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Hyperspectral Classification Using Stacked Autoencoders with Deep Learning
Date
2014-07-24
Author
Özdemir, Okan Bilge
Gedik, Ekin
Çetin, Yasemin
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https://hdl.handle.net/11511/77540
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HYPERSPECTRAL CLASSIFICATION USING STACKED AUTOENCODERS WITH DEEP LEARNING
Özdemir, Ataman; Cetin, C. Yasemin Yardimci (2014-06-27)
In this study, stacked autoencoders which are widely utilized in deep learning research are applied to remote sensing domain for hyperspectral classification. High dimensional hyperspectral data is an excellent candidate for deep learning methods. However, there are no works in literature that focuses on such deep learning approaches for hyperspectral imagery. This study aims to fill this gap by utilizing stacked autoencoders. Experiments are conducted on the Pavia University scene. Using stacked autoencode...
Hyperspectral Image Compression Using an Online Learning Method
Ulku, Irem; TÖREYİN, BEHÇET UĞUR (2015-04-24)
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 on...
Hyperspectral Superpixel Extraction Using Boundary Updates Based on Optimal Spectral Similarity Metric
Çalışkan, Akın; Koz, Alper; Alatan, Abdullah Aydın (2015-07-31)
The high spectral resolution of hyperspectral images (HSI) requires a heavy processing load. Assigning each pixel to a group in the image, which is called superpixel, and processing the superpixels instead of the pixels is resorted as a means to overcome this challenge in the hyperspectral literature. In this paper, we propose an algorithm to segment a hyperspectral image into superpixels by means of iteratively updating the boundary pixels of superpixels. We first explore the optimal similarity metric for ...
Hyperspectral anomaly detection method based on autoencoder
Batı, Emrecan; Alatan, Abdullah Aydın (2015-09-24)
Süperpikseller ve İmza Tabanlı Yöntemler Kullanarak Hiperspektral Hedef Tespiti
Kütük, Mustafa; Alatan, Abdullah Aydın (2019-06-27)
Spectral signature based methods which form the mainstream in hyperspectral target detection can be classified mainly in three categories as the methods using background modeling, subspace projection based methods, and hybrid methods merging linear unmixing with background estimation. A common characteristic of all these methods is to classify each pixel of the hyperspectral image as a target or background while ignoring the spatial relations between neighbor pixels. Integration of contextual information de...
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O. B. Özdemir, E. Gedik, and Y. Çetin, “Hyperspectral Classification Using Stacked Autoencoders with Deep Learning,” 2014, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/77540.