Lossy compression of AVIRIS hyperspectral images is considered. An automatic approach to selection of compression parameters depending on noise characteristics in component images is proposed. Several ways of performing lossy compression are discussed and compared. It is shown that in order to minimize distortions and provide a sufficient compression ratio it is reasonable to group the channels according to the evaluated noise variances in subband images and depending upon the sensor that produces sets of subband images. It is shown that for real life images the attained compression ratios can be of the order 8. ..25.

This study proposes an interband version of the linear prediction approach for hyperspectral images. Linear prediction represents one of the best performing and most practical and general purpose lossless image compression techniques known today. The interband linear prediction method consists of two stages: predictive decorrelation producing residuals, and entropy coding of these residuals. Our method achieved an average compression ratio of 3.23 using 13 airborne visible/infrared imaging spectrometer (AVIRIS) images. © 2003 Society of Photo-Optical Instrumentation Engineers.

Counting and tracking fish populations is important for conservation purposes as well as for the fishing industry. Various non-invasive automatic fish counters exist based on such principles as resistivity, light beams and sonar. However, such methods typically cannot make distinction between fish and other passing objects, and moreover, cannot recognize different species. Computer vision techniques provide an attractive alternative for building a more robust and versatile fish counting systems. In this paper we present the fish detection framework for noisy videos captured in water with low visibility. For this purpose, we compare three background subtraction methods for the task. Moreover, we propose necessary post-processing steps and heuristics to detect the fish and separate them from other moving objects. The results showed that by choosing an appropriate background subtraction method, it is possible to achieve a satisfying detection accuracy of 80% and 60% for two challenging datasets. The proposed method will form a basis for the future development of fish species identification methods.