Tugcan Dundar

Tugcan Dundar
Gaziantep University · Department of Control and Automation

Master of Science

About

6
Publications
566
Reads
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58
Citations
Introduction
Tugcan Dundar currently works at the Gaziantep University. Tugcan does research in Electronic Engineering, Electrical Engineering and Programming Languages. Their most recent publication is 'Sparse Representation-Based Hyperspectral Image Classification Using Multiscale Superpixels and Guided Filter'.

Publications

Publications (6)
Article
We propose a spatial-spectral hyperspectral image classification method based on multiscale superpixels and guided filter (MSS-GF). In order to use spatial information effectively, MSSs are used to get local information from different region scales. Sparse representation classifier is used to generate classification maps for each region scale. Then...
Preprint
We propose a superpixel weighted low-rank and sparse unmixing (SWLRSU) method for sparse unmixing. The proposed method consists of two steps. In the first step, we segment hyperspectral image into superpixels which are defined as the homogeneous regions with different shape and sizes according to the spatial structure. Then, an efficient method is...
Conference Paper
A weighted and label-consistent joint sparse classifier (WLC-JSC) is proposed for hyperspectral image classification. Spatial information captured by a window centered at a test sample is improved using a weight assignment strategy. Neighbor pixels similar to the test sample are assigned more weight, while the remaining pixels are either eliminated...
Article
Full-text available
Sparse representation based approaches have recently begun to be applied to hyperspectral images due to the performance displayed in areas such as signal and image processing and successfull results have been achieved. The joint sparse representation classifier (JSRC) model has been developed so that spatial information in the hyperspectral image c...
Article
Full-text available
Sparse hyperspectral unmixing aims at finding the sparse fractional abundance vector of a spectral signature present in a mixed pixel. However, there are several types of noise present in the hyperspectral images. These are called mixed noise including stripes, impulse noise and Gaussian noise which deteriorate the performance of sparse unmixing al...
Article
Full-text available
Sparse representation based techniques are frequently used in the areas such as signal and image processing, computer vision and pattern recognition due to their performance. In recent years, sparse representation techniques have been used in the proposed methods related to classification of hyperspectral images and favorable results have been obta...

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