The image grand tour is a method for visualizing multispectral or multiple registered images. In many settings, several registered images of the same scene are collected. This most often happens when multispectral images are collected, but may happen in other settings as well. A multispectral image can be viewed as an image in which each pixel has a multivariate vector attached. The desired goa...

Multispectral image analysis is a relatively promising field of research with applications in several areas, such as medical imaging and satellite monitoring. However, a considerable number of current methods of analysis are based on parametric statistics. Alternatively, some methods in Computational Intelligence are inspired by biology and other sciences. Here we claim that Philosophy can be a...

An application of multispectral imaging in multimedia is presented. We describe a system for the acquisition of multispectral images using a CCD camera with carefully selected optical filters. We further discuss how the surface spectral reflectance of the imaged objects may be reconstructed from the camera response. We then present an application where the acquired multispectral images are used...

Multispectral image reconstruction allows to combine a multispectral low resolution image with a panchromatic high resolution one to obtain a new multispectral image with the spectral properties of the lower resolution image and the level of detail of the higher resolution one. In this paper we formulate the problem of multispectral image reconstruction based on super-resolution techniques and ...

This thesis describes the study of Artificial Neural Network (ANN) based techniques for the classification of aerial images for various types of land-use. In this study both gray-scale and multispectral aerial images were used in land-use classification. Three approaches were used for the preparation of the data as inputs to the ANN, including histograms of the pixel intensities, textural param...

Most of the satellite sensors, presently operating in the optical domain, are providing a data set comprising multispectral images at a low spatial resolution and images at a higher spatial resolution but with a lower spectral content. The trend of satellite sensors is similar to the present situation. The idea of fusing multispectral images with a highest spatial resolution enables the creatio...

In this paper we present a new approach in the compression of multispectral images. It is based on the merging of two main tendencies such as the use of KLT as a spectral decorrelator and object based image coding schemes. The use of the principal component in multispectral imagery is described and used to perform a multispectral segmenta-tion. This segmentation is taken as the basis for a spec...

Fully Constrained Least Squares (FCLS) has been widely used and proven to be a powerful tool for hyperspectral image classification. But for multispectral remote sensing images with only a few bands, the Least-Squares based approaches will all encounter the band number constraint (BNC), which requires the number of bands should be no less than the number of classes. In this paper, we proposed a...

Multispectral images consist of multiple channels, each containing data acquired from a different band within the frequency spectrum. Since most objects emit or reflect energy over a large spectral bandwidth, there usually exists a significant correlation between channels. Due to often harsh imaging environments, the acquired data may be degraded by both blur and noise. Simply applying a monoch...

In the paper is presented one new approach for efficient decorrelation of multispectral images, based on hierarchical adaptive Principal Component Analysis. The research is aimed at the efficient compression of multispectral images (MS), whose resolution and band number in some application areas are extremely high. Together with this, also grows up the needed volume of the corresponding image d...