Hyperspectral anomaly detection method based on autoencoder

Date

2015-09-24

Author

Batı, Emrecan
Alatan, Abdullah Aydın

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https://hdl.handle.net/11511/74062

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Hyperspectral Anomaly Detection Method Based on Auto-encoder Bati, Emrecan; Caliskan, Akin; Koz, Alper; Alatan, Abdullah Aydın (2015-09-23) A major drawback of most of the existing hyperspectral anomaly detection methods is the lack of an efficient background representation, which can successfully adapt to the varying complexity of hyperspectral images. In this paper, we propose a novel anomaly detection method which represents the hyperspectral scenes of different complexity with the state-of-the-art representation learning method, namely auto-encoder. The proposed method first encodes the spectral image into a sparse code, then decodes the co...
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...
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...
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 ...
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Citation Formats

E. Batı and A. A. Alatan, “Hyperspectral anomaly detection method based on autoencoder,” 2015, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/74062.