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      Deep Learning-Based Classification of Hyperspectral Data

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          Classification of hyperspectral remote sensing images with support vector machines

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            Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position

            A neural network model for a mechanism of visual pattern recognition is proposed in this paper. The network is self-organized by "learning without a teacher", and acquires an ability to recognize stimulus patterns based on the geometrical similarity (Gestalt) of their shapes without affected by their positions. This network is given a nickname "neocognitron". After completion of self-organization, the network has a structure similar to the hierarchy model of the visual nervous system proposed by Hubel and Wiesel. The network consists of an input layer (photoreceptor array) followed by a cascade connection of a number of modular structures, each of which is composed of two layers of cells connected in a cascade. The first layer of each module consists of "S-cells", which show characteristics similar to simple cells or lower order hypercomplex cells, and the second layer consists of "C-cells" similar to complex cells or higher order hypercomplex cells. The afferent synapses to each S-cell have plasticity and are modifiable. The network has an ability of unsupervised learning: We do not need any "teacher" during the process of self-organization, and it is only needed to present a set of stimulus patterns repeatedly to the input layer of the network. The network has been simulated on a digital computer. After repetitive presentation of a set of stimulus patterns, each stimulus pattern has become to elicit an output only from one of the C-cells of the last layer, and conversely, this C-cell has become selectively responsive only to that stimulus pattern. That is, none of the C-cells of the last layer responds to more than one stimulus pattern. The response of the C-cells of the last layer is not affected by the pattern's position at all. Neither is it affected by a small change in shape nor in size of the stimulus pattern.
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              Kernel-based methods for hyperspectral image classification

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                Author and article information

                Journal
                IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
                IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing
                Institute of Electrical and Electronics Engineers (IEEE)
                1939-1404
                2151-1535
                June 2014
                June 2014
                : 7
                : 6
                : 2094-2107
                Article
                10.1109/JSTARS.2014.2329330
                d9dea311-6900-4f44-b867-d9a27510c580
                © 2014
                History

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