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Home / programming language / Online Matlab Projects / A Spectral – Spatial Multicriteria Active Learning Technique for Hyperspectral Image Classification
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A Spectral – Spatial Multicriteria Active Learning Technique for Hyperspectral Image Classification

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SKU: PROJ8376 Categories: 2018-2019 Projects, Digital Image Processing Projects, Online Matlab Projects Tags: Android Project, Asp.Net Project, C# Project, Computer Engineering Final Year Projects, Computer Science Final Year Projects, Electronics Projects Engineering Students Final Year, Engineering Student Project Ideas, Final Semester Projects, Final Year Project Center, Final Year Projects, ieee ECE Projects, ieee EEE Projects, ieee Final Year Projects, IEEE Projects, ieee Projects Networking, Image Processing Projects, J2EE Project, Java Project, Matlab Project, PHP Project, Students Projects
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A Spectral – Spatial Multicriteria Active Learning Technique for Hyperspectral Image Classification

Abstract– Hyperspectral image classification with limited labeled samples is a challenging task and still an open research issue. In this article, a novel technique is presented to address such an issue by exploiting dimensionality reduction, spectral-spatial information, and classification with active learning. The proposed technique is based on two phases. Considering the importance of dimensionality reduction and spatial information for the analysis of hyperspectral images, Phase I generates the patterns corresponding to each pixel of the image using both spectral and spatial information. To this end, first, principal component analysis is used to reduce the dimensionality of an hyperspectral image,thenextended morphological profiles are exploited. The spectral-spatial information based patterns generated by extended morphological profiles are used as input to the Phase II. Phase II performs the classification task guided by an active learning technique. This technique is based on a novel query function that uses uncertainty, diversity, and cluster assumption criteria by exploiting the properties of k-means clustering, K-nearest neighbors algorithm, support vector machines, and genetic algorithms. Experiments on three benchmark hyperspectral datasets demonstrate that the proposed method outperforms five state-of-the-art active learning methods.

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