Computationally efficient method for retrieving physical properties from 7-14 um hyperspectral imaging data under clear and cloudy background conditions

摘要

The present invention relates to a computationally compact and efficient method for determining physical characteristics of remote targets of interest from hyperspectral image scenes. Ground-based as well as space-borne hyperspectral imaging in the 7-14 microns region, also known as Thermal InfraRed (TIR) Hyperspectral imaging, is assuming increasing importance in military and civilian remote sensing. However, converting large hyperspectral imaging datasets into useable data products is complex and often requires long processing times. In-situ, field and on-board TIR hyperspectral imaging data processing is desirable for immediate detection, but currently very limited. Additionally, retrieving physical information of a target, seen against a background of clouds, is currently not possible. The present method creates a way to significantly improve the efficiency of analyzing hyperspectral imaging data to retrieve characteristics of remote targets of interest in the presence of both clear and cloudy sky background conditions. The present method uses a supervised machine learning Partial Least Squares Regression (PLSR) algorithm, which was trained from a library of simulated radiative transfer spectra. The radiative transfer library included a large number of complex conditions, which are difficult to implement in traditional lookup table methods, but become amenable in the present method. This invention is computationally compact and efficient and can be employed for on-board sensor data processing on the ground and in space. Various tests have shown the efficiency and reliability of the present method.