Hyperspectral imagery is capable of providing detailed spectral reflectance information of agricultural fields for potential use in site-specific management operations. Analysis of these data are complicated by the large number of spectral bands, the many different components or endmembers (e.g. plant and soil), and the presence of shadows. Unlike simple unmixing approaches which compute the fraction of a fixed number of components, multiple endmember spectral mixture analysis (MESMA) also determines which components are present in each pixel. This study compared whether using different shadow endmembers (EM) in a 4-EM model (sunlit green leaf, sunlit soil, shadowed leaf, shadowed soil) would improve estimates of scene components compared to a 3-EM model (sunlit green leaf, sunlit soil, photometric shade). Results revealed that correlations with percent cover and height were improved when shadow or shade endmembers were included for both models compared to the green leaf fraction alone. The 3-EM model was superior for developing a direct relationship for estimating cover and height but was not able to estimate SPAD or chlorophyll a. The 4-EM model showed the best results for SPAD and chlorophyll a, with r2 values of 0.84 and 0.77, respectively.
Hyperspectral images of the Earth’s surface are increasingly being acquired from aerial platforms. The dozens or hundreds of bands acquired by a typical hyperspectral sensor are acquired either through a scanning process or by collecting a sequence of images at varying wavelengths. This latter method has the advantage of acquiring coherent images of a scene at different wavelengths. However, it takes time to collect these images and some form of co-registration is required to build coherent image cubes. In this paper, we present a method to register many bands acquired sequentially at different wavelengths from a helicopter. We discuss the application of the Phase Correlation (PC) Method to recover scaling, rotation, and translation from an airborne hyperspectral imaging system, dubbed PHyTIS. This approach is well suited for remotely sensed images acquired from a moving platform, which induces image registration errors due to along and across track movement. We were able to register images to within ± 1 pixel across entire image cubes obtained from the PHyTIS hyperspectral imaging system, which was developed for precision farming applications.
Conference Committee Involvement (2)
Remote Sensing and Modeling of Ecosystems for Sustainability II
2 August 2005 | San Diego, California, United States
Remote Sensing and Modeling of Ecosystems for Sustainability
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