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Titlebook: Optical Remote Sensing; Advances in Signal P Saurabh Prasad,Lori M. Bruce,Jocelyn Chanussot Book 2011 Springer-Verlag Berlin Heidelberg 201

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Recent Developments in Endmember Extraction and Spectral Unmixing,hyperspectral scenes obtained by different imaging spectrometers are used to provide a quantitative and comparative algorithm assessment. To address the computational requirements introduced by hyperspectral imaging algorithms, the chapter also includes a parallel processing example in which the per
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Change Detection in VHR Multispectral Images: Estimation and Reduction of Registration Noise Effectlar domain. Experimental results obtained on simulated and real VHR multitemporal images confirm the validity of the proposed analysis on RN, the reliability of the derived properties and the effectiveness of the proposed techniques for the estimation of RN distribution and change detection.
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Fusion of Optical and SAR Data for Seismic Vulnerability Mapping of Buildings, Fusion between optical and radar data allows covering the widest range of information pieces; in this chapter we will describe how such information may be extracted and how it can be profitably fed to simplified seismic vulnerability models to assign a seismic vulnerability class to each building.
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Effects of the Spatial Enhancement of Hyperspectral Images on the Distribution of Spectral Classes,ed to original Hyperion HS data (30 m) and to fusion products with the panchromatic channel of ALI (10 m) obtained from different sharpening methods, in order to evaluate possible advantages or critical situations deriving from multi-sensor, multi-resolution data fusion.
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Book 2011 remotely sensed data.Challenges in pre-processing images, storing and representing high dimensional data, fusing different sensor modalities, pattern classification and target recognition, visualization of high dimensional imagery.
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Hyperspectral Data Compression Tradeoff,estigated in the present chapter and the different possible tradeoffs are explored. In conclusion, we see that a number of challenges remain, of which the most important is to find an easier way to qualify the different algorithm proposals.
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Decision Fusion of Multiple Classifiers for Vegetation Mapping and Monitoring Applications by Means the weak classifiers. A comparison is also done with the simple majority voting scheme of the multiple classifiers. Our experiments clearly show the improvement of classification accuracy using the proposed fusion techniques.
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