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Earth observing satellites usually not only take ordinary red-green-blue images, but provide several images including the near-infrared and infrared spectrum. These images are called multispectral, for about four to seven different bands, or hyperspectral, for higher dimensional images of up to 210 bands. The drawback of the additional spectral information(More)
—The goal of pan-sharpening is to fuse a low spatial resolution multispectral image with a higher resolution panchro-matic image to obtain an image with high spectral and spatial resolution. The Intensity-Hue-Saturation (IHS) method is a popular pan-sharpening method used for its efficiency and high spatial resolution. However, the final image produced(More)
Pan-sharpening combines a low-resolution color multispectral image with a high-resolution grayscale panchromatic image to create a high-resolution fused color image. In this paper we examine five different pan-sharpening methods: IHS, PCA, Wavelet fusion, P+XS, and VWP and evaluate their effectiveness. Additionally, we propose an extension to the IHS(More)
Linear barcodes—the ubiquitous alternating black and white stripes whose relative widths encode information—are used in shipping, tracking, and identification and come in more than 250 varieties. The most familiar is probably the UPC barcode used in supermarkets; based on their grocery-shopping experience, most readers probably consider the reading of(More)
In the past there has been little study to determine an optimal dimension reduction algorithm on hyperspectral images. In this paper we investigate the performance of different dimension reduction algorithms including PCA and Isomap using various hyperspectral tasks to compare them. We considered runtime, classification, anomaly dection, target detection(More)
Because hyperspectral imagery is generally low resolution, it is possible for one pixel in the image to contain several materials. The process of determining the abundance of representative materials in a single pixel is called spectral unmixing. We discuss the L1 unmixing model and fast computational approaches based on Bregman iteration. We then use the(More)
There has been significant research on pan-sharpening multispectral imagery with a high resolution image, but there has been little work extending the procedure to high dimensional hyperspectral imagery. We present a wavelet-based variational method for fusing a high resolution image and a hyperspectral image with an arbitrary number of bands. To ensure(More)
Given discrete event data, we wish to produce a probability density that can model the relative probability of events occurring in a spatial region. Common methods of density estimation, such as Kernel Density Estimation, do not incorporate geographical information. Using these methods could result in non-negligible portions of the support of the density in(More)
We discuss the problem of interpolating visually acceptable images at a higher resolution. We first present the interpolation problem and why linear interpolation filters are inadequate for image data. To represent the major mathematical approaches to image processing, we discuss and evaluate five different image interpolation methods. First, we present a(More)