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With the availability of very high resolution multispectral imagery from sensors such as IKONOS and Quickbird, it is possible to identify small-scale features in urban environment. Because of the multiscale feature and diverse composition of land cover types found within the urban environment, the production of accurate urban land cover maps from high resolution satellite imagery is a difficult...

Recently, in classification of multispectral remote resensing image by using back-propagation neural network (BPNN), all bands of image must be used for training and classing. Disadvantage of the mentioned method not only requires more time for training and classing but also more complexity. In this paper, to decrease the mentioned disadvantage, principal component analysis (PCA) is applied to ...

In this paper we present a lossless coding scheme for multispectral images. The algorithm di ers from classical lossless approaches of multispectral image coding (1, 2, 3) in the fact that it is based on an independent coding of spectrally homogeneous regions. Regions that present a common multispectral signature are segmented. Then, spectral prediction is performed within these regions and nal...

P. Gouton University of Burgundy UFR Science and Technology LE2I UMR CNRS-5158 BP 4787

The ARSIS concept is meant to increase the spatial resolution of an image without modification of its spectral contents by merging structures extracted from a higher resolution image of the same scene but in a different spectral band. It makes use of wavelet transforms and multiresolution analysis. It is currently applied in an operational way with dyadic wavelet transforms that limit the mergi...

A new adaptive multispectral image compression technique based on the regions identified is proposed. The algorithm is adaptive in the sense that according to the data type class of the region, appropriate encoding technique is chosen. The image is first segmented by means of Region splitting and merging procedure based on the statistical characteristic of the image. Then class adaptive hotelli...

Multispectral images acquired by satellites are used to study phenomena on the Earth’s surface. Unsupervised classification techniques analyze multispectral image content without considering prior knowledge of the observed terrain; this is done using techniques which group pixels that have similar statistics of digital level distribution in the various image channels. In this paper, we propose ...

The coefficients of a weighted summation of red, green and blue intensities are shown to effect textural characteristics. Thus multispectral images can be mutated into forms that facilitate their discrimination by a given set of statistical texture measures.