Adaptive Deep Co-Occurrence Feature Learning Based on Classifier-Fusion for Remote Sensing Scene Classification

Remote sensing scene classification has numerous applications on land cover land use. However, classifying the scene images into their correct categories is a challenging task. This challenge is attributable to the diverse semantics of remote sensing images. This nature of remote sensing images make...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Ronald Tombe, Serestina Viriri
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Adaptive deep co-occurrence learning deep feature extraction ensemble learning machine learning multigrained forests scene classification Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
Acceso en línea:	https://doaj.org/article/8375e9d955184c62b7a659acda78357a
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:8375e9d955184c62b7a659acda78357a
record_format	dspace
spelling	oai:doaj.org-article:8375e9d955184c62b7a659acda78357a2021-11-20T00:00:13ZAdaptive Deep Co-Occurrence Feature Learning Based on Classifier-Fusion for Remote Sensing Scene Classification2151-153510.1109/JSTARS.2020.3044264https://doaj.org/article/8375e9d955184c62b7a659acda78357a2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9291440/https://doaj.org/toc/2151-1535Remote sensing scene classification has numerous applications on land cover land use. However, classifying the scene images into their correct categories is a challenging task. This challenge is attributable to the diverse semantics of remote sensing images. This nature of remote sensing images makes the task of effective feature extraction and learning complex. Effective image feature representation is essential in image analysis and interpretation for accurate scene image classification with machine learning algorithms. The recent literature shows that convolutional neural networks are mighty in feature extraction for remote sensing scene classification. Additionally, recent literature shows that classifier-fusion attains superior results than individual classifiers. This article proposes the adaptive deep co-accordance feature learning (ADCFL). The ADCFL method utilizes a convolutional neural network to extract spatial feature information from an image in a co-occurrence manner with filters, and then this information is fed to the multigrain forest for feature learning and classification through majority votes with ensemble classifiers. An evaluation of the effectiveness of ADCFL is conducted on the public datasets Resisc45 and Ucmerced. The classification accuracy results attained by the ADCFL demonstrate that the proposed method achieves improved results.Ronald TombeSerestina ViririIEEEarticleAdaptive deep co-occurrence learningdeep feature extractionensemble learningmachine learningmultigrained forestsscene classificationOcean engineeringTC1501-1800Geophysics. Cosmic physicsQC801-809ENIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 155-164 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Adaptive deep co-occurrence learning deep feature extraction ensemble learning machine learning multigrained forests scene classification Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
spellingShingle	Adaptive deep co-occurrence learning deep feature extraction ensemble learning machine learning multigrained forests scene classification Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 Ronald Tombe Serestina Viriri Adaptive Deep Co-Occurrence Feature Learning Based on Classifier-Fusion for Remote Sensing Scene Classification
description	Remote sensing scene classification has numerous applications on land cover land use. However, classifying the scene images into their correct categories is a challenging task. This challenge is attributable to the diverse semantics of remote sensing images. This nature of remote sensing images makes the task of effective feature extraction and learning complex. Effective image feature representation is essential in image analysis and interpretation for accurate scene image classification with machine learning algorithms. The recent literature shows that convolutional neural networks are mighty in feature extraction for remote sensing scene classification. Additionally, recent literature shows that classifier-fusion attains superior results than individual classifiers. This article proposes the adaptive deep co-accordance feature learning (ADCFL). The ADCFL method utilizes a convolutional neural network to extract spatial feature information from an image in a co-occurrence manner with filters, and then this information is fed to the multigrain forest for feature learning and classification through majority votes with ensemble classifiers. An evaluation of the effectiveness of ADCFL is conducted on the public datasets Resisc45 and Ucmerced. The classification accuracy results attained by the ADCFL demonstrate that the proposed method achieves improved results.
format	article
author	Ronald Tombe Serestina Viriri
author_facet	Ronald Tombe Serestina Viriri
author_sort	Ronald Tombe
title	Adaptive Deep Co-Occurrence Feature Learning Based on Classifier-Fusion for Remote Sensing Scene Classification
title_short	Adaptive Deep Co-Occurrence Feature Learning Based on Classifier-Fusion for Remote Sensing Scene Classification
title_full	Adaptive Deep Co-Occurrence Feature Learning Based on Classifier-Fusion for Remote Sensing Scene Classification
title_fullStr	Adaptive Deep Co-Occurrence Feature Learning Based on Classifier-Fusion for Remote Sensing Scene Classification
title_full_unstemmed	Adaptive Deep Co-Occurrence Feature Learning Based on Classifier-Fusion for Remote Sensing Scene Classification
title_sort	adaptive deep co-occurrence feature learning based on classifier-fusion for remote sensing scene classification
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/8375e9d955184c62b7a659acda78357a
work_keys_str_mv	AT ronaldtombe adaptivedeepcooccurrencefeaturelearningbasedonclassifierfusionforremotesensingsceneclassification AT serestinaviriri adaptivedeepcooccurrencefeaturelearningbasedonclassifierfusionforremotesensingsceneclassification
_version_	1718419844306567168

Adaptive Deep Co-Occurrence Feature Learning Based on Classifier-Fusion for Remote Sensing Scene Classification

Ejemplares similares