Image Segmentation Phd Thesis 2010

Image Segmentation Phd Thesis 2010-57
The embedded processor completes the EM algorithm and also controls image data transmission between host computer and on-board memory.The whole system has been implemented on Xilinx Virtex 6 FPGA and achieved over 100 times processing improvement compared to standard desktop computer.

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The dataset was captured over Samford Ecological Research Facility (SERF), Queensland, Australia. Spectral-Spatial Classification of Hyperspectral Remote Sensing Images [B1] M.

The dataset is composed of hyperspectral and Li DAR data as well as their corresponding training and test samples.

You may download the data from the following address: P.

Ghamisi, Spectral and Spatial Classification of Hyperspectral Data, Ph. P.

International Journal of Computer Applications 109(8):18-25, January 2015.

on Geoscience and Remote Sensing, 52(9): 5771-5782, 2014.

Sveinsson, Automatic Spectral-Spatial Classification Framework Based on Attribute Profiles and Supervised Feature Extraction, IEEE Trans.

Lotufo, “Extinction Profiles for the Classification of Remote Sensing Data,” in IEEE Transactions on Geoscience and Remote Sensing.

Phinn, “Landcover classification using both hyperspectral and lidar data,” International Journal of Image and Data Fusion, vol.


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