SPECTRAL-SPATIAL CLASSIFICATION OF HYPERSPECTRAL IMAGERY USING NEURAL NETWORK ALGORITHM AND HIERARCHICAL SEGMENTATION Article Swipe

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Davood Akbari , Mina Moradizadeh , Mohammad Ghasem Akbari ·

YOU? · · 2019 · Open Access · · DOI: https://doi.org/10.5194/isprs-archives-xlii-2-w12-1-2019

This paper describes a new framework for classification of hyperspectral images, based on both spectral and spatial information. The spatial information is obtained by an enhanced Marker-based Hierarchical Segmentation (MHS) algorithm. The hyperspectral data is first fed into the Multi-Layer Perceptron (MLP) neural network classification algorithm. Then, the MHS algorithm is applied in order to increase the accuracy of less-accurately classified land-cover types. In the proposed approach, the markers are extracted from the classification maps obtained by MLP and Support Vector Machines (SVM) classifiers. Experimental results on Washington DC Mall hyperspectral dataset, demonstrate the superiority of proposed approach compared to the MLP and the original MHS algorithms.

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Computer Science

Artificial Intelligence

Support Vector Machine

Perceptron

Segmentation Fault

Multilayer Perceptron

Concepts

Hyperspectral imaging Pattern recognition (psychology) Computer science Artificial intelligence Support vector machine Perceptron Artificial neural network Segmentation Multilayer perceptron Spatial analysis Algorithm Remote sensing Geography

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Type: article
Language: en
Landing Page: https://doi.org/10.5194/isprs-archives-xlii-2-w12-1-2019
PDF: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W12/1/2019/isprs-archives-XLII-2-W12-1-2019.pdf
OA Status: diamond
References: 16
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OpenAlex ID: https://openalex.org/W2943877772

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DOI: https://doi.org/10.5194/isprs-archives-xlii-2-w12-1-2019

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Title: SPECTRAL-SPATIAL CLASSIFICATION OF HYPERSPECTRAL IMAGERY USING NEURAL NETWORK ALGORITHM AND HIERARCHICAL SEGMENTATION

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Language: en

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Publication year: 2019

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Publication date: 2019-05-09

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Authors: Davood Akbari, Mina Moradizadeh, Mohammad Ghasem Akbari

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Landing page: https://doi.org/10.5194/isprs-archives-xlii-2-w12-1-2019

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PDF URL: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W12/1/2019/isprs-archives-XLII-2-W12-1-2019.pdf

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Concepts: Hyperspectral imaging, Pattern recognition (psychology), Computer science, Artificial intelligence, Support vector machine, Perceptron, Artificial neural network, Segmentation, Multilayer perceptron, Spatial analysis, Algorithm, Remote sensing, Geography

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abstract_inverted_index.obtained	23, 76
abstract_inverted_index.original	105
abstract_inverted_index.proposed	66, 97
abstract_inverted_index.spectral	15
abstract_inverted_index.Abstract.	0
abstract_inverted_index.algorithm	50
abstract_inverted_index.approach,	67
abstract_inverted_index.describes	3
abstract_inverted_index.extracted	71
abstract_inverted_index.framework	6
abstract_inverted_index.Perceptron	41
abstract_inverted_index.Washington	88
abstract_inverted_index.algorithm.	31, 46
abstract_inverted_index.classified	61
abstract_inverted_index.land-cover	62
abstract_inverted_index.Multi-Layer	40
abstract_inverted_index.algorithms.	107
abstract_inverted_index.demonstrate	93
abstract_inverted_index.information	21
abstract_inverted_index.superiority	95
abstract_inverted_index.Experimental	85
abstract_inverted_index.Hierarchical	28
abstract_inverted_index.Marker-based	27
abstract_inverted_index.Segmentation	29
abstract_inverted_index.classifiers.	84
abstract_inverted_index.information.	18
abstract_inverted_index.hyperspectral	10, 33, 91
abstract_inverted_index.classification	8, 45, 74
abstract_inverted_index.less-accurately	60
cited_by_percentile_year
countries_distinct_count	1
institutions_distinct_count	3
sustainable_development_goals[0].id	https://metadata.un.org/sdg/15
sustainable_development_goals[0].score	0.5699999928474426
sustainable_development_goals[0].display_name	Life in Land
citation_normalized_percentile.value	0.04946297
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	False