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Hyperspectral Image Generation, Processing and Analysis
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image 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. , p. 61
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 82
Keywords [en]
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
Keywords [sv]
Bildanalys
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:uu:diva-5905ISBN: 91-554-6318-5 (print)OAI: oai:DiVA.org:uu-5905DiVA, id: diva2:166839
Public defence
2005-09-23, Sal 80101, Ångström Laboratory, Lägerhyddsvägen 1, Uppsala, 10:00
Opponent
Supervisors
Available from: 2005-09-05 Created: 2005-09-05 Last updated: 2018-01-13Bibliographically approved
List of papers
1. Sensitivity analysis of multi-channel images intended for spectrometry applications
Open this publication in new window or tab >>Sensitivity analysis of multi-channel images intended for spectrometry applications
(English)Article in journal (Refereed) Submitted
Identifiers
urn:nbn:se:uu:diva-93367 (URN)
Available from: 2005-09-05 Created: 2005-09-05 Last updated: 2010-03-01Bibliographically approved
2. Using Multiple Colour Mosaics for Multi- and Hyperspectral Imaging
Open this publication in new window or tab >>Using Multiple Colour Mosaics for Multi- and Hyperspectral Imaging
(English)Article in journal (Refereed) Submitted
Identifiers
urn:nbn:se:uu:diva-93368 (URN)
Available from: 2005-09-05 Created: 2005-09-05 Last updated: 2010-03-01Bibliographically approved
3. New approaches for surface water quality estimation in Lake Erken, Sweden, using remotely sensed hyperspectral data
Open this publication in new window or tab >>New approaches for surface water quality estimation in Lake Erken, Sweden, using remotely sensed hyperspectral data
(English)Article in journal (Refereed) Submitted
Identifiers
urn:nbn:se:uu:diva-93369 (URN)
Available from: 2005-09-05 Created: 2005-09-05 Last updated: 2010-03-01Bibliographically approved
4. Industrial plume detection by employing spectral descriptive signatures for anomaly detection
Open this publication in new window or tab >>Industrial plume detection by employing spectral descriptive signatures for anomaly detection
(English)Article in journal (Refereed) Submitted
Identifiers
urn:nbn:se:uu:diva-93370 (URN)
Available from: 2005-09-05 Created: 2005-09-05 Last updated: 2010-03-01Bibliographically approved
5. Using Feature Vector Based Analysis, based on Principal Component Analysis and Independent Component Analysis, for Analysing Hyperspectral Images
Open this publication in new window or tab >>Using Feature Vector Based Analysis, based on Principal Component Analysis and Independent Component Analysis, for Analysing Hyperspectral Images
2001 In: Proceedings of 11th International Conference for Image Analysis and Processing, p. 309-315Article in journal (Refereed) Published
Identifiers
urn:nbn:se:uu:diva-93371 (URN)
Available from: 2005-09-05 Created: 2005-09-05Bibliographically approved
6. Feature Vector Based Analysis: A Unified Concept for Multivariate Image Analysis
Open this publication in new window or tab >>Feature Vector Based Analysis: A Unified Concept for Multivariate Image Analysis
2001 In: Proceedings of Irish Machine Vision and Image Processing Conference, p. 219-226Article in journal (Refereed) Published
Identifiers
urn:nbn:se:uu:diva-93372 (URN)
Available from: 2005-09-05 Created: 2005-09-05Bibliographically approved
7. Feature Vector Based Analysis of Hyperspectral Crop Reflectance Data for Discrimination and Quantification of Fungal Disease Severity in Wheat
Open this publication in new window or tab >>Feature Vector Based Analysis of Hyperspectral Crop Reflectance Data for Discrimination and Quantification of Fungal Disease Severity in Wheat
2003 (English)In: Biosystems Engineering, Vol. 86, no 2, p. 125-134Article in journal (Refereed) Published
Identifiers
urn:nbn:se:uu:diva-93373 (URN)
Available from: 2005-09-05 Created: 2005-09-05 Last updated: 2010-03-01Bibliographically approved
8. Hyperspectral Crop Reflectance Data for characterising and estimating Fungal Disease Severity in Wheat
Open this publication in new window or tab >>Hyperspectral Crop Reflectance Data for characterising and estimating Fungal Disease Severity in Wheat
2005 (English)In: Biosystems Engineering, Vol. 91, no 1, p. 9-20Article in journal (Refereed) Published
Identifiers
urn:nbn:se:uu:diva-93374 (URN)
Available from: 2005-09-05 Created: 2005-09-05 Last updated: 2010-03-01Bibliographically approved
9. Measuring crop status using multivariate analysis of hyperspectral field reflectance with application on disease severity and amount of plant density
Open this publication in new window or tab >>Measuring crop status using multivariate analysis of hyperspectral field reflectance with application on disease severity and amount of plant density
2005 (English)In: Proceedings of 5th European Conference on Precision Agriculture, Vol. Precision Agriculture ’05, p. 217-225Article in journal (Refereed) Published
Identifiers
urn:nbn:se:uu:diva-93375 (URN)
Available from: 2005-09-05 Created: 2005-09-05 Last updated: 2010-03-01Bibliographically approved
10. Using Weighted Fixed Neural Networks for Unsupervised Fuzzy Clustering
Open this publication in new window or tab >>Using Weighted Fixed Neural Networks for Unsupervised Fuzzy Clustering
2002 (English)In: International Journal of Neural Systems, Vol. 12, no 6, p. 425-434Article in journal (Refereed) Published
Identifiers
urn:nbn:se:uu:diva-93376 (URN)
Available from: 2005-09-05 Created: 2005-09-05 Last updated: 2010-03-01Bibliographically approved
11. Unsupervised Fuzzy Clustering Using Weighted Incremental Neural Networks
Open this publication in new window or tab >>Unsupervised Fuzzy Clustering Using Weighted Incremental Neural Networks
2004 (English)In: International Journal of Neural Systems, Vol. 14, no 6, p. 355-371Article in journal (Refereed) Published
Identifiers
urn:nbn:se:uu:diva-93377 (URN)
Available from: 2005-09-05 Created: 2005-09-05 Last updated: 2010-03-01Bibliographically approved
12. Unsupervised Fuzzy Clustering and Image Segmentation Using Weighted Neural Networks
Open this publication in new window or tab >>Unsupervised Fuzzy Clustering and Image Segmentation Using Weighted Neural Networks
2003 (English)In: Proceedings of 12th International Conference for Image Analysis and Processing, p. 308-313Article in journal (Refereed) Published
Identifiers
urn:nbn:se:uu:diva-93378 (URN)
Available from: 2005-09-05 Created: 2005-09-05 Last updated: 2010-03-01Bibliographically approved
13. Unsupervised Hyperspectral Image Segmentation Using a New Class of Neuro-Fuzzy Systems Based on Weighted Incremental Neural Networks
Open this publication in new window or tab >>Unsupervised Hyperspectral Image Segmentation Using a New Class of Neuro-Fuzzy Systems Based on Weighted Incremental Neural Networks
2002 (English)In: 31st Applied Imagery Pattern Recognition Worshop (AIPR 2002), Washington DC, USA, 2002Conference paper, Published paper (Other scientific)
Abstract [en]

Segmenting hyperspectral images is an important task for simplifying the

Keywords
Hyperspectral images, Unsupervised Image Segmentation, Unsupervised Fuzzy Clustering, Neuro-Fuzzy Systems, Weighted IncrementalNeural Network (WINN), Watersheds.
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:uu:diva-42446 (URN)
Available from: 2005-08-25 Created: 2005-08-25 Last updated: 2018-01-11
14. A Comparison of Neuro-Fuzzy and Traditional Image Segmentation Methods for Automated Detection of Buildings in Aerial Photos
Open this publication in new window or tab >>A Comparison of Neuro-Fuzzy and Traditional Image Segmentation Methods for Automated Detection of Buildings in Aerial Photos
2002 (English)In: Proceedings of PCV'02: PHOTOGRAMMETRIC COMPUTER VISION 2002Article in journal (Refereed) Published
Identifiers
urn:nbn:se:uu:diva-93380 (URN)
Available from: 2005-09-05 Created: 2005-09-05 Last updated: 2010-03-01Bibliographically approved

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