We describe a method to obtain the principal components of a multispectral image. It allows a simultaneous radiometric enhancement by means of a suitable finer level quantization that does not introduce artifacts. Using this method we are able to produce good photographic prints pf the principal components of LANDSAT MSS images. As a matter of fact the twn first components (that we call bands A...

Principal component analysis (PCA) is often used for analyzing data in the most diverse areas. In this work, we report an integrated approach to several theoretical and practical aspects of PCA. We start by providing, intuitive accessible manner, basic principles underlying PCA its applications. Next, present a systematic, though no exclusive, survey some representative works illustrating poten...

Ozone is one of the most severe air pollution problems in the world. The concentration of ozone in the troposphere is of great interest because of its negative influence on the human health, vegetation and materials. The complexity of ozone (O3) formation mechanisms in the troposphere, the complexity of meteorological conditions in urban areas and the uncertainty in the measurements of all the ...

The recent paper (Cheung 2001) has studied the blind identification of Gaussian source process through a general temporal independent component analysis (ICA) approach named dual autoregressive modelling. It is actually a temporal extension of the classical principal component analysis without considering the principal order of the components. In this paper, we will further show the identifiabl...

Solid State Nuclear Track Detectors (SSNTD) have been used as passive dosimeters for many decades. Fast neutron detection is performed by detecting recoil protons generated in a hydrogenous material (neutron converter). The dose estimated multiplying the track-spots density present on detector surface calibration coefficient obtained from reference irradiations. Therefore, one needs to automati...