Ms. Weiwei Ma Profile

Weiwei Ma

at Shanghai Institute of Technical Physics of CAS

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Publications (3)

Proceedings Article | 8 October 2015 Paper

A multi-characteristic based algorithm for classifying vegetation in a plateau area: Qinghai Lake watershed, northwestern China

Weiwei Ma, Cailan Gong, Yong Hu, Long Li, Peng Meng

Proceedings Volume 9675, 96750L (2015) https://doi.org/10.1117/12.2197777

KEYWORDS: Earth observing sensors, Vegetation, Charge-coupled devices, Image classification, Remote sensing, CCD image sensors, Spatial resolution, Data modeling, Reflectivity, Landsat

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Proceedings Article | 30 August 2013 Paper

The Hughes phenomenon in hyperspectral classification based on the ground spectrum of grasslands in the region around Qinghai Lake

Weiwei Ma, Cailan Gong, Yong Hu, Peng Meng, Feifei Xu

Proceedings Volume 8910, 89101G (2013) https://doi.org/10.1117/12.2034457

KEYWORDS: Neodymium, Remote sensing, Principal component analysis, Vegetation, Distance measurement, Statistical analysis, Spectral resolution, Spectroscopy, Hyperspectral imaging, Mahalanobis distance

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Hyperspectral data, consisting of hundreds of spectral bands with a high spectral resolution, enables acquisition of continuous spectral characteristic curves, and therefore have served as a powerful tool for vegetation classification. The difficulty of using hyperspectral data is that they are usually redundant, strongly correlated and subject to Hughes phenomenon where classification accuracy increases gradually in the beginning as the number of spectral bands or dimensions increases, but decreases dramatically when the band number reaches some value. In recent years，some algorithms have been proposed to overcome the Hughes phenomenon in classification, such as selecting several bands from full bands, PCA- and MNF-based feature transformations. Up to date, however, few studies have been conducted to investigate the turning point of Hughes phenomenon (i.e., the point at which the classification accuracy begins to decline). In this paper, we firstly analyze reasons for occurrence of Hughes phenomenon, and then based on the Mahalanobis classifier, classify the ground spectrum of several grasslands which were recorded in September 2012 using FieldSpec3 spectrometer in the regions around Qinghai Lake，a important pasturing area in the north of China. Before classification, we extract features from hyperspectral data by bands selecting and PCA- based feature transformations, and In the process of classification, we analyze how the correlation coefficient between wavebands, the number of waveband channels and the number of principal components affect the classification result. The results show that Hushes phenomenon may occur when the correlation coefficient between wavebands is greater than 94%，the number of wavebands is greater than 6, or the number of principal components is greater than 6. Best classification result can be achieved (overall accuracy of grasslands 90%) if the number of wavebands equals to 3 (the band positions are 370nm, 509nm and 886nm respectively) or the number of principal components ranges from 4 to 6.

Proceedings Article | 30 August 2013 Paper

Automatic identification and tracking the Arctic sea ice based on sequential images of FY-3A satellite

Ying Zhou, Cai-lan Gong, Yong Hu, Peng Meng, Wei-wei Ma

Proceedings Volume 8910, 89101N (2013) https://doi.org/10.1117/12.2034650

KEYWORDS: Clouds, Image segmentation, Satellites, Satellite imaging, Reflectivity, Automatic tracking, Earth observing sensors, Image resolution, Optical tracking, Data centers

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