Satellite Image Fusion in Various Domains

In order to find out the fusion algorithm which is best suited for the panchromatic and multispectral images, fusion algorithms, such as PCA and wavelet algorithms have been employed and analyzed. In this paper, performance evaluation criteria are also used for quantitative assessment of the fusion performance. The spectral quality of fused images is evaluated by the ERGAS and Q4. The analysis indicates that the DWT fusion scheme has the best definition as well as spectral fidelity, and has better performance with regard to the high textural information absorption. Therefore, as the study area is concerned, it is most suited for the panchromatic and multispectral image fusion. an image fusion algorithm based on wavelet transform is proposed for Multispectral and panchromatic satellite image by using fusion in spatial and transform domains. In the proposed scheme, the images to be processed are decomposed into sub-images with the same resolution at same levels and different resolution at different levels and then the information fusion is performed using high-frequency sub-images under the Multi-resolution image fusion scheme based on wavelets produces better fused image than that by the MS or WA schemes

Wavelet Based on Satellite Image Resolution Enhancement

Satellite images are being used in many fields of research. Satellite images are being used in many applications like Meteorology, Agriculture, Geology, Forestry, Landscape, Biodiversity, Planning, Instruction, Area and oceanography. The Image Enhancement is the main technique for improving the resolution and visual appearance of the image. One of the major issues in Image Enhancement is Wavelet Transform. The Wavelet Transform is the technique which decomposes an image into a set of basic functions called Wavelets. A new satellite image resolution enhancement technique based on the interpolation of the high-frequency sub-band images obtained by discrete wavelet transform (DWT) and the input image. DWT is applied in order to decompose an input image into dissimilar sub-bands. Then the high frequency sub-bands as well as the input image are interpolated. All these sub-bands are combined to generate a new high resolution image by using inverse DWT (IDWT). The quantitative peak signal-to-noise ratio (PSNR) and root mean square error (RMSE) and visual results show the superiority of the proposed technique over the conventional bicubic interpolation, wavelet zero padding and state-of-art image resolution enhancement techniques

Use of the SRI Satellite Image Analyzer Console for Mapping Southern Arizona Plant Communities from ERTS-1 Imagery

SRI satellite image analyzer console for mapping southern Arizona plant communities from ERTS-1 imager

Satellite image analysis using neural networks

The tremendous backlog of unanalyzed satellite data necessitates the development of improved methods for data cataloging and analysis. Ford Aerospace has developed an image analysis system, SIANN (Satellite Image Analysis using Neural Networks) that integrates the technologies necessary to satisfy NASA's science data analysis requirements for the next generation of satellites. SIANN will enable scientists to train a neural network to recognize image data containing scenes of interest and then rapidly search data archives for all such images. The approach combines conventional image processing technology with recent advances in neural networks to provide improved classification capabilities. SIANN allows users to proceed through a four step process of image classification: filtering and enhancement, creation of neural network training data via application of feature extraction algorithms, configuring and training a neural network model, and classification of images by application of the trained neural network. A prototype experimentation testbed was completed and applied to climatological data

Computer-aided Land Cover Mapping Protocol

The purpose of the resource is to produce a land cover type map from the digital file of a Landsat satellite image using MultiSpec software. Educational levels: Middle school, High school