Evaluation Of Two Applications Of Spectral Mixing Models To Image Fusion

Many applications in remote sensing require merging low-resolution multispectral or hyperspectral images with high-resolution panchromatic images to create high-resolution multispectral or hyperspectral material maps. A number of methods are currently in use to produce such hybrid imagery. Until now, these methods have only been evaluated independently, and have not been compared to one another to determine an optimum method. This research performed a quantitative test of three image fusion procedures. The first method involves first sharpening low-resolution multispectral data using the panchromatic image, to produce a high-resolution multispectral image. This image was then separated into a series of high-resolution images which provided a mapping of materials within the scene. The second method involved first separating the low-resolution multispectral data into a series of material maps using a recently developed adaptive unmixing algorithm. These maps, along with the panchromatic image, were used to produce high-resolution material maps. The final method examined involved creating the low-resolution material maps using traditional image-wide unmixing methods. The resulting images, along with the panchromatic image, were used to produce sharpened material maps. These three image fusion procedures were evaluated for their radiometric and unmixing accuracy. It is hoped that the optimum method identified by this research will enable analysts to more easily and accurately produce high-resolution material maps for various applications.

Written using clear and accessible language, this useful guide discusses fundamental concepts and practices of multi-resolution image fusion.

Remote sensing and geographical information science (GIS) have advanced considerably in recent years. However, the potential of remote sensing and GIS within the environmental sciences is limited by uncertainty, especially in connection with the data sets and methods used. In many studies, the issue of uncertainty has been incompletely addressed. The situation has arisen in part from a lack of appreciation of uncertainty and the problems it can cause as well as of the techniques that may be used to accommodate it. This book provides general overviews on uncertainty in remote sensing and GIS that illustrate the range of uncertainties that may occur, in addition to describing the means of measuring uncertainty and the impacts of uncertainty on analyses and interpretations made. Uncertainty in Remote Sensing and GIS provides readers with comprehensive coverage of this largely undocumented subject: * Relevant to a broad variety of disciplines including geography, environmental science, electrical engineering and statistics * Covers range of material from base overviews to specific applications * Focuses on issues connected with uncertainty at various points along typical data analysis chains used in remote sensing and GIS Written by an international team of researchers drawn from a variety of disciplines, Uncertainty in Remote Sensing and GIS provides focussed discussions on topics of considerable importance to a broad research and user community. The book is invaluable reading for researchers, advanced students and practitioners who want to understand the nature of uncertainty in remote sensing and GIS, its limitations and methods of accommodating it.

Remote Sensing deals with the fundamental ideas underlying the rapidly growing field of remote sensing. John Schott explores energy-matter interaction, radiation propagation, data dissemination, and described the tools and procedures required to extract information from remotely sensed data using the image chain approach. Organizations and individuals often focus on one aspect of the remote sensing process before considering it as a whole, thus investigating unjustified effort, time, and expense to get minimal improvement. Unlike other books on the subject, Remote Sensing treats the process as a continuous flow. Schott examines the limitations obstructing the flow of information to the user, employing numerous applications of remote sensing to earth observation disciplines. For this second edition, in addition to a thorough update, there are major changes and additions, such as a much more complete treatment of spectroscopic imaging, which has matured dramatically in the last ten years, and a more rigorous treatment of image processing with an emphasis on spectral image processing algorithms. Remote Sensing is an ideal first text in remote sensing for advanced undergraduate and graduate students in the physical or engineering sciences, and will also serve as a valuable reference for practitioners.

With the exponential increase in computing power and broad proliferation of digital cameras, super-resolution imaging is poised to become the next "killer app." The growing interest in this technology has manifested itself in an explosion of literature on the subject. Super-Resolution Imaging consolidates key recent research contributions from eminent scholars and practitioners in this area and serves as a starting point for exploration into the state of the art in the field. It describes the latest in both theoretical and practical aspects of direct relevance to academia and industry, providing a base of understanding for future progress. Features downloadable tools to supplement material found in the book Recent advances in camera sensor technology have led to an increasingly larger number of pixels being crammed into ever-smaller spaces. This has resulted in an overall decline in the visual quality of recorded content, necessitating improvement of images through the use of post-processing. Providing a snapshot of the cutting edge in super-resolution imaging, this book focuses on methods and techniques to improve images and video beyond the capabilities of the sensors that acquired them. It covers: History and future directions of super-resolution imaging Locally adaptive processing methods versus globally optimal methods Modern techniques for motion estimation How to integrate robustness Bayesian statistical approaches Learning-based methods Applications in remote sensing and medicine Practical implementations and commercial products based on super-resolution The book concludes by concentrating on multidisciplinary applications of super-resolution for a variety of fields. It covers a wide range of super-resolution imaging implementation techniques, including variational, feature-based, multi-channel, learning-based, locally adaptive, and nonparametric methods. This versatile book can be used as the basis for short courses for engineers and scientists, or as part of graduate-level courses in image processing.

As remote sensing data and methods have become increasingly complex and varied - and increasingly reliable - so have their uses in forest management. New algorithms have been developed in virtually every aspect of image analysis, from classification to enhancements to estimating parameters. Remote Sensing for Sustainable Forest Management reviews t

This book constitutes the refereed conference proceedings of the 13th IFIP WG 6.11 Conference on e-Business, e-Services and e-Society, I3E 2014, held in Sanya, China, in November 2014. The 32 revised full papers presented were carefully reviewed and selected from 42 submissions. They are organized in the following topical sections: digital services, digital society, and digital business.