AmirAbbas Davari, M. Sc.
Development of Vector-Based Mathematical Morphology for Hyper-spectral Remote Sensing Image Description and Classification
(Non-FAU Project)Term: since March 1, 2016
Remote sensing is nowadays of paramount importance for several application fields, including environmental monitoring, urban planning, ecosystem-oriented natural resources management, urban change detection and agricultural region monitoring. Majority of the aforementioned monitoring and detection applications requires at some stage a label map of the remotely sensed images, where individual pixels are marked as members of specific classes, e.g. water, asphalt, grass, etc. In other words, classification is a crucial step for several remote sensing applications. It is widely acknowledged that exploiting both the spectral as well as spatial properties of pixels, improves classification performance with respect to using only spectral based features.
In this regard, morphological profiles (MP) are one of the popular and powerful image analysis techniques that enable us to compute such spectral-spatial pixel descriptions. They have been studied extensively in the last decade and their effectiveness has been validated repeatedly.
The characterization of spatial information obtained by the application of a MP is particularly suitable for representing the multi-scale variations of image structures, but they are limited by the shape of the structuring elements. To avoid this limitation, morphological attribute profiles (AP) have been developed. By operating directly on connected components instead of pixels, not only we are able to employ arbitrary region descriptors (e.g. shape, color, texture, etc) but it paves the way for object based image analysis as well. In addition, APs can be implemented efficiently by means of hierarchical image representations, e.g. Max-/Min-tree and alpha-tree.
The aforementioned proposed techniques for hyper-spectral remote sensing image analysis are basically based on marginal processing of the image, i.e. analyzing each spectral channel individually and not simultaneously. Therefore, the channels’ correlation is neglected in the conventional marginal approaches.
Motivated from that, our project focuses on extending the mathematical morphology to the field of hyper-spectral image processing and applying morphological content based operators, e.g. MP and AP, on all of the spectral bands simultaneously rather than marginally in order to take the spectral channels’ correlation into account.
Tapping the Potential of Earth Observation - Calving Front Detection in SAR Images using Deep Learning Techniques
EGU General Assembly 2021 (, April 19, 2021 - May 30, 2021)
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Fast and Efficient Limited Data Hyperspectral Remote Sensing Image Classification via GMM-Based Synthetic Samples
In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12 (2019), p. 2107-2120
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GMM-based Synthetic Samples for Classification of Hyperspectral Images with Limited Training Data
In: IEEE Geoscience and Remote Sensing Letters 15 (2018), p. 942-946
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Fast Sample Generation with Variational Bayesian for Limited Data Hyperspectral Image Classification
IEEE International Geoscience and Remote Sensing Symposium (Valencia, July 22, 2018 - July 27, 2018)
In: IEEE International Geoscience and Remote Sensing Symposium 2018
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Classification of Breast Cancer Histology Images Using Transfer Learning
International Conference Image Analysis and Recognition (, June 12, 2018)
In: ICIAR 2018: Image Analysis and Recognition 2018
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Hyper-Hue and EMAP on Hyperspectral Images for Supervised Layer Decomposition of Old Master Drawings
International Conference on Image Processing (ICIP) (Athens, Greece, October 7, 2018 - October 10, 2018)
In: International Conference on Image Processing (ICIP) 2018
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GMM Supervectors for Limited Training Data in Hyperspectral Remote Sensing Image Classification
17th International Conference on Computer Analysis of Images and Patterns (Ystad, August 22, 2017 - August 24, 2017)
In: Proceedings of the 17th International Conference on Computer Analysis of Images and Patterns Part II 2017
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Sketch Layer Separation in Multi-Spectral Historical Document Images
International Symposium on Digital Humanities
In: International Symposium on Digital Humanities: Book of Abstracts 2017
Open Access: https://journals.lnu.se/index.php/isdh/article/download/431/378
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Semi-Automatic Algorithm for Breast MRI Lesion Segmentation Using Marker-Controlled Watershed Transformation
IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC) (Atlanta, Georgia, USA)
In: 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC) 2017
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Image Registration for the Alignment of Digitized Historical Documents
Open Access: https://arxiv.org/abs/1712.04482
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