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This paper highlights SSC San Diego contributions to the research and development of hyperspectral technology. SSC San Diego developed the real-time, onboard hyperspectral data processor for automated cueing of high-resolution imagery as part of the Adaptive Spectral Reconnaissance Program (ASRP), which demonstrated a practical solution to broad area search by leveraging hyperspectral phenomenology. The authors explain how the DARPA ASRP successfully demonstrated the capability to detect military targets of interest in real time by using an airborne hyperspectral system to cue high-resolution images for ground analysis. SSC San Diego is now implementing the ASRP algorithm suite on parallel processors, using a portable, scalable architecture that will be remotely accessible. SSC San Diego performed the initial demonstrations that led to the Littoral Airborne Sensor Hyperspectral (LASH) program, which applies hyperspectral imaging to the problem of submarine detection in the littoral zone. These sensors can perform a wide range of ocean sensing tasks. Targets range from submarines and sea mines for military applications, to chlorophyll and sediment load in physical oceanographic applications, to schools of dolphins and whales in marine biology applications. Hyperspectral systems such as LASH are being developed that use spectral and spatial processing algorithms to discern objects and organisms below the sea surface. The performance of such systems depends on environmental and optical properties of the sea. An instrument suite, the Portable Profiling Oceanographic Instrument System (PorPOIS), was developed to ascertain and quantify these environmental and hydro-optic conditions. Under the In-house Laboratory Independent Research (ILIR) program, SSC San Diego has developed new and enhanced methods for hyperspectral analysis and exploitation.
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Hyperspectral Imagery, or HSI, is a sophisticated, versatile intelligence gathering technology that could potentially enable the US military to make significant strides towards improving the preparation for and execution of its missions. Many of the difficulties in bringing the promise of HSI to fruition have very little to do with the technology itself. As will be discussed shortly, HSI technology has been successfully demonstrated in a variety of diverse applications. In point of fact, it is the versatility of HSI that may be hindering its implementation into the mainstream of the U.S. military's intelligence gathering capability. The objective of this paper is threefold. The first goal is to introduce the reader to both the technology itself and the myriad potential applications of Hyperspectral Imagery. The second goal is to realistically examine the challenges that HSI must overcome, specifically in the areas of how HSI fits into the world of joint vision, intelligence doctrine, and the intelligence cycle. Finally, the paper will provide a series of recommendations some focused on organizational issues and others on acquisition issues that will address the majority of the challenges faced by the intelligence community as they endeavor to incorporate an HSI capability into the U.S. intelligence community.
Hyperspectral Imagery, or HSI, is a sophisticated, versatile intelligence gathering technology that could potentially enable the US military to make significant strides towards improving the preparation for and execution of its missions. Many of the difficulties in bringing the promise of HSI to fruition have very little to do with the technology itself. As will be discussed shortly, HSI technology has been successfully demonstrated in a variety of diverse applications. In point of fact, it is the versatility of HSI that may be hindering its implementation into the mainstream of the U.S. military's intelligence gathering capability. The objective of this paper is threefold. The first goal is to introduce the reader to both the technology itself and the myriad potential applications of Hyperspectral Imagery. The second goal is to realistically examine the challenges that HSI must overcome, specifically in the areas of how HSI fits into the world of joint vision, intelligence doctrine, and the intelligence cycle. Finally, the paper will provide a series of recommendations some focused on organizational issues and others on acquisition issues that will address the majority of the challenges faced by the intelligence community as they endeavor to incorporate an HSI capability into the U.S. intelligence community.
Here's an up-to-date, comprehensive review of surveillance and reconnaissance (S & R) imaging system modeling and performance prediction. This new, one-of-a-kind resource helps you predict the information potential of new surveillance system designs, compare and select from alternative measures of information extraction, relate the performance of tactical acquisition sensors and surveillance sensors, and understand the relative importance of each element of the image chain on S& R system performance. It provides you with system descriptions and characteristics, S& R modeling history, and performance modeling details.
The aim of the Special Issue “Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences” was to present a selection of innovative studies using hyperspectral imaging (HSI) in different thematic fields. This intention reflects the technical developments in the last three decades, which have brought the capacity of HSI to provide spectrally, spatially and temporally detailed data, favoured by e.g., hyperspectral snapshot technologies, miniaturized hyperspectral sensors and hyperspectral microscopy imaging. The present book comprises a suite of papers in various fields of environmental sciences—geology/mineral exploration, digital soil mapping, mapping and characterization of vegetation, and sensing of water bodies (including under-ice and underwater applications). In addition, there are two rather methodically/technically-oriented contributions dealing with the optimized processing of UAV data and on the design and test of a multi-channel optical receiver for ground-based applications. All in all, this compilation documents that HSI is a multi-faceted research topic and will remain so in the future.
This book includes some very recent applications and the newest emerging trends of hyper-spectral imaging (HSI). HSI is a very recent and strange beast, a sort of a melting pot of previous techniques and scientific interests, merging and concentrating the efforts of physicists, chemists, botanists, biologists, and physicians, to mention just a few, as well as experts in data crunching and statistical elaboration. For almost a century, scientific observation, from looking to planets and stars down to our own cells and below, could be divided into two main categories: analyzing objects on the basis of their physical dimension (recording size, position, weight, etc. and their variations) or on how the object emits, reflects, or absorbs part of the electromagnetic spectrum, i.e., spectroscopy. While the two aspects have been obviously entangled, instruments and skills have always been clearly distinct from each other. With HSI now available, this is no longer the case. This instrument can return specimen dimensionalities and spectroscopic properties to any single pixel of your specimen, in a single set of data. HSI modality is ubiquitous and scale-invariant enough to be used to mark terrestrial resources on the basis of a land map obtained from satellite observation (actually, the oldest application of this type) or to understand if the cell you are looking at is cancerous or perfectly healthy. For all these reasons, HSI represents one of the most exciting methodologies of the new millennium.
Hyperspectral Imaging: Techniques for Spectral Detection and Classification is an outgrowth of the research conducted over the years in the Remote Sensing Signal and Image Processing Laboratory (RSSIPL) at the University of Maryland, Baltimore County. It explores applications of statistical signal processing to hyperspectral imaging and further develops non-literal (spectral) techniques for subpixel detection and mixed pixel classification. This text is the first of its kind on the topic and can be considered a recipe book offering various techniques for hyperspectral data exploitation. In particular, some known techniques, such as OSP (Orthogonal Subspace Projection) and CEM (Constrained Energy Minimization) that were previously developed in the RSSIPL, are discussed in great detail. This book is self-contained and can serve as a valuable and useful reference for researchers in academia and practitioners in government and industry.
Techniques and Applications of Hyperspectral Image Analysis gives an introduction to the field of image analysis using hyperspectral techniques, and includes definitions and instrument descriptions. Other imaging topics that are covered are segmentation, regression and classification. The book discusses how high quality images of large data files can be structured and archived. Imaging techniques also demand accurate calibration, and are covered in sections about multivariate calibration techniques. The book explains the most important instruments for hyperspectral imaging in more technical detail. A number of applications from medical and chemical imaging are presented and there is an emphasis on data analysis including modeling, data visualization, model testing and statistical interpretation.