检索结果-南通市图书馆

Unsupervised linear spectral mixture analysis with AVIRIS data

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Journal of Harbin Institute of Technology(New Series) 2005年第5期12卷 471-476页

作者：谷延锋杨冬云张晔 Dept. of Information Engineering Harbin Institute of Technology Harbin 150001 China Dept. of Electronic Engineering Heilongjiang Institute of Technology Harbin 150050 China

A new algorithm for unsupervised hyperspectral data unmixing is investigated, which includes a modified minimum noise fraction （MNF） transformation and independent component analysis （ICA）. The modified MNF transformation is used to reduce noise and remove correlation between neighboring bands. Then the ICA is applied to unmix hyperspectral images, and independent endmembers are obtained from unmixed images by using post-processing which includes image segmentation based on statistical histograms and morphological operations. The experimental results demonstrate that this algorithm can identify endmembers resident in mixed pixels. Meanwhile, the results show the high computational efficiency of the modified MNF transformation. The time consumed by the modified method is almost one fifth of the traditional MNF transformation.

关键词： spectral mixture analysis minimum noise fraction independent component analysis linear mixture model adaptive subspace decomposition

Improvement of Urban Impervious Surface Estimation in Shanghai Using Landsat7 ETM+ Data

在线全文

学校读者我要写书评

暂无评论

Chinese Geographical Science 2009年第3期19卷 283-290页

作者： YUE Wenze Department of Land Management Zhejiang University Hangzhou 310029 China

This paper explores the potential to improve the impervious surface estimation accuracy using a multi-stage approach on the basis of vegetation-impervious surface-soil (V-I-S) model. In the first stage of spectral mixture analysis (SMA) process, pixel purity index, a quantitative index for defining endmember quality, and a 3-dimensional endmember selection method were applied to refining endmembers. In the second stage, instead of obtaining impervious surface fraction by adding high and low albedo fractions directly, a linear regression model was built between impervious surface and high/low albedo using a random sampling method. The urban impervious surface distribution in the urban central area of Shanghai was predicted by the linear regression model. Estimation accuracy of spectral mixture analysis and impervious surface fraction were assessed using root mean square (RMS) and color aerial photography respectively. In comparison with three different research methods, this improved estimation method has a higher overall accuracy than traditional Linear spectral mixture analysis (LSMA) method and the normalized SMA model both in root mean square error (RMSE) and standard error (SE). However, the model has a tendency to overestimate the impervious surface distribution.

关键词： vegetation-impervious surface-soil model spectral mixture analysis impervious surface Shanghai

Detecting soil salinity with arid fraction integrated index and salinity index in feature space using Landsat TM imagery

在线全文

学校读者我要写书评

暂无评论

Journal of Arid Land 2013年第3期5卷 340-353页

作者： Fei WANG Xi CHEN GePing LUO JianLi DING XianFeng CHEN State Key Laboratory of Desert and Oasis Ecology Xinjiang Institute of Ecology and GeographyChinese Academy of Sciences University of Chinese Academy of Sciences College of Resources and Environmental Science Xinjiang University Slippery Rock University of Pennsylvania Slippery RockPA16057USA

Modeling soil salinity in an arid salt-affected ecosystem is a difficult task when using remote sensing data because of the complicated soil context （vegetation cover, moisture, surface roughness, and organic matter） and the weak spectral features of salinized soil. Therefore, an index such as the salinity index （SI） that only uses soil spectra may not detect soil salinity effectively and quantitatively. The use of vegetation reflectance as an indirect indicator can avoid limitations associated with the direct use of soil reflectance. The normalized difference vegetation index （NDVI）, as the most common vegetation index, was found to be responsive to salinity but may not be available for retrieving sparse vegetation due to its sensitivity to background soil in arid areas. Therefore, the arid fraction integrated index （AFⅡ） was created as supported by the spectral mixture analysis （SMA）, which is more appropriate for analyzing variations in vegetation cover （particularly halophytes） than NDVI in the study area. Using soil and vegetation separately for detecting salinity perhaps is not feasible. Then, we developed a new and operational model, the soil salinity detecting model （SDM） that combines AFⅡ and SI to quantitatively estimate the salt content in the surface soil. SDMs, including SDM1 and SDM2, were constructed through analyzing the spatial characteristics of soils with different salinization degree by integrating AFⅡ and SI using a scatterplot. The SDMs were then compared to the combined spectral response index （COSRI） from field measurements with respect to the soil salt content. The results indicate that the SDM values are highly correlated with soil salinity, in contrast to the performance of COSRI. Strong exponential relationships were observed between soil salinity and SDMs （R2〉0.86, RMSE〈6.86） compared to COSRI （R2=0.71, RMSE=16.21）. These results suggest that the feature space related to biophysical properties combined with AFII and SI can effectively pr

关键词： soil salinity spectrum halophytes Landsat TM spectral mixture analysis feature space model

Carbon uptake and change in net primary productivity of oasis-desert ecosystem in arid western China with remote sensing technique

在线全文

学校读者我要写书评

暂无评论

Journal of Geographical Sciences 2006年第3期16卷 315-325页

作者： ZHANG Jie PAN Xiaoling GAO Zhiqiang SHI Qingdong LV Guanghui Department of Urban and Resources Sciences Nanjing University Nanjing 210093 China Institute of Arid Ecology ＆ Environment Xinjiang University Urumqi 830046 China Institute of Geographic Sciences and Natural Resources Research CAS Beijing 100101 China

Arid and semi-arid ecosystems exhibit a spatially complex biogeophysical structure. According to arid western special climate-vegetation characters, the fractional cover of photosynthetic vegetation （PV）, non-photosynthetic vegetation （NPV）, bare soil and water are unmixed, using the remote sensing spectral mixture analysis. We try the method to unmix the canopy funation structure of arid land cover in order to avoid the differentiation of regional vegetation system and the disturbance of environmental background. We developed a modified production efficiency model NPP-PEM appropriate for the arid area at regional scale based on the concept of radiation use efficiency. This model refer to the GLO-PEM and CASA model was driven with remotely sensed observations, and calculates not just the conversion efficiency of absorbed photosynthetically active radiation but also the carbon fluxes that determine net primary productivity （NPP）. We apply and validate the model in the Kaxger and Yarkant river basins in arid western China. The NPP of the study area in 1992 and 1998 was estimated based on the NPP-PEM model. The results show that the improved PEM model, considering the photosynthetical activation of heterogeneous functional vegetation, is in good agreement with field measurements and the existing literature. An accurate agreement （R2= 0.85, P〈0.001） between the estimates and the ground-based measurement was obtained. The spatial distribution of mountain-oasis-desert ecosystem shows an obvious heterogeneous carbon uptake. The results are applicable to arid ecosystem studies ranging from characterizing carbon cycle, carbon flux over arid areas to monitoring change in mountain-oasis-desert productivity, stress and management.

关键词： add western area oasis desert spectral mixture analysis NPP-PEM net primary productivity , Kaxger