Utilizing hyperspectral remote sensing imagery for afforestation planning of partially covered areas

Fatih Omruuzun; Didem Ozisik Baskurt; Hazan Daglayan; Yasemin Yardimci Cetin

doi:10.1117/12.2196532

15 October 2015 Utilizing hyperspectral remote sensing imagery for afforestation planning of partially covered areas

Fatih Omruuzun, Didem Ozisik Baskurt, Hazan Daglayan, Yasemin Yardimci Cetin

Proceedings Volume 9643, Image and Signal Processing for Remote Sensing XXI; 96432N (2015) https://doi.org/10.1117/12.2196532
Event: SPIE Remote Sensing, 2015, Toulouse, France

Abstract

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.

Citation Download Citation

Fatih Omruuzun, Didem Ozisik Baskurt, Hazan Daglayan, and Yasemin Yardimci Cetin "Utilizing hyperspectral remote sensing imagery for afforestation planning of partially covered areas", Proc. SPIE 9643, Image and Signal Processing for Remote Sensing XXI, 96432N (15 October 2015); https://doi.org/10.1117/12.2196532

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available