Extraction of landscape pattern information from Airborne Hyperspectral Images

In view of the large deviation of landscape pattern information extraction results caused by many types of landscape patterns and strong interference factors, a landscape pattern information extraction method based on Airborne Hyperspectral Images is proposed. Relevant images are collected through the imaging and interpretation process of ground object spectra, and the remote sensing images are decomposed and processed. The decomposed images are fused by Laplace method. On this basis, according to the second-order neighborhood difference algorithm of Markov random field model, the energy function in the background is extracted, the non target landscape pattern information is suppressed, and the target area of landscape pattern is calibrated. The spectral vector is added in front of the projection operator, and the background and landscape pattern information of the calibration area are separated by means of low probability detection algorithm to realize the extraction of landscape pattern information. The experimental results show that the proposed method has high integrity, short running time and high accuracy.