In this work we consider six methods for automatic white balance available in the literature. The idea investigated does not rely on a single method, but instead considers a consensus decision that takes into account the compendium of the responses of all the considered algorithms. Combining strategies are then proposed and tested both on synthetic and multispectral images, extracted from well ...

A method for the automatic detection of buildings and their roof planes from LIDAR data and multispectral images is presented. For building detection, a classification technique is applied in a hierarchic way to overcome the problems encountered in areas of heterogeneous appearance of buildings. The detection of roof planes is based on a region growing algorithm applied to the LIDAR data, the s...

We carry out low bit-rate compression of multispectral images by means of the Said and Pearlman’s SPIHT algorithm, suitably modified to take into account the interband dependencies. Two techniques are proposed: in the first, a three-dimensional (3-D) transform is taken (wavelet in the spatial domain, Karhunen–Loeve in the spectral domain) and a simple 3-D SPIHT is used; in the second, after tak...

A new technique for denoising and compression of multispectral satellite images to remove the effect of noise on the compression process is presented. One type of multispectral images has been considered: Landsat Enhanced Thematic Mapper Plus. The discrete wavelet transform (DWT), the dual-tree DWT, and a simple Huffman coder are used in the compression process. Simulation results show that the...

A new approach for generating high-speed multispectral images has been developed. The central concept is that spectra can be acquired for each pixel in a confocal spatial scan by using a fast spectrometer based on optical fiber delay lines. This concept merges fast spectroscopy with standard spatial scanning to create datacubes in real time. The spectrometer is based on a serial array of reflec...

In this paper we study the application of bayesian network models to classify multispectral and hyperspectral remote sensing images. Different models of bayesian networks as: Naive Bayes, Tree Augmented Naive Bayes, Forest Augmented Naive Bayes and General Bayesian Networks, are applied in the classification of hyperspectral data. In addition, several bayesian multi-net models are applied in th...

Remote sensing has become an unavoidable tool for better managing our environment, generally by realizing maps of land cover using classification techniques. Traditional classification techniques assign only one class (e.g., water, soil, grass) to each pixel of remote sensing images. However, the area covered by one pixel contains more than one surface component and results in the mixture of th...