Local density based background estimation

摘要

Background statistics estimation is the key point for the statistics model based detectors. The background statistics obtained globally from the whole image may be inaccurate due to target contamination of the background information. To solve this problem, this paper proposed a local density based background estimation algorithm (LDBE) based on the definition of local density based anomaly score (LDAS). LDAS is a new metric that utilizes the distance between spectral to calculate each pixel's probability of background. LDBE uses LDAS as a criterion to determine whether a pixel is part of the background or not. By applying this algorithm, the background statistics can be estimated more accurately with the non-background pixels eliminated. The experimental results on real hyperspectral datasets suggest that the proposed background estimation algorithm can greatly improve the performance of statistical model based target detectors.