One of the aims of our research team is to develop algorithms to assign automatically a crop to a cadastral parcel, matching raster information (multispectral classified images) and vectorial information (polygons defining parcel borderlines). The use of multispectral images with high spatial resolution would assist these assignations. In this work, new image-fusion methods are presented and de...

An innovative approach to spectral pattern recognition for multispectral images based on Genetic Programming is introduced. The problem is faced in terms of unsupervised pixel classification. The system is tested on a multispectral image with 31 spectral bands and 256 · 256 pixels. A good quality clustered output image is obtained.

This paper addresses the principles, implementation and evaluation of wavelet transformation based image fusion. 2-D discrete wavelet transformation is presented concisely to facility the understanding of the wavelet based image fusion method. To best retain the quality of the input images, we propose a strategy that minimizes the necessary resampling operations to limit potential image quality...

Remote sensing image fusion is an effective way to use a large volume of data from multisensor images. Most earth satellites such as SPOT, Landsat 7, IKONOS and QuickBird provide both panchromatic (Pan) images at a higher spatial resolution and multispectral (MS) images at a lower spatial resolution and many remote sensing applications require both high spatial and high spectral resolutions, es...

In remote sensing applications, lower spatial resolution multispectral images are fused with higher spatial resolution panchromatic ones. The objective of this fusion process is to enhance the spatial resolution of the multispectral images to make important features more apparent for human or machine perception. This enhancement is performed by injecting the high frequency component of the panc...

Forests can be efficiently monitored by automatic semantic segmentation of trees using satellite and/or aerial images. Still, several challenges make the problem difficult, including varying spectral signature different trees, lack sufficient labelled data, and geometrical occlusions. In this paper, we address tree multispectral imagery. While carry out large-scale experiments on deep learning ...

Remote sensing multispectral image compression encoder requires low complexity, high robust, and high performance because it usually works on the satellite where the resources, such as power, memory, and processing capacity, are limited. For multispectral images, the compression algorithms based on 3D transform (like 3D DWT, 3D DCT) are too complex to be implemented in space mission. In this pa...