhixing 收藏  |  浏览/下载：41/0  |  提交时间：2016/04/20 Mountain glacier  Ice surface velocity  Pixel-tracking technique  West Kunlun Shan  ALOS/PALSAR   Displacement estimation by the phase-shiftings of fourier transform in present white noise (EI CONFERENCE) 会议论文  OAI收割 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China 作者:  Wu Y.-H. 收藏  |  浏览/下载：27/0  |  提交时间：2013/03/25 Displacement estimation is a fundamental problem in Real-time video image processing. It can be typically approached by theories based on features in spatial domain. This paper presents an algorithm which improves the theory for estimating the moving object's displacement in spatial domain by its Fourier transform frequency spectrum. Because of the characters of Fourier transform  the result is based on all the features in the image. Utilizing shift theorem of Fourier transform and auto-registration  the algorithm employs the phase spectrum difference in polar coordinate of two frame images sequence with the moving target1  2. The method needn't transform frequency spectrum to spatial domain after calculation comparing with the traditional algorithm which has to search Direc peak  and it reduces processing time. Since the technique proposed uses all the image information  including all the white noise in the image especially  and it's hard to overcome the aliasing from noises  but the technique can be an effective way to analyze the result in little white noise by the different characters between high and low frequency bands. It can give the displacement of moving target within 1 pixel of accuracy. Experimental evidence of this performance is presented  and the mathematical reasons behind these characteristics are explained in depth. It is proved that the algorithm is fast and simple and can be used in image tracking and video image processing.