Hyperspectral Unmixing Based Analysis of Forested Areas

Date

2015-05-19

Author

Başkurt, Nur Didem
Omruuzun, Fatih
Çetin, Yasemin

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

192
views

0
downloads

This study aims to extract the planted regions in partially forested area by analyzing the hyperspectral remote sensing images acquired with airborne platforms. The proposed study utilizes the endmember signatures obtained from hyperspectral unmixing algorithms in order to classify the image pixels. The classification algorithm selects the endmember with highest spectral vegetation characteristic, and associates this endmember with the planted area pixels. The algorithm is tested on a scene covering METU Ankara campus area that is acquired by high resolution hyperspectral push-broom sensor operating in visible and NIR range of the electromagnetic spectrum on October, 22 2014.

Subject Keywords

Hyperspectral imaging, Remote sensing, Forest detection, Hyperspectral unmixing

URI

https://hdl.handle.net/11511/54641

Collections

Graduate School of Informatics, Conference / Seminar

Suggestions

OpenMETU
Core

UTILIZING HYPERSPECTRAL REMOTE SENSING IMAGERY FOR AFFORESTATION PLANNING OF PARTIALLY COVERED AREAS Omruuzun, Fatih; Baskurt, Didem Ozisik; Daglayan, Hazan; Çetin, Yasemin (2015-09-23) 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...
Shadow Removal from VNIR Hyperspectral Remote Sensing Imagery with Endmember Signature Analysis Omruuzun, Fatih; Baskurt, Didem Ozisik; Daglayan, Hazan; Çetin, Yasemin (2015-04-22) 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...
HYPERSPECTRAL UNMIXING BASED VEGETATION DETECTION WITH SEGMENTATION Özdemir, Okan Bilge; Soydan, Hilal; Çetin, Yasemin; Duzgun, Sebnem (2016-07-15) This paper presents a vegetation detection application with semi-supervised target detection using hyperspectral unmixing and segmentation algorithms. The method firstly compares the known target spectral signature from a generic source such as a spectral library with each pixel of hyperspectral data cube employing Spectral Angle Mapper (SAM) algorithm. The pixel(s) with the best match are assumed to be the most likely target vegetation locations. The regions around these potential target locations are furt...
Deep convolutional neural networks for airport detection in remote sensing images Budak, Umit; Sengur, Abdulkadir; Halıcı, Uğur (2018-05-05) This study investigated the use of deep convolutional neural networks (CNNs) in providing a solution for the problem of airport detection in remote sensing images (RSIs). In recent years, Deep CNNs have gained much attention with numerous applications having been undertaken in the area of computer vision. Researchers generally approach airport detection as a pattern recognition problem, in which first various distinctive features are extracted, and then a classifier is adopted to detect airports. CNNs not o...
Dry Dock Detection in Satellite Images with Representation Learning Aktaş, Ümit Ruşen; Firat, Orhan; Yarman Vural, Fatoş Tunay (2013-04-26) In this study, we propose a method to detect dry docks, a harbour man-made object which is hard to recognize, using representation learning in satellite images. Dry docks are coastal structures which may include ships for repairing purposes, and they exist in harbour regions. The search space is pruned by making use of two low-level features that invariantly define docks, and remaining samples are used to train a representation learning system. Experimental results suggest that classification methods using ...

Citation Formats

N. D. Başkurt, F. Omruuzun, and Y. Çetin, “Hyperspectral Unmixing Based Analysis of Forested Areas,” 2015, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/54641.