Nonlinear Spectral Unmixing of Landsat Imagery for Urban Surface Cover Mapping
Nonlinear Spectral Unmixing of Landsat Imagery for Urban Surface Cover Mapping
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DOI：
10.1109/jstars.2016.2522181
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发表时间：
2016-07-01
影响因子：
5.5
通讯作者：
Carbone, Francesco
中科院分区：
文献类型：
--
作者：
Mitraka, Zina;Del Frate, Fabio;Carbone, Francesco
人造结构的高度空间多样性、城市材料的光谱变异性以及城市的三维结构使得利用地球观测数据绘制城市表面图成为遥感领域最具挑战性的任务之一。光谱分解技术可以被证明是有用的中光谱分辨率数据，以评估亚像素水平的城市地表覆盖信息。由于城市材料的光谱变异性大，光在城市区域表面之间的多次散射，需要使用多个端元，并考虑光谱混合物的非线性。在这项研究中，这些问题是用端元和非线性混合合成光谱训练的人工神经网络来解决的，用来反演陆地卫星图像中的像素光谱混合。建立了一个光谱库，包括从图像中收集的端元光谱和使用专门为城市地区开发的非线性模型产生的合成光谱。该方法在案例研究中进行了测试，对高分辨率产品的验证表明，所有丰度图的准确率约为90%。该方法的线性实现和非线性实现之间的比较证明了包括非线性项的必要性，特别是为了改进建立的丰度图。该方法易于移植到任何城市，计算速度快，是实现可运营的城市服务的理想方法。
The high spatial diversity of man-made structures, the spectral variability of urban materials, and the three-dimensional structure of the cities make the mapping of urban surfaces using Earth Observation data, one of the most challenging tasks in remote sensing field. Spectral unmixing techniques can be proven useful with medium spectral resolution data to assess urban surface cover information on a subpixel level. Due to the large spectral variability of urban materials and the multiple scattering of light between surfaces in urban areas, multiple endmembers should be used, and the nonlinearity of spectral mixture should be accounted for. In this study, these issues are addressed using an artificial neural network trained with endmember and nonlinearly mixed synthetic spectra to inverse the pixel spectral mixture in Landsat imagery. A spectral library is built, consisting of endmember spectra collected from the image and synthetic spectra, produced using a nonlinear model specifically developed for urban areas. The method was tested over a case study, and the validation against higher resolution products revealed an accuracy of around 90% for all abundance maps. The comparison performed between the linear and nonlinear implementation of the method proved the need for including the nonlinear term, especially for improving the built-up abundance map. The proposed method is easily transferable to any city and fast in terms of computations, which makes it ideal for the implementation of operational urban services.