Hyperspectral optimization process exemplar, a&bb-based K model, and a water column correction model are used to process the hyperspectral data for detecting the subtle spectral difference of coral reefs. The water column correction model only tracks those effective photons by fully considering the geometrical distribution of the light field. The adaptivity of the parameters and models to the in situ data collected in Sanya Bay is evaluated. The modeled and uncorrected spectra are examined separately to reflect the coral reflectance, and the coefficients of determination for the relationships drops from 0.90 to 0.05. The retrieved bottom reflectance for 70 corals (Acropora, Porites) exhibited the classic chlorophyll features. The reflectance at 700 nm collected in Sanya Bay is relatively lower than the results conducted by other researchers. Peak ratio index and derivative analysis are utilized and are proved to be effective for coral reef classification and coral healthy assessment.

A novel personal recognition system utilizing palm vein patterns and a novel technique to analyze these vein patterns is presented. The technique utilizes the curvelet transform to extract features from vein patterns to facilitate recognition. This technique provides optimally sparse representations of objects along the edges. Principal component analysis (PCA) is applied on curvelet-decomposed images for dimensionality reduction. A simple distance-based classifier, such as the nearest-neighbor (NN) classifier, is employed. The experiments are performed using our palm vein database. Experimental results show that the algorithm reaches a recognition accuracy of 99.6% on the database of 500 distinct subjects.