Extrema-based Band Selection for Hyperion Data

극단화소 기반의 Hyperion 데이터 밴드선택

Among 242 Hyperion bands, there are 46 bands that contain completely no information and some other bands with various kinds of noise. It is mainly due to the atmosphenc absorption and the low signal-to-noise ratio. The visual inspection for selecting clean and stable bands is a simple practice, but is a manual, inefficient, and subjective Process. Though uncalibrated, overlapping, and all deep water absorption bands are removed, there still exist noisy bands. In this paper, we propose that the extrema ratio be measured for noise estimation and the unsupervised band selection be performed using the Expectation-Maximization algorithm. The Hyperion data were classified into 5 categories according to the image quality by visual inspection, and used as the reference data. The accuracy of the proposed method was compared with signal-to-noise ranking and entropy ranking. As a result, the proposed mettled was effective as preprocessing step for band selection.