关键词: 高光谱影像, 混合像元, 光谱信息熵, N-FINDR, 端元提取

Abstract:

Endmember extraction is a key step for unmixing hyperspectral mixed pixels and an important prerequisite in the further analysis of hyperspectral imagery. The traditional N-FINDR algorithm is a classical and effective algorithm among various endmember extraction methods. However, the N-FINDR algorithm need to compute all the possible pixel combinations, thus it is time consuming. In order to improve the time efficiency of the N-FINDR algorithm, this paper proposed an improved N-FINDR algorithm on hyperspectral endmember extraction based on spectral Shannon entropy theory and the convex geometry, meanwhile we utilize the characteristic that in spectral feature space, all pixels of the hyperspectral imagery could compose a single shape body, in which the pure pixels are located at the apex and the mixed pixels in the interior or at the surface. The spectral Shannon entropies of all pixels are calculated according to the pixel gray probability, and are used to determine the purity of pixels. The pixel is removed if its spectral Shannon entropy is greater than the threshold value of the spectral Shannon entropy, otherwise it is preserved. Next, the N-FINDR was used to search the largest volume from the single shape body composed by the preserved pixels, and the pixels at the apex of the body with the largest volume would be the endmembers. Finally, we use the Hyperion data of a copper mine in Dexing city from Jiangxi province to testify the improved N-FINDR algorithm. By analyzing the experimental results, the improved algorithm ensured a high accuracy as well as improved the data processing efficiency very greatly in the course of extracting hyperspectral endmembers.

Key words: hyperspectral imagery, mixed pixel, spectral Shannon entropy, N-FINDR, endmember extraction