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4 results on '"Shen, Zhangquan"'

3. Review of shadow detection and de-shadowing methods in remote sensing

Author

Yang Ning, Nathan Moore, AmirReza Shahtahmassebi, Shen Zhangquan, and Wang Ke

Subjects
business.industry, Geography, Planning and Development, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Cloud computing, computer.software_genre, Thresholding, GeneralLiterature_MISCELLANEOUS, Information extraction, Remote sensing (archaeology), Shadow, General Earth and Planetary Sciences, Satellite, business, Image resolution, computer, Change detection, ComputingMethodologies_COMPUTERGRAPHICS, Remote sensing
Abstract

Shadow is one of the major problems in remotely sensed imagery which hampers the accuracy of information extraction and change detection. In these images, shadow is generally produced by different objects, namely, cloud, mountain and urban materials. The shadow correction process consists of two steps: detection and de-shadowing. This paper reviews a range of techniques for both steps, focusing on urban regions (urban shadows), mountainous areas (topographic shadow), cloud shadows and composite shadows. Several issues including the problems and the advantages of those algorithms are discussed. In recent years, thresholding and recovery techniques have become important for shadow detection and de-shadowing, respectively. Research on shadow correction is still an important topic, particularly for urban regions (in high spatial resolution data) and mountainous forest (in high and medium spatial resolution data). Moreover, new algorithms are needed for shadow correction, especially given the advent of new satellite images.

Published
2013

4. Evaluation on the two filling functions for the recovery of forest information in mountainous shadows on Landsat ETM + Image.

Author

Amir reza, SHAHTAHMASSEBI, WANG Ke, SHEN Zhangquan, DENG Jinsong, ZHU Wenjuan, HAN Ning, LIN Fengfang, and Nathan, MOORE

Subjects
VEGETATION mapping, FOREST mapping, LANDSAT satellites, ARTIFICIAL satellites
Abstract

In general, topographic shadow may reduce performance of forest mapping over mountainous regions in remotely sensed images. In this paper, information in shadow was synthesized by using two filling techniques, namely, roifill and imfill, in order to improve the accuracy of forest mapping over mountainous regions. These two methods were applied to Landsat Enhanced Thematic Mapper (ETM +) multispectral image from Dong Yang County, Zhejiang Province, China. The performance of these methods was compared with two conventional techniques, including cosine correction and multisource classification. The results showed that by applying filling approaches, average overall accuracy of classification was improved by 14 percent. However, through conventional methods this value increased only by 9 percent. The results also revealed that estimated forest area on the basis of shadow- corrected images by 'roifill' technique was much closer to the survey data compared to traditional algorithms. Apart from this finding, our finding indicated that topographic shadow was an accentuated problem in medium resolution images such as Landsat ETM+ over mountainous regions. [ABSTRACT FROM AUTHOR]

Published
2011
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