Proceedings ArticleDOI
Supervised Building Extraction Using Morphological Profiles with Adaptive Structures
Hasnat Khurshid,Muhammad Faisal Khan,A. Ahmed +2 more
- pp 96-100
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TLDR
In this paper, the authors quantitatively evaluated the fact that the classification accuracy of each profile is dependent on the size and shape of the structural element and proposed a classification scheme which uses morphological profiles with adaptive structuring element.Abstract:
Morphological profiles are one of the highly effective tools for image classification when structural information is critical Morphological profiles, however create high dimensional feature space and increase the complexity of classifier In this paper we have quantitatively evaluated the fact that the classification accuracy of each profile is dependent on the size and shape of the structuring element We propose a classification scheme which uses morphological profiles with adaptive structuring element We relate the shape and size of structuring element which is used for producing morphological profiles with the discrete wavelet transform of the image The size and shape of structuring element adapts to the frequency content of the pixel's neighborhood With this adaptive structuring element we can produce a single profile which is quite effective for classification The results show that with proposed scheme significant improvement was obtained in the classification accuracy with reduced dimensionality of feature spaceread more
Citations
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Journal ArticleDOI
Construction Algorithm for Adaptive Morphological Structuring Elements Based on the Neighborhood Gray Difference Changing Vector Field and Relative Density
TL;DR: A new construction algorithm for adaptive structuring elements is proposed based on the neighborhood gray difference changing vector field and relative density that is able to adaptively change shape according to the gray and edge characteristics of an image.
Proceedings ArticleDOI
Experimental Validation of Population-based Optimization Algorithms for Construction of Adaptive Structuring Elements in VHR Image Classification
TL;DR: In this paper , the authors proposed an experimental methodology to validate the performance of six population-based optimization algorithms (POAs), namely, Artificial Immune System (AIS), Ant Colony Optimization (ACO), Artificial Fish Swarm Algorithm (AFSA), Genetic Algorithms (GA), Particle Swarm Optimisation (PSO) and Parallel PSO (PPSO), for construction of adaptive SEs in VHR image classification.
Proceedings ArticleDOI
Experimental Validation of Population-based Optimization Algorithms for Construction of Adaptive Structuring Elements in VHR Image Classification
TL;DR: This paper proposes an experimental methodology to validate the performance of six POAs, namely, Artificial Immune System (AIS), Ant Colony Optimization (ACO), Artificial Fish Swarm Algorithm (AFSA), Genetic Algorithms (GA), Particle Swarmoptimization (PSO) and Parallel PSO (PPSO), for construction of adaptive SEs in VHR image classification.
References
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A systematic analysis of performance measures for classification tasks
Marina Sokolova,Guy Lapalme +1 more
TL;DR: This paper presents a systematic analysis of twenty four performance measures used in the complete spectrum of Machine Learning classification tasks, i.e., binary, multi-class,multi-labelled, and hierarchical, to produce a measure invariance taxonomy with respect to all relevant label distribution changes in a classification problem.
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A new approach for the morphological segmentation of high-resolution satellite imagery
TL;DR: The proposed method performs well in the presence of both low radiometric contrast and relatively low spatial resolution, which may produce a textural effect, a border effect, and ambiguity in the object/background distinction.
Journal ArticleDOI
Morphological Attribute Profiles for the Analysis of Very High Resolution Images
TL;DR: The classification maps obtained by considering different APs result in a better description of the scene than those obtained with an MP, and the usefulness of APs in modeling the spatial information present in the images is proved.
Journal ArticleDOI
Classification of Hyperspectral Images by Using Extended Morphological Attribute Profiles and Independent Component Analysis
TL;DR: A technique based on independent component analysis (ICA) and extended morphological attribute profiles (EAPs) is presented for the classification of hyperspectral images and the effectiveness of the proposed technique was proved.
Journal ArticleDOI
Kernel principal component analysis for the classification of hyperspectral remote sensing data over urban areas
TL;DR: Experimental results presented in this paper confirm the usefulness of the KPCA for the analysis of hyperspectral data and improve results in terms of accuracy.