Deeply learned attribute profiles for hyperspectral pixel classification

Özdemir, Murat Can (2016) Deeply learned attribute profiles for hyperspectral pixel classification. [Thesis]

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Hyperspectral Imaging has a large potential for knowledge representation about the real world. Providing a pixel classi cation algorithm to generate maps with labels has become important in numerous elds since its inception, found use from military surveillance and natural resource observation to crop turnout estimation. In this thesis, within the branch of mathematical morphology, Attribute Pro les (AP) and their extension into the Hyperspectral domain have been used to extract descriptive vectors from each pixel on two hyperspectral datasets. These newly generated feature vectors are then supplied to Convolutional Neural Networks (CNNs), from o -the-shelf AlexNet and GoogLeNet to our proposed networks that would take into account local connectivity of regions, to extract further, higher level abstract features. Bearing in mind that the last layers of CNNs are supplied with softmax classi ers, and using Random Forest (RF) classi ers as a control group for both raw and deeply learned features, experiments are made. The results showed that not only there are signi cant improvements in numerical results on the Pavia University dataset, but also the classi cation maps become more robust and more intuitive as di erent, insightful and compatible attribute pro les are used along with spectral signatures with a CNN that is designed for this purpose.
Item Type: Thesis
Additional Information: Yükseköğretim Kurulu Tez Merkezi Tez No: 444567.
Uncontrolled Keywords: Mathematical Morphology. -- Convolutional Neural Networks. -- Deep Learning. -- Remote Sensing. -- Extended Attribute Profiles. -- Hyperspectral Image Classi cation. -- Uzaktan Algılama. -- Derin Ögrenme. -- Evrişimsel Sinir Ağları. -- Matematiksel Biçimbilim. -- Hiperspektral Görüntü Sınıfandırma. -- Öznitelik Profilleri.
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics > TK7885-7895 Computer engineering. Computer hardware
Divisions: Faculty of Engineering and Natural Sciences > Academic programs > Computer Science & Eng.
Faculty of Engineering and Natural Sciences
Depositing User: IC-Cataloging
Date Deposited: 13 Apr 2018 22:16
Last Modified: 26 Apr 2022 10:16

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