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Comparison of Single Channel Indices for U-Net Based Segmentation of Vegetation in Satellite Images

Ülkü, İrem; Barmpoutis, P.; Stathaki, T.; Akagündüz, Erdem (SPIE, 2020)

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 ...
Comparison of single channel indices for U-Net based segmentation of vegetation in satellite images

Ülkü, İrem; Barmpoutis, Panagiotis; Stathaki, Tania; Akagündüz, Erdem (2020)
Hybrid hyperspectral image compression method by using online dictionary learning based on sparse coding

Ülkü, İrem (2017)

Bu tezde, seyrek kodlama tabanlı çevrimiçi sözlük öğrenme yaklaşımını literatürde ilk kez hiperspektral görüntülerin sıkıştırılması için adapte eden hibrit bir yöntem önerilmiştir. Seyrek kodlama tabanlı çevrimiçi sözlük ...
Hyperspectral image compression using an online learning method

Ülkü, İrem; Töreyin, B. Uğur (2015)

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 ...
Large-scale hyperspectral image compression via sparse representations based on online learning

Ülkü, İrem; Kizgut, Ersin (Univ Zielona Gora Press, 2018-03)

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 ...
Lossy Compression of Hyperspectral Images Using Online Learning Based Sparse Coding

Ülkü, İrem; Töreyin, Behçet Uğur (IEEE, 2014)

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 ...
Lossy Compressive Sensing Based on Online Dictionary Learning

Ülkü, İrem; Kizgut, Ersin (Slovak Acad Sciences Inst Informatics, 2019)

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 ...
Sparse coding of hyperspectral imagery using online learning

Ülkü, İrem; Töreyin, Behçet Uğur (Springer, 2015-05)

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. ...
Sparse representations for online-learning-based hyperspectral image compression

Ülkü, İrem; Töreyin, Behçet Uğur (Optical Soc Amer, 2015-10-10)

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 ...

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