UTILIZING HYPERSPECTRAL REMOTE SENSING IMAGERY FOR AFFORESTATION PLANNING OF PARTIALLY COVERED AREAS

Date

2015-09-23

Author

Omruuzun, Fatih
Baskurt, Didem Ozisik
Daglayan, Hazan
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

174
views

0
downloads

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.

Subject Keywords

Anomaly detection, Hyperspectral unmixing, Afforestation planning, Hyperspectral imaging

URI

https://hdl.handle.net/11511/56926

DOI

https://doi.org/10.1117/12.2196532

Collections

Graduate School of Informatics, Conference / Seminar

Suggestions

OpenMETU
Core

Hyperspectral Unmixing Based Analysis of Forested Areas Başkurt, Nur Didem; Omruuzun, Fatih; Çetin, Yasemin (2015-05-19) 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 An...
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...
A COMPARATIVE STUDY OF HYPERSPECTRAL ANOMALY AND SIGNATURE BASED TARGET DETECTION METHODS FOR OIL SPILLS Soydan, Hilal; Koz, Alper; Düzgün, Hafize Şebnem; Alatan, Abdullah Aydın (2015-06-05) Hyperspectral target detection methods have until now progressed mainly on two paths in remote sensing research. The first approach, anomaly detection methods, use the difference of a local region with respect to its neighborhood to analyze the image without using any prior information of the searched target. The second approach on the other hand uses a previously obtained signature of the target, which uniquely represents the target's characteristics with respect to the spectral wavelengths. The signature ...
Abnormal Crowd Behavior Detection Using Novel Optical Flow-Based Features Direkoglu, Cem; Sah, Melike; O'Connor, Noel E. (2017-09-01) In this paper, we propose a novel optical flow based features for abnormal crowd behaviour detection. The proposed feature is mainly based on the angle difference computed between the optical flow vectors in the current frame and in the previous frame at each pixel location. The angle difference information is also combined with the optical flow magnitude to produce new, effective and direction invariant event features. A one-class SVM is utilized to learn normal crowd behavior. If a test sample deviates si...
Automatic Mapping of Linearwoody Vegetation Features in Agricultural Landscapes AKSOY, SELİM; AKÇAY, HÜSEYİN GÖKHAN; Cinbiş, Ramazan Gökberk; Wassenaar, Tom (2008-07-11) Development of automatic methods for agricultural mapping and monitoring using remotely sensed imagery has been an important research problem. We describe algorithms that exploit the spectral, textural and object shape information using hierarchical feature extraction and decision making steps for automatic mapping of linear strips of woody vegetation in very high-resolution imagery. First, combinations of multispectral values and multi-scale Gabor and entropy texture features are used for training pixel le...

Citation Formats

F. Omruuzun, D. O. Baskurt, H. Daglayan, and Y. Çetin, “UTILIZING HYPERSPECTRAL REMOTE SENSING IMAGERY FOR AFFORESTATION PLANNING OF PARTIALLY COVERED AREAS,” 2015, vol. 9643, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/56926.