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Optical remote sensing involves acquisition and analysis of optical data – electromagnetic radiation captured by the sensing modality after reflecting off an area of interest on ground.  Optical image acquisition modalities have come a long way – from gray-scale photogrammetric images to hyperspectral images. The advances in imaging hardware over recent decades have enabled availability of high spatial, spectral and temporal resolution imagery to the remote sensing analyst. These advances have created unique challenges for researchers in the remote sensing community working on algorithms for representation, exploitation and analysis of such data. Early optical remote sensing systems relied on multispectral sensors, which are characterized by a small number of wide spectral bands. Although multispectral sensors are still employed by analysts, in recent years, the remote sensing community has seen a steady shift to hyperspectral sensors, which are characterized by hundreds of fine resolution co-registered spectral bands, as the dominant optical sensing technology. Such data has the potential to reveal the underlying phenomenology as described by spectral characteristics accurately. This “extension” from multispectral to hyperspectral imaging does not imply that the signal processing and exploitation techniques can be simply scaled up to accommodate the extra dimensions in the data. This book presents state-of-the-art signal processing and exploitation algorithms that address three key challenges within the context of modern optical remote sensing: (1) Representation and visualization of high dimensional data for efficient and reliable transmission, storage and interpretation; (2) Statistical pattern classification for robust land-cover-classification, target recognition and pixel unmixing; (3) Fusion of multi-sensor data to effectively exploit multiple sources of information for analysis.

Image processing -- Digital techniques. --- Optical instruments -- United States. --- Optical instruments.. --- Remote sensing. --- Signal processing. --- Electrical & Computer Engineering --- Engineering & Applied Sciences --- Applied Physics --- Electrical Engineering --- Telecommunications --- Optical instruments. --- Image processing. --- Pictorial data processing --- Picture processing --- Processing, Image --- Processing, Signal --- Optics --- Remote-sensing imagery --- Remote sensing systems --- Remote terrain sensing --- Sensing, Remote --- Terrain sensing, Remote --- Instruments --- Engineering. --- Pattern recognition. --- Microwaves. --- Optical engineering. --- Signal, Image and Speech Processing. --- Pattern Recognition. --- Microwaves, RF and Optical Engineering. --- Imaging systems --- Optical data processing --- Information measurement --- Signal theory (Telecommunication) --- Physical instruments --- Aerial photogrammetry --- Aerospace telemetry --- Detectors --- Space optics --- Optical pattern recognition. --- Hertzian waves --- Electric waves --- Electromagnetic waves --- Geomagnetic micropulsations --- Radio waves --- Shortwave radio --- Pattern perception --- Perceptrons --- Visual discrimination --- Speech processing systems. --- Mechanical engineering --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Computational linguistics --- Electronic systems --- Information theory --- Modulation theory --- Oral communication --- Speech --- Telecommunication --- Singing voice synthesizers

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Optical remote sensing involves acquisition and analysis of optical data - electromagnetic radiation captured by the sensing modality after reflecting off an area of interest on ground.  Optical image acquisition modalities have come a long way - from gray-scale photogrammetric images to hyperspectral images. The advances in imaging hardware over recent decades have enabled availability of high spatial, spectral and temporal resolution imagery to the remote sensing analyst. These advances have created unique challenges for researchers in the remote sensing community working on algorithms for representation, exploitation and analysis of such data. Early optical remote sensing systems relied on multispectral sensors, which are characterized by a small number of wide spectral bands. Although multispectral sensors are still employed by analysts, in recent years, the remote sensing community has seen a steady shift to hyperspectral sensors, which are characterized by hundreds of fine resolution co-registered spectral bands, as the dominant optical sensing technology. Such data has the potential to reveal the underlying phenomenology as described by spectral characteristics accurately. This extension  from multispectral to hyperspectral imaging does not imply that the signal processing and exploitation techniques can be simply scaled up to accommodate the extra dimensions in the data. This book presents state-of-the-art signal processing and exploitation algorithms that address three key challenges within the context of modern optical remote sensing: (1) Representation and visualization of high dimensional data for efficient and reliable transmission, storage and interpretation; (2) Statistical pattern classification for robust land-cover-classification, target recognition and pixel unmixing; (3) Fusion of multi-sensor data to effectively exploit multiple sources of information for analysis.

Mathematical statistics --- Spectrometric and optical chemical analysis --- Computer. Automation --- patroonherkenning --- beeldverwerking --- factoranalyse --- remote sensing --- signal processing --- signaalverwerking --- spectrometrie