Unsupervised segmentation of hyperspectral images using modified phase correlation

Date

2006-10-01

Author

Ertuerk, Alp
Ertuerk, Sarp

Metadata

Show full item record

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

Item Usage Stats

137
views

0
downloads

This letter presents hyperspectral image segmentation based on the phase-correlation measure of subsampled hyperspectral data, which is referred to as modified phase correlation. The hyperspectral spectrum of each pixel is initially subsampled to gain, robustness against noise and spatial variability, and phase correlation is applied to determine spectral similarity. Similar and dissimilar pixels are decided according to the peak value of the phase correlation result to determine pixels that fall into the same segments. The approach can be regarded as a region-growing technique. The total number of segments is determined automatically according to the similarity threshold.

Subject Keywords

Hyperspectral image segmentation, Phase correlation

URI

https://hdl.handle.net/11511/65150

Journal

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS

DOI

https://doi.org/10.1109/lgrs.2006.880535

Collections

Department of Electrical and Electronics Engineering, Article

Suggestions

OpenMETU
Core

Unsupervised Deep Learning for Subspace Clustering SEKMEN, ali; Koku, Ahmet Buğra; PARLAKTUNA, Mustafa; ABDULMALEK, Ayad; VANAMALA, Nagendrababu (2017-12-14) This paper presents a novel technique for the segmentation of data W = [w(1) . . . w(N)] subset of R-D drawn from a union U = boolean OR(M)(i=1) S-i of subspaces {S-i}(i=1)(M). First, an existing subspace segmentation algorithm is used to perform an initial data clustering {C-i}(i=1)(M), where C-i = {w(i1) . . . w(ik)} subset of W is the set of data from the ith cluster. Then, a local subspace LSi is matched for each C-i and the distance d(ij) between LSi and each point w(ij) is an element of C-i is compute...
An image retrieval system based on region classification Ozcanli, OC; Yarman-Vural, F (2004-01-01) In this study, a content based image retrieval (CBIR) system to query the objects in an image database is proposed. Images are represented as collections of regions after being segmented with Normalized Cuts algorithm. MPEG-7 content descriptors are used to encode regions in a 239-dimensional feature space. User of the proposed CBIR system decides which objects to query and labels exemplar regions to train the system using a graphical interface. Fuzzy ARTMAP algorithm is used to learn the mapping between fe...
Low-level image segmentation based scene classification Akbaş, Emre (2010-08-26) This paper is aimed at evaluating the semantic information content of multiscale, low-level image segmentation. As a method of doing this, we use selected features of segmentation for semantic classification of real images. To estimate the relative measure of the information content of our features, we compare the results of classifications we obtain using them with those obtained by others using the commonly used patch/grid based features. To classify an image using segmentation based features, we model th...
MRF Based Image Segmentation Augmented with Domain Specific Information Karadag, Ozge Oztimur; Yarman Vural, Fatoş Tunay (2013-09-13) A Markov Random Field based image segmentation system which combines top-down and bottom-up segmentation approaches is proposed in this study. The system is especially proposed for applications where no labeled training set is available, but some priori general information referred as domain specific information about the dataset is available. Domain specific information is received from a domain expert and formalized by a mathematical representation. The type of information and its representation depends o...
REGION-BASED IMAGE SEGMENTATION VIA GRAPH CUTS Cigla, Cevahir; Alatan, Abdullah Aydın (2008-01-01) A graph theoretic color image segmentation algorithm is proposed, in which the popular normalized cuts image segmentation method is improved with modifications on its graph structure. The image is represented by a weighted undirected graph, whose nodes correspond to over-segmented regions, instead of pixels, that decreases the complexity of the overall algorithm. In addition, the link weights between the nodes are calculated through the intensity similarities of the neighboring regions. The irregular distri...

Citation Formats

A. Ertuerk and S. Ertuerk, “Unsupervised segmentation of hyperspectral images using modified phase correlation,” IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, pp. 527–531, 2006, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/65150.