Browsing by Author "Omruuzun, Fatih"

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    Citation - WoS: 6
    Citation - Scopus: 9
    Shadow Removal From Vnir Hyperspectral Remote Sensing Imagery With Endmember Signature Analysis
    (Spie-int Soc Optical Engineering, 2015) Omruuzun, Fatih; Baskurt, Didem Ozisik; Daglayan, Hazan; Cetin, Yasemin Yardimci
    This study aims to develop an effective regional shadow removal algorithm using rich spectral information existing in hyperspectral imagery. The proposed method benefits from spectral similarity of shadow and neighboring nonshadow pixels regardless of the intensity values. Although the shadow area has lower reflectance values due to inadequacy of incident light, it is expected that this area contains similar spectral characteristics with nonshadow area. Using this assumption, the endmembers in both shadowed and nonshadow area are extracted by Vertex Component Analysis (VCA). On the other hand, HySime algorithm overcomes estimating number of endmembers, which is one of the challenging parts in hyperspectral unmixing. Therefore, two sets of endmembers are extracted independently for both shadowed and nonshadow area. The proposed study aims at revealing the relation between these two endmember sets by comparing their pairwise similarities. Finally, reflectance values of shadowed pixels are re-calculated separately for each spectral band of hyperspectral image using this information.
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    Citation - WoS: 1
    Citation - Scopus: 2
    Utilizing Hyperspectral Remote Sensing Imagery for Afforestation Planning of Partially Covered Areas
    (Spie-int Soc Optical Engineering, 2015) Omruuzun, Fatih; Baskurt, Didem Ozisik; Daglayan, Hazan; Cetin, Yasemin Yardimci
    In this study, a supportive method for afforestation planning process of partially forested areas using hyperspectral remote sensing imagery has been proposed. The algorithm has been tested on a scene covering METU campus area that is acquired by high resolution hyperspectral push-broom sensor operating in visible and NIR range of the electromagnetic spectrum. The main contribution of this study to the literature is segmentation of partially forested regions with a semi-supervised classification of specific tree species based on chlorophyll content quantified in hyperspectral scenes. In addition, the proposed method makes use of various hyperspectral image processing algorithms to improve identification accuracy of image regions to be planted.