Başkurt, Nur Didem

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Baskurt,N.D.
N.D.Baskurt
N.,Başkurt
Başkurt,N.D.
Nur Didem, Başkurt
Baskurt, Nur Didem
B.,Nur Didem
B., Nur Didem
Başkurt, Nur Didem
N., Baskurt
N.D.Başkurt
Nur Didem, Baskurt
Baskurt, Didem Ozisik
Job Title
Araştırma Görevlisi
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Software Engineering
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Former Staff
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Scholarly Output

3

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Citation Count

8

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2015 - 20163
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Now showing 1 - 3 of 3
  • Thumbnail Image
    Conference Object
    Citation - WoS: 1
    Citation - Scopus: 1
    A Novel Method To Detect Shadows on Multispectral Images
    (Spie-int Soc Optical Engineering, 2016) Sevim, Hazan Daglayan; Cetin, Yasemin Yardimci; Baskurt, Didem Ozisik; Computer Engineering; Software Engineering
    Shadowing occurs when the direct light coming from a light source is obstructed by high human made structures, mountains or clouds. Since shadow regions are illuminated only by scattered light, true spectral properties of the objects are not observed in such regions. Therefore, many object classification and change detection problems utilize shadow detection as a preprocessing step. Besides, shadows are useful for obtaining 3D information of the objects such as estimating the height of buildings. With pervasiveness of remote sensing images, shadow detection is ever more important. This study aims to develop a shadow detection method on multispectral images based on the transformation of C-1 C-2 C-3 space and contribution of NIR bands. The proposed method is tested on Worldview-2 images covering Ankara, Turkey at different times. The new index is used on these 8-band multispectral images with two NIR bands. The method is compared with methods in the literature.
  • Thumbnail Image
    Conference Object
    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; Computer Engineering; Software Engineering
    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.
  • Thumbnail Image
    Conference Object
    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; Computer Engineering; Software Engineering
    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.