Hyperspectral data visualizations are useful as a background layer to labeling information in the hyperspectral scene such as classification information, locations, or geographic features. Given a hyperspectral image H , where the ith-jth pixel Hij is a d-dimensional vector representing reflectance at d wavelengths, any dimensionality-reduction method an be used to reduce the d dimensions down ...

Photorealistic 3D models, representing an object’s surface geometry textured with conventional photography, are used for visualization, interpretation and spatial measurement in many disparate fields, such as cultural heritage, archaeology and throughout the earth sciences, including geology. Virtual models of geological outcrops allow for large quantities of geometric data, such as sizes of fe...

DISCLAIMER This document describes work undertaken as part of a programme of study at the Faculty of Geo-Information Science and Earth Observation of the University of Twente. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the Faculty. i ABSTRACT This research work concentrates on understanding the concepts of hyp...

Hyperspectral image mostly have very large amounts of data which makes the computational cost and subsequent classification task a difficult issue. Firstly, to solve the problem of computational complexity, spectral clustering algorithm is imported to select efficient bands for subsequent classification task. Secondly, due to lack of labeled training sample points, this paper proposes a new alg...

The abundant information available in hyperspectral image has provided important opportunities for land-cover classification and recognition. However, “Curse of dimensionality” and small training sample set are two difficulties which hinder the improvement of computational efficiency and classification precision. In this paper, we present a co-training based method on hyperspectral image classi...

This article presents the scientific outcomes of 2022 Hyperspectral Pansharpening Challenge organized by 12th IEEE Workshop on Image and Signal Processing: Evolution in Remote Sensing (IEEE WHISPERS 2022). The aims at fusing a panchromatic image with hyperspectral data to get high spatial resolution cube same while preserving spectral information data. Four datasets acquired PRISMA mission owne...

Feature selection especially band selection plays important roles in hyperspectral remote sensed image processing. It is worth nothing that band selection approaches need to be combined with image spatial structure information so as to select valid bands and improve the performance. But all of the existing remote sensing data processing algorithms are used for the conventional broadband spectra...

This book focuses on the super resolution of images and video. The authors’ use of the term super resolution (SR) is used to describe the process of obtaining a high resolution (HR) image, or a sequence of HR images, from a set of low resolution (LR) observations. This process has also been referred to in the literature as resolution enhancement (RE). SR has been applied primarily to spatial an...

High spatial resolution hyperspectral data often used in precision farming applications are not available from current satellite sensors, and difficult or expensive to acquire from standard aircraft. Alternatively, in precision farming, unmanned aerial vehicles (UAVs) are emerging as lower cost and more flexible means to acquire very high resolution imagery. Miniaturized hyperspectral sensors h...