Many researchers are concerning with using powerful image processing tools to achieve high quality images for their applications. Recently, great interest has been arisen on using wavelet transforms [1-10] to analysis multi-resolution images and to fuse remote sensing images. Image fusion especially in remote sensing applications is one of the fields that growing continuously. Many methods have...

. Optical spectral reflectance and multi–spectral image analysis techniques were investigated to characterize chicken hearts for real–time disease detection. Spectral signatures of five categories of chicken hearts (airsacculitis, ascites, normal, cadaver, and septicemia) were obtained from optical reflectance measurements taken with a visible/near–infrared spectroscopic system in the range 473...

Using data fusion of multi-spectral and microwave radar images, a semiautomatic method is developed based on the level set method for application to urban road extraction. The fast marching method of level set makes data fusion. Radar remote sensing image can make up road breaks due to shadowing of high building or tree canopy in multi-spectral image, while multi-spectral image can be of helpfu...

We study spectral triples on (weighted) groups and consider functors between the categories of weighted groups and spectral triples. We study the properties of weights and the corresponding functor for spectral triples coming from discrete weighted groups.

With the development of remote sensing technology, remote sensed data provide detailed information of structure and spectral about ground scenes. However, the traditional classification methods only take objects’ spectral information into consideration but neglect their structural information. There are two major reasons for this: First, the multi-spectral data contain a lot of information on t...

The goal of multi-image classification is to identify and label "similar regions" within a scene. The ability to correctly classify a remotely sensed multi-image of a scene is affected by the ability of the classification process to adequately compensate for the effects of atmospheric variations and sensor anomalies. Better classification may be obtained if the multi-image is preprocessed befor...

A vector segmentation of multi-spectral MSG2 images, based on the Markov formalism, is presented in this paper. The use of Markov fields take into account space and spectral interactions between the pixels of multi-component image. The use of these models requires an initial choice of their parameters obtained by algorithms of segmentation which can generate additional processing times. To avoi...

A new nonlinear dimensionality reduction method for multi-spectral images was presented to solve the problem brought by high dimensionality of multi-spectral images during color reproduction. Firstly, according to the characteristics of human visual system, the CIE standard observer color matching functions were weighted to the source spectral reflectance and then a principal component analysis...

This paper addresses the problem of mixing spectral bands and space in a smooth, linear, and casual manner. Such properties are of fundamental importance for many basic multi-spectral-image processing algorithms that probe inter-spectral structure. The example of color images is used in a simple spectral perspective. An example is given comparing the deep structure of the color and the gray gra...