An endmember-based distance for content based hyperspectral image retrieval
نویسندگان
چکیده
We propose a specific content-based image retrieval (CBIR) system for hyperspectral images exploiting its rich spectral information. The CBIR image features are the endmember signatures obtained from the image data by endmember induction algorithms (EIAs). Endmembers correspond to the elementary materials in the scene, so that the pixel spectra can be decomposed into a linear combination of endmember signatures. EIA search for points in the high dimensional space of pixel spectra defining a convex polytope, often a simplex, covering the image data. This paper introduces a dissimilarity measure between hyperspectral images computed over the image induced endmembers, proving that it complies with the axioms of a distance. We provide a comparative discussion of dissimilarity functions, and quantitative evaluation of their relative performances on a large collection of synthetic hyperspectral images, and on a dataset extracted from a real hyperspectral image. Alternative dissimilarity functions considered are the Hausdorff distance and robust variations of it. We assess the CBIR performance sensitivity to changes in the distance between endmembers, the EIA employed, and some other conditions. The proposed hyperspectral image distance improves over the alternative dissimilarities in all quantitative performance measures. The visual results of the CBIR on the real image data demonstrate its usefulness for practical applications. & 2012 Published by Elsevier Ltd.
منابع مشابه
Content Based Hyperspectral Image Retrieval: A Systematic Review
Hyperspectral imaging comprises the technologies that incorporates remote sensing and analysis of an object or specific area of the earth at different distances with very large number of bands. Currently, a wide range of hyperspectral data sets are obtained continuously, in addition to conventional multispectral remote sensing images, and presented to users by institutions for both commercial a...
متن کاملOn the Use of a Hybrid Approach to Contrast Endmember Induction Algorithms
In remote sensing hyperspectral image processing, identifying the constituent spectra (endmembers) of the materials in the image is a key procedure for further analysis. The contrast between Endmember Inductions Algorithms (EIAs) is a delicate issue, because there is a shortage of validation images with accurate ground truth information, and the induced endmembers may not correspond to any know...
متن کاملImage retrieval using the combination of text-based and content-based algorithms
Image retrieval is an important research field which has received great attention in the last decades. In this paper, we present an approach for the image retrieval based on the combination of text-based and content-based features. For text-based features, keywords and for content-based features, color and texture features have been used. Query in this system contains some keywords and an input...
متن کاملAn image-based endmember bundle extraction algorithm using reconstruction error for hyperspectral imagery
Although many endmember extraction algorithms have been proposed for hyperspectral images in recent years, there are still some problems in endmember extraction which would lead to inaccurate endmember extraction. One important problem is the variation in endmember spectral signatures due to spatial and temporal variability in the condition of scene components and differential illumination cond...
متن کاملMinimum distance constrained nonnegative matrix factorization for the endmember extraction of hyperspectral images
Endmember extraction and spectral unmixing is a very challenging task in multispectral/hyperspectral image processing due to the incompleteness of information. In this paper, a new method for endmember extraction and spectral unmixing of hyperspectral images is proposed, which is called as minimum distance constrained nonnegative matrix factorization (MDC-NMF). After being compared with a newly...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Pattern Recognition
دوره 45 شماره
صفحات -
تاریخ انتشار 2012