DEEPLY LEARNED ATTRIBUTE PROFILES FOR HYPERSPECTRAL PIXEL CLASSIFICATION
Murat Can Özdemir
Computer Science and Engineering, MSc. Thesis, 2016
Prof. Dr. Berrin Yanıkoğlu (Thesis Advisor), Assoc. Prof. Dr. Erchan Aptoula (Co-Advisor), Assoc. Prof. Dr. Koray Kayabol, Assoc. Prof. Dr. Selim Balcısoy, Asst. Prof. Dr. Kamer Kaya
Date & Time: August 9th, 2016 – 3:30 PM
Place: FENS G029
Keywords : Mathematical Morphology, Convolutional Neural Networks, Deep Learning, Remote Sensing, Extended Attribute Profiles, Hyperspectral Image Classification
Hyperspectral Imaging has a large potential for knowledge representation about the real world. Providing a pixel classification algorithm to generate maps with labels has become important in numerous fields 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 Profiles (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 off-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 classifiers, and using Random Forest (RF) classifiers as a control group for both raw and deeply learned features, experiments are made. The results showed that not only there are significant improvements in numerical results on the Pavia University dataset, but also the classification maps become more robust and more intuitive as different, insightful and compatible attribute profiles are used along with spectral signatures with a CNN that is designed for this purpose.