Shoujin Yin

State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing

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Xiaoling Chen

Guangxi University

Xiaobin Cai

Northwestern Polytechnical University

Xiaoling Chen

Wuhan University

Chuqun Chen

Institute of Oceanology

Jian Zhang

Huazhong Agricultural University

WenXia Gan

State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing

Hao Wu

Wuhan University

Cooperative Institutions

Wuhan University

State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing

University of Hong Kong

Chinese Academy of Sciences

Guangxi University

Shanghai Zhabei District Shibei Hospital

Nanchang Institute of Technology

Yanshan University

Fuzhou University

Guangzhou University of Chinese Medicine

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Exploring some issues of sub-pixel mapping based on directly spatial attraction

Jian Zhang Xiaoling Chen WenXia Gan Shoujin Yin Hao Wu

With pixel un-mixing, the omission of pixel caused by mixed pixel can be resolved so as to improve the classification accuracy. But the trouble is only the proportion of each end member object in one pixel which can be got through pixel un-mixing, while the spatial distribution of is uncertain. The objective of this study is to introduce the sub-pixel mapping based on spatial attraction model and explore some issues of it, such as neighboring pixels selection and spatial attraction normalization. As the experiments proven, the sub-pixel mapping could get better result by the eight neighboring pixels selecting mode and normalizing by sub-pixel mode in most cases.

Normalization

Mode (computer interface)

10.1109/igarss.2010.5652045

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Wavelet image fusion based on the high order polynomial regression

Xiaobin Cai Xiaoling Chen Shoujin Yin Chuqun Chen

In many applications of remote sensing there were demands to combine the high spatial and the high spectral resolutions for obtaining the most complete and accurate description of the observed scene. Some wavelet methods were utilized to fuse multispectral (MS) and panchromatic (PAN) images. Any image can be broken into various high- and lowfrequency components through discrete wavelet transformation (DWT). By definition, the low-frequency image is of approximation and the high-frequency image contains the detail of the image. Many merging models of DWT coefficients were developed. As the representation of the true detail information the high-frequency part of high resolution image (HRI) used in the fusion was often overemphasized, and the spectral difference between HRI and the channels of low resolution image (LRI) was often ignored. In some researches though the correlation analysis between the HRI and LRI were take account as the weight in the detail and approximation coefficients combination, the spectral relationship might not be expressed by the first order polynomial. The high order polynomial (2 order and 3 order) methods were introduced and the quantitative comparison was analyzed in this study. For 2 order regression, the method in different DWT functions all showed better appearances in the quantity assessment. But the 3 order results appeared serious instability.

Panchromatic film

Multiresolution analysis

10.1109/igarss.2007.4423500

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