Browsing by Author "Ulku, Irem"

Now showing 1 - 7 of 7

Citation - WoS: 5
Citation - Scopus: 7
Comparison of Single Channel Indices for U-Net Based Segmentation of Vegetation in Satellite Images
(Spie-int Soc Optical Engineering, 2020) Ulku, Irem; Barmpoutis, Panagiotis; Stathaki, Tania; Akagunduz, Erdem; 233834; 01. Çankaya Üniversitesi
Hyper-spectral satellite imagery, consisting of multiple visible or infrared bands, is extremely dense and weighty for deep operations. Regarding problems related to vegetation as, more specifically, tree segmentation, it is difficult to train deep architectures due to lack of large-scale satellite imagery. In this paper, we compare the success of different single channel indices, which are constructed from multiple bands, for the purpose of tree segmentation in a deep convolutional neural network ( CNN) architecture. The utilized indices are either hand-crafted such as excess green index (ExG) and normalized difference vegetation index (NDVI) or reconstructed from the visible bands using feature space transformation methods such as principle component analysis (PCA). For comparison, these features are fed to an identical CNN architecture, which is a standard U-Net-based symmetric encoder-decoder design with hierarchical skip connections and the segmentation success for each single index is recorded. Experimental results show that single bands, which are constructed from the vegetation indices and space transformations, can achieve similar segmentation performances as compared to that of the original multi-channel case.
Citation - WoS: 1
Citation - Scopus: 3
Hyperspectral Image Compression Using an Online Learning Method
(Spie-int Soc Optical Engineering, 2015) Ulku, Irem; Toreyin, B. Ugur; 19325; 06.03. Elektrik-Elektronik Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
A hyperspectral image compression method is proposed using an online dictionary learning approach. The online learning mechanism is aimed at utilizing least number of dictionary elements for each hyperspectral image under consideration. In order to meet this "sparsity constraint", basis pursuit algorithm is used. Hyperspectral imagery from AVIRIS datasets are used for testing purposes. Effects of non-zero dictionary elements on the compression performance are analyzed. Results indicate that, the proposed online dictionary learning algorithm may be utilized for higher data rates, as it performs better in terms of PSNR values, as compared with the state-of-the-art predictive lossy compression schemes.
Citation - WoS: 4
Citation - Scopus: 4
Large-Scale Hyperspectral Image Compression Via Sparse Representations Based on Online Learning
(Univ Zielona Gora Press, 2018) Ulku, Irem; Kizgut, Ersin; 17575; 07.01. Lisansüstü Eğitim Enstitüsü; 07. Enstitüler; 01. Çankaya Üniversitesi
In this study, proximity based optimization algorithms are used for lossy compression of hyperspectral images that are inherently large scale. This is the first time that such proximity based optimization algorithms are implemented with an online dictionary learning method. Compression performances are compared with the one obtained by various sparse representation algorithms. As a result, proximity based optimization algorithms are listed among the three best ones in terms of compression performance values for all hyperspectral images. Additionally, the applicability of anomaly detection is tested on the reconstructed images.
Citation - Scopus: 5
Lossy Compression of Hyperspectral Images Using Online Learning Based Sparse Coding
(Ieee, 2014) Toreyin, B. Ugur; Ulku, Irem; 17575; 06.03. Elektrik-Elektronik Mühendisliği; 06. Mühendislik Fakültesi; 01. Çankaya Üniversitesi
A lossy hyperspectral image compression method is proposed using online learning based sparse coding. The least number of coefficients are obtained to represent hyperspectral images by applying the sparse coding algorithm which is based on a dicriminative online dictionary learning method. Results indicate that a pre-analysis of the number of non-zero dictionary elements may help in improving the overall compression quality.
Citation - WoS: 1
Citation - Scopus: 3
Lossy Compressive Sensing Based on Online Dictionary Learning
(Slovak Acad Sciences inst informatics, 2019) Ulku, Irem; Kizgut, Ersin; 17575; 07.01. Lisansüstü Eğitim Enstitüsü; 07. Enstitüler; 01. Çankaya Üniversitesi
In this paper, a lossy compression of hyperspectral images is realized by using a novel online dictionary learning method in which three dimensional datasets can be compressed. This online dictionary learning method and blind compressive sensing (BCS) algorithm are combined in a hybrid lossy compression framework for the first time in the literature. According to the experimental results, BCS algorithm has the best compression performance when the compression bit rate is higher than or equal to 0.5 bps. Apart from observing rate-distortion performance, anomaly detection performance is also tested on the reconstructed images to measure the information preservation performance.
Citation - WoS: 8
Citation - Scopus: 10
Sparse Coding of Hyperspectral Imagery Using Online Learning
(Springer London Ltd, 2015) Toreyin, Behcet Ugur; Ulku, Irem; 17575; 19325; 01. Çankaya Üniversitesi; 06. Mühendislik Fakültesi; 06.03. Elektrik-Elektronik Mühendisliği
Sparse coding ensures to express the data in terms of a few nonzero dictionary elements. Since the data size is large for hyperspectral imagery, it is reasonable to use sparse coding for compression of hyperspectral images. In this paper, a hyperspectral image compression method is proposed using a discriminative online learning-based sparse coding algorithm. Compression and anomaly detection tests are performed on hyperspectral images from the AVIRIS dataset. Comparative rate-distortion analyses indicate that the proposed method is superior to the state-of-the-art hyperspectral compression techniques.
Citation - WoS: 10
Citation - Scopus: 12
Sparse Representations for Online-Learning Hyperspectral Image Compression
(Optical Soc Amer, 2015) Toreyin, Behcet Ugur; Ulku, Irem; 17575; 19325; 01. Çankaya Üniversitesi; 06. Mühendislik Fakültesi; 06.03. Elektrik-Elektronik Mühendisliği
Sparse models provide data representations in the fewest possible number of nonzero elements. This inherent characteristic enables sparse models to be utilized for data compression purposes. Hyperspectral data is large in size. In this paper, a framework for sparsity-based hyperspectral image compression methods using online learning is proposed. There are various sparse optimization models. A comparative analysis of sparse representations in terms of their hyperspectral image compression performance is presented. For this purpose, online-learning-based hyperspectral image compression methods are proposed using four different sparse representations. Results indicate that, independent of the sparsity models, online-learning-based hyperspectral data compression schemes yield the best compression performances for data rates of 0.1 and 0.3 bits per sample, compared to other state-of-the-art hyperspectral data compression techniques, in terms of image quality measured as average peak signal-to-noise ratio. (c) 2015 Optical Society of America