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Unsupervised Fuzzy Clustering and Image Segmentation Using Weighted Neural Networks
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
2003 (English)In: Proceedings of 12th International Conference for Image Analysis and Processing, 308-313 p.Article in journal (Refereed) Published
Place, publisher, year, edition, pages
2003. 308-313 p.
URN: urn:nbn:se:uu:diva-93378OAI: oai:DiVA.org:uu-93378DiVA: diva2:166836
Available from: 2005-09-05 Created: 2005-09-05 Last updated: 2010-03-01Bibliographically approved
In thesis
1. Hyperspectral Image Generation, Processing and Analysis
Open this publication in new window or tab >>Hyperspectral Image Generation, Processing and Analysis
2005 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Hyperspectral reflectance data are utilised in many applications, where measured data are processed and converted into physical, chemical and/or biological properties of the target objects and/or processes being studied. It has been proven that crop reflectance data can be used to detect, characterise and quantify disease severity and plant density.

In this thesis, various methods were proposed and used for detection, characterisation and quantification of disease severity and plant density utilising data acquired by hand-held spectrometers. Following this direction, hyperspectral images provide both spatial and spectral information opening for more efficient analysis.

Hence, in this thesis, various surface water quality parameters of inland waters have been monitored using hyperspectral images acquired by airborne systems. After processing the images to obtain ground reflectance data, the analysis was performed using similar methods to those of the previous case. Hence, these methods may also find application in future satellite based hyperspectral imaging systems.

However, the large size of these images raises the need for efficient data reduction. Self organising and learning neural networks, that can follow and preserve the topology of the data, have been shown to be efficient for data reduction. More advanced variants of these neural networks, referred to as the weighted neural networks (WNN), were proposed in this thesis, such as the weighted incremental neural network (WINN), which can be used for efficient reduction, mapping and clustering of large high-dimensional data sets, such as hyperspectral images.

Finally, the analysis can be reversed to generate spectra from simpler measurements using multiple colour-filter mosaics, as suggested in the thesis. The acquired instantaneous single image, including the mosaic effects, is demosaicked to generate a multi-band image that can finally be transformed into a hyperspectral image.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2005. 61 p.
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 82
Bildanalys, hyperspectral imaging, colour filter mosaics, remote sensing, crop reflectance, surface water quality, disease severity, plant density, linear system of equations, waste water detection, chlorophyll a, suspended paticulate matter, weighted fixed neural networks, weighted incremental neural networks, camera spectrometer, Bildanalys
National Category
Computer Vision and Robotics (Autonomous Systems)
urn:nbn:se:uu:diva-5905 (URN)91-554-6318-5 (ISBN)
Public defence
2005-09-23, Sal 80101, Ångström Laboratory, Lägerhyddsvägen 1, Uppsala, 10:00
Available from: 2005-09-05 Created: 2005-09-05 Last updated: 2013-12-04Bibliographically approved

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