Abstract
Because of the limitations of hardware devices, such as the sensors, processing capacity, and high accuracy altitude control equipment, traditional optical remote sensing (RS) imageries capture information regarding the same scene from mostly one single angle or a very small number of angles. Nowadays, with video satellites coming into service, obtaining imageries of the same scene from a more-or-less continuous array of angles has become a reality. In this paper, we analyze the differences between the traditional RS data and continuous multi-angle remote sensing (CMARS) data, and unravel the characteristics of the CMARS data. We study the advantages of using CMARS data for classification and try to capitalize on the complementarity of multi-angle information and, at the same time, to reduce the embedded redundancy. Our arguments are substantiated by real-life experiments on the employment of CMARS data in order to classify urban land covers while using a support vector machine (SVM) classifier. They show the superiority of CMARS data over the traditional data for classification. The overall accuracy may increase up to about 9% with CMARS data. Furthermore, we investigate the advantages and disadvantages of directly using the CMARS data, and how such data can be better utilized through the extraction of key features that characterize the variations of spectral reflectance along the entire angular array. This research lay the foundation for the use of CMARS data in future research and applications.
Highlights
A variety of sensors have been brought into use since the launch of the first Earth observation satellite, including visible light, infrared ray, hyperspectral, synthetic aperture radar (SAR), dual light sensors for stereo mapping, etc. [1]
For the training set with two different labels, the support vector machine (SVM) training algorithm maps training data to a space and learns a classifier that can best separate two classes with the maximum margin between them
The multiple-class SVM, which is implemented in Matlab with the support of LIBSVM toolbox [33], is employed as a basis for the evaluation of land cover classification while using continuous multi-angle remote sensing (CMARS) data
Summary
A variety of sensors have been brought into use since the launch of the first Earth observation satellite, including visible light, infrared ray, hyperspectral, synthetic aperture radar (SAR), dual light sensors for stereo mapping, etc. [1]. A variety of sensors have been brought into use since the launch of the first Earth observation satellite, including visible light, infrared ray, hyperspectral, synthetic aperture radar (SAR), dual light sensors for stereo mapping, etc. With the increasing number of observation satellites being launched, the Global Earth Observation System of Systems (GEOSS) has been established to make better use of those satellites [2]. Spectral reflectances of the same land cover are different under different observation angles, traditional RS is deficient in providing a full coverage via an array of observation angles. We still have problems in establishing a precise model for the relationship between spectral reflectance and different observation angles. Finding an exact expression for the spectral reflectance of the earth surface at different observation angles is still a challenge [3,4,5].
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