Accuracy of a supervised classification of the artificial objects in thermal hyperspectral images

Thermal hyperspectral imagery introduces new possibilities in remote sensing. This paper deals with testing the accuracy of the supervised classification of the artificial objects in the thermal hyperspectral imagery, on a part of 79 channels, in three raster sets: (a) five thermal images (8.747, 9.648, 10.482, 11.266, 11.997 μm), (b) five thermal images and one infrared reflective (0.807 μm), (c) four thermal images (9.648, 10.482, 11.266, 11.997 μm), one infrared reflective and two visible images (0.501, 0.587 μm). The visible channels were used as a substitute for the real ground truth source and one infrared reflective channel was used for the enhanced discrimination of the vegetation. Global characteristics and principal components of thermal rasters were considered; unsupervised and supervised classifications have been done too. The result was difficulty in determining the training fields which are the best or, at least, good enough for desired accuracy of supervised classification