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Image classification based on sparse-coded features using sparse coding technique for aerial imagery: a hybrid dictionary approach

Aerial photography; Aircraft detection; Antennas; Codes (symbols); Discrete cosine transforms; Discrete wavelet transforms; Glossaries; Image classification; Image coding; Image enhancement; Learning algorithms; Learning systems; Object recognition; Remote sensing; Satellite imagery; Satellites; Unm...

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Main Authors:	Qayyum A., Saeed Malik A., Saad N.M., Iqbal M., Abdullah M.F., Rasheed W., Abdullah T.A.B.R., Bin Jafaar M.Y.
Other Authors:	57211138712
Format:	Article
Published:	Springer London 2023
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spelling	my.uniten.dspace-245502023-05-29T15:24:28Z Image classification based on sparse-coded features using sparse coding technique for aerial imagery: a hybrid dictionary approach Qayyum A. Saeed Malik A. Saad N.M. Iqbal M. Abdullah M.F. Rasheed W. Abdullah T.A.B.R. Bin Jafaar M.Y. 57211138712 12800348400 56567441400 54386959400 57188825497 24475459400 56594684600 57193519737 Aerial photography; Aircraft detection; Antennas; Codes (symbols); Discrete cosine transforms; Discrete wavelet transforms; Glossaries; Image classification; Image coding; Image enhancement; Learning algorithms; Learning systems; Object recognition; Remote sensing; Satellite imagery; Satellites; Unmanned aerial vehicles (UAV); Discrete tchebichef transforms; Discriminative features; Finite Ridgelet Transform; Histogram of oriented gradients; Image processing and computer vision; Scale invariant feature transforms; SIFT; Sparse coding; Classification (of information) This work offers an approach to aerial image classification for use in remote sensing object recognition, image processing and computer vision. Sparse coding (SC) is used to classify unmanned-aerial-vehicle (UAV) and satellite images because SC representation can generalize a large dataset and improve the detection of distinctive features by reducing calculation time for feature matching and classification. Features from images are extracted based on the following descriptors: (a) Scale Invariant Feature Transform; (b) Histogram of Oriented Gradients; and (c) Local Binary Patterns. SC representation and local image features are combined to represent global features for classification. Features are deployed in a sparse model to store descriptor features using extant dictionaries such as (a) the Discrete Cosine Transform and (b) the Discrete Wavelet Transform. An additional two dictionaries are proposed as developed for the present work: (c) the Discrete Ridgelet Transform (DRT) and (d) the Discrete Tchebichef Transform. The DRT dictionary is constructed by using the Ricker wavelet function to generate finite Ridgelet transforms as basis elements for a hybrid dictionary. Different pooling methods have also been employed to convert sparse-coded features into a feature matrix. Various machine learning algorithms are then applied to the feature matrix to classify objects contained in UAV and satellite imagery data. Experimental results show that the SC model secured better accuracy rates for extracted discriminative features contained in remote sensing images. The authors concluded that the proposed SC technique and proposed dictionaries provided feasible solutions for image classification and object recognition. � 2017, The Natural Computing Applications Forum. Final 2023-05-29T07:24:28Z 2023-05-29T07:24:28Z 2019 Article 10.1007/s00521-017-3300-5 2-s2.0-85039749366 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85039749366&doi=10.1007%2fs00521-017-3300-5&partnerID=40&md5=6fe11b9fa23c604501e4d591eedc41b5 https://irepository.uniten.edu.my/handle/123456789/24550 31 8 3587 3607 Springer London Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
description	Aerial photography; Aircraft detection; Antennas; Codes (symbols); Discrete cosine transforms; Discrete wavelet transforms; Glossaries; Image classification; Image coding; Image enhancement; Learning algorithms; Learning systems; Object recognition; Remote sensing; Satellite imagery; Satellites; Unmanned aerial vehicles (UAV); Discrete tchebichef transforms; Discriminative features; Finite Ridgelet Transform; Histogram of oriented gradients; Image processing and computer vision; Scale invariant feature transforms; SIFT; Sparse coding; Classification (of information)
author2	57211138712
author_facet	57211138712 Qayyum A. Saeed Malik A. Saad N.M. Iqbal M. Abdullah M.F. Rasheed W. Abdullah T.A.B.R. Bin Jafaar M.Y.
format	Article
author	Qayyum A. Saeed Malik A. Saad N.M. Iqbal M. Abdullah M.F. Rasheed W. Abdullah T.A.B.R. Bin Jafaar M.Y.
spellingShingle	Qayyum A. Saeed Malik A. Saad N.M. Iqbal M. Abdullah M.F. Rasheed W. Abdullah T.A.B.R. Bin Jafaar M.Y. Image classification based on sparse-coded features using sparse coding technique for aerial imagery: a hybrid dictionary approach
author_sort	Qayyum A.
title	Image classification based on sparse-coded features using sparse coding technique for aerial imagery: a hybrid dictionary approach
title_short	Image classification based on sparse-coded features using sparse coding technique for aerial imagery: a hybrid dictionary approach
title_full	Image classification based on sparse-coded features using sparse coding technique for aerial imagery: a hybrid dictionary approach
title_fullStr	Image classification based on sparse-coded features using sparse coding technique for aerial imagery: a hybrid dictionary approach
title_full_unstemmed	Image classification based on sparse-coded features using sparse coding technique for aerial imagery: a hybrid dictionary approach
title_sort	image classification based on sparse-coded features using sparse coding technique for aerial imagery: a hybrid dictionary approach
publisher	Springer London
publishDate	2023
_version_	1806428434795069440
score	13.214268

Image classification based on sparse-coded features using sparse coding technique for aerial imagery: a hybrid dictionary approach

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