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Signal and Image Processing for Remote Sensing (2nd Edition) by C.H. Chen

By C.H. Chen

Carrying on with within the footsteps of the pioneering first variation, sign and snapshot Processing for distant Sensing, moment version explores the main updated sign and picture processing tools for facing distant sensing difficulties. even if so much facts from satellites are in photo shape, sign processing can give a contribution considerably in extracting details from remotely sensed waveforms or time sequence information. This ebook combines either, supplying a special stability among the position of sign processing and photograph processing.

Featuring contributions from around the globe specialists, this publication keeps to stress mathematical methods. now not restricted to satellite tv for pc information, it additionally considers signs and pictures from hydroacoustic, seismic, microwave, and different sensors. Chapters hide very important themes in sign and picture processing and speak about ideas for facing distant sensing difficulties. each one bankruptcy deals an advent to the subject ahead of delving into study effects, making the publication obtainable to a extensive audience.

This moment variation displays the substantial advances that experience happened within the box, with 23 of 27 chapters being new or solely rewritten. assurance comprises new mathematical advancements akin to compressive sensing, empirical mode decomposition, and sparse illustration, in addition to new part research equipment equivalent to non-negative matrix and tensor factorization. The ebook additionally provides new experimental effects on SAR and hyperspectral photograph processing.

The emphasis is on mathematical thoughts that might a ways out live the speedily altering sensor, software program, and applied sciences. Written for business and educational researchers and graduate scholars alike, this publication is helping readers attach the "dots" in photo and sign processing.

New during this Edition
The moment version contains 4 chapters from the 1st version, plus 23 new or fullyyt rewritten chapters, and one hundred ninety new figures. New subject matters lined include:

-- Compressive sensing
-- The combined pixel challenge with hyperspectral images
-- Hyperspectral photograph (HSI) aim detection and type in response to sparse representation
-- An ISAR strategy for refocusing relocating ambitions in SAR images
-- Empirical mode decomposition for sign processing
-- characteristic extraction for category of distant sensing indications and images
-- lively studying equipment in category of distant sensing images
-- sign subspace identity of hyperspectral data
-- Wavelet-based multi/hyperspectral photograph recovery and fusion

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Extra info for Signal and Image Processing for Remote Sensing (2nd Edition)

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Time-Frequency/Time-Scale Analysis, Academic Press, San Diego, 1999. 5. Bedrosian, E. On the quadrature approximation to the Hilbert transform of modulated signals, Proc. IEEE, 51, 868–869, 1963. 6. Nuttall, A. H. On the quadrature approximation to the Hilbert transform of modulated signals, Proc. IEEE, 54, 1458–1459, 1966. 7. Huang, N. , Wu, M. , Long, S. , Shen, S. S. , Qu, W. , and Fan, K. L. A confidenc limit for the empirical mode decomposition and the Hilbert spectral analysis, Proc. of Roy.

The sinc filter is highly sensitive to nonuniform sampling points due to slow decay of the pulse. The rate of decay of the tails of the raised cosine pulse increases with the roll-off factor and this makes it less sensitive to sampling point errors [9]. However, increasing the roll-off factor also has the effect of increased filter bandwidth, thereby reducing its frequency resolution. So, to summarize, increasing the roll-off factor 0 through 1 has two adverse consequences: it decreases the sensitivity of the filter to sampling point errors, but simultaneously reduces its frequency resolution.

This would require knowing the physical size of the image in cm or some other unit and its equivalent in pixels from the array analyzed. 10 is to separate the original image into columns instead of rows. This would make the analysis more sensitive to structures that were better aligned with that direction, and also with the direction of wind and waves. 15. Component images 1 and 2 developed from the vertical column analysis were not included here, after they were found to contain results of such a short scale uniformly spread throughout the image, and without structure.

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