Anomaly Compensation and Cloud Clearing of AIRS Hyperspectral Data
Choongyeun Cho
Invited seminar at the meeting of IEEE Geoscience and Remote Sensing Society (GRSS) Boston Section, August 2005
Abstract
Hyperspectral sensors observe hundreds or thousands of narrow contiguous spectral bands. The use of hyperspectral imagery for remote sensing applications is new and promising, yet the characterization and analysis of such data by exploiting both spectral and spatial information have not been extensively investigated thus far. A generic methodology is presented for detecting and compensating anomalies from hyperspectral imagery, taking advantage of all information available -- spectral and spatial correlation and any a priori knowledge about the anomalies. An anomaly is generally defined as an undesired spatial and spectral feature statistically different from its surrounding background. The main thrust of the research is development and evaluation of a general method that cooperatively combines spatial and spectral processing so that the signal-to-noise ratio (SNR) of an anomaly of interest is successively enhanced, thereby making it increasingly prominent, permitting its removal. Principal component analysis (PCA) and Iterative Order and Noise (ION) estimation algorithm provide valuable tools to characterize signal and reduce noise. Various methodologies are also addressed to cope with non-linearities in the system without much computational burden. An anomaly compensation technique is applied to specific problems that exhibit different stochastic models for an anomaly and its performance is evaluated. Hyperspectral anomalies dealt with in this study are (1) cloud impact in hyperspectral radiance fields, (2) faulty channels and (3) scan-line miscalibration. Estimation of the cloud impact using the proposed algorithm is especially highly successful and comparable to an alternative physics-based algorithm. Faulty channels and miscalibrated scan-lines are also fairly well compensated or removed using the proposed algorithm.
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