In this paper, we present a novel approach for automatically detecting buildings from multiple heterogeneous and uncalibrated very high-resolution (VHR) satellite images for a rapid response to natural disasters. In the proposed method, a simple and efficient visual attention method is first used to extract built-up area candidates (BACs) from each multispectral (MS) satellite image. After this...

For the years 1997 to 2000 it is expected that a number of new satellites will be launched into orbit by private companies which are speciied to deliver panchromatic imagery of the earth surface with a spatial resolution as ne as 1 m. In contrast to the panchromatic band, the spectrally resolved bands will have a four times coarser ground resolution. Therefore, image fusion algorithms will cert...

In this paper, we present an adaptive variational segmentation algorithm of spectral-texture regions in satellite images using level set. Satellite images contain both textured and non-textured regions, so for each region cues of spectral and texture are integrated according to their discrimination power. Motivated by Fisher-Rao’s linear discriminant analysis, two region’s weights are defined t...

The use of new, high-resolution spectral sensors, in contrast with low-resolution, increases both the amount of information acquired about land cover at local scales, and the geometric detail and accuracy. However, the algorithms needed for high-resolution image processing are more complex than those needed for their low-resolution counterparts. This paper's main objective is to present a new m...

This paper proposes a novel spatial and spectral fusion method for satellite multispectral and hyperspectral (or high-spectral) images based on dictionary-pair learning. By combining the spectral information from sensors with low spatial resolution but high spectral resolution (LSHS) and the spatial information from sensors with high spatial resolution but low spectral resolution (HSLS), this m...