Hyperspectral Image Classification via Basic Thresholding Classifier

Date

2016-07-01

Author

Toksoz, Mehmet Altan
Ulusoy, İlkay

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

147
views

0
downloads

We propose a lightweight sparsity-based algorithm, namely, the basic thresholding classifier (BTC), for hyperspectral image (HSI) classification. BTC is a pixelwise classifier which uses only the spectral features of a given test pixel. It performs the classification using a predetermined dictionary consisting of labeled training pixels. It then produces the class label and residual vector of the test pixel. Since incorporating spatial and spectral information in HSI classification is quite an effective way of improving classification accuracy, we extend our proposal to a three-step spatial-spectral framework. First, every pixel of a given HSI is classified using BTC. The resulting residual vectors form a cube which could be interpreted as a stack of images representing residual maps. Second, each residual map is filtered using an averaging filter. Finally, the class label of each test pixel is determined based on minimal residual. Numerical results on public data sets show that our proposal outperforms well-known support vector machine-based techniques and sparsity-based greedy approaches like simultaneous orthogonal matching pursuit in terms of both classification accuracy and computational cost.

Subject Keywords

Basic Thresholding Classifier (BTC), Hyperspectral Image (HSI) Classification, Simultaneous Orthogonal Matching Pursuit (OMP) (SOMP), Sparsity-Based Algorithms, Support Vector Machines (SVMs)

URI

https://hdl.handle.net/11511/42555

Journal

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

DOI

https://doi.org/10.1109/tgrs.2016.2535458

Collections

Department of Electrical and Electronics Engineering, Article

Suggestions

OpenMETU
Core

Hyperspectral Image Classification via Kernel Basic Thresholding Classifier TOKSÖZ, Mehmet Altan; Ulusoy, İlkay (2017-02-01) We propose a nonlinear kernel version of recently introduced basic thresholding classifier (BTC) for hyperspectral image (HSI) classification. BTC is a sparsity-based linear classifier, which utilizes inner product for similarity measure based on the fact that the hyperspectral data are linearly separable in the feature space. In practice, the pixels of different classes of a given HSI are not always distinct and may overlap. This is known as the inseparability problem, which reduces the performance of a li...
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...
Image segmentation by fusion of low level and domain specific information via Markov Random Fields Karadag, Ozge Oztimur; Yarman Vural, Fatoş Tunay (2014-09-01) We propose a new segmentation method by fusing a set of top-down and bottom-up segmentation maps under the Markov Random Fields (MRF) framework. The bottom-up segmentation maps are obtained by varying the parameters of an unsupervised segmentation method, such as Mean Shift. The top-down segmentation maps are constructed from some priori information, called domain specific information (DSI), received from a domain expert in the form of general properties about the image dataset. The properties are then used...
Image Annotation With Semi-Supervised Clustering Sayar, Ahmet; Yarman Vural, Fatoş Tunay (2009-09-16) Methods developed for image annotation usually make use of region clustering algorithms. Visual codebooks are generated from the region clusters of low level features. These codebooks are then, matched with the words of the text document related to the image, in various ways. In this paper, we supervise the clustering process by using three types of side information. The first one is the topic probability information obtained from the text document associated with the image. The second is the orientation an...
AUTOMATIC MULTI-SCALE SEGMENTATION OF HIGH SPATIAL RESOLUTION SATELLITE IMAGES USING WATERSHEDS Sahin, Kerem; Ulusoy, İlkay (2013-07-26) An automatic segmentation algorithm that is based on watersheds and region merging type multi-scale segmentation (MSS) is proposed, which can be used as the initial step of an object-based classifier. First, the image is segmented using watershed segmentation. Then, primitive segments are merged to create meaningful objects by the proposed hybrid region merging algorithm. During these steps, an unsupervised segmentation accuracy metric is considered so that best performing parameters of the proposed algorit...

Citation Formats

M. A. Toksoz and İ. Ulusoy, “Hyperspectral Image Classification via Basic Thresholding Classifier,” IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, pp. 4039–4051, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/42555.