Výsledky vyhledávání - "Ronald Tombe"

Akademický článek

Remote Sensing Image Scene Classification: Advances and Open Challenges

Publikováno v: Geomatics, Vol 3, Iss 1, Pp 137-155 (2023)

Deep learning approaches are gaining popularity in image feature analysis and in attaining state-of-the-art performances in scene classification of remote sensing imagery. This article presents a comprehensive review of the developments of various co

Externí odkaz: https://doaj.org/article/6e6a9ba6ae524fb2a7264c32dc1e1770

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Akademický článek

Agricultural Social Networks: An Agricultural Value Chain-Based Digitalization Framework for an Inclusive Digital Economy

Autor: Ronald Tombe, Hanlie Smuts

Publikováno v: Applied Sciences, Vol 13, Iss 11, p 6382 (2023)

Sustainable agriculture is the backbone of food security systems and a driver of human well-being in global economic development (Sustainable Development Goal SDG 3). With the increase in world population and the effects of climate change due to the

Externí odkaz: https://doaj.org/article/ca8deac9e044421e94d470c05fdf161f

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Akademický článek

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

Autor: Ronald Tombe, Serestina Viriri

Publikováno v: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 155-164 (2021)

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

Externí odkaz: https://doaj.org/article/8375e9d955184c62b7a659acda78357a

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Society 5.0-Inspired Digitalization Framework for Resilient and Sustainable Agriculture

Autor: Ronald Tombe, Hanlie Smuts

Publikováno v: EPiC Series in Computing.

This research paper proposes a digitalization framework based on Society 5.0 principles for promoting resilient and sustainable agricultural value chains in the context of climate change. Climate change is affecting the productivity and sustainabilit

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::c98d51f1e563f8995677c0baa0269b7e
https://doi.org/10.29007/xc5q

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

Autor: Serestina Viriri, Ronald Tombe

Publikováno v: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 155-164 (2021)

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::211e7a7383b71bb5b785b3de11eb05e6
https://doi.org/10.1109/jstars.2020.3044264

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Effective Processing of Convolutional Neural Networks for Computer Vision: A Tutorial and Survey

Autor: Serestina Viriri, Ronald Tombe

Publikováno v: IETE Technical Review. 39:49-62

Over the past few years, Convolutional neural networks (ConvNets) is emerging as computer vision discipline within deep learning. ConvNets is a key strategy for addressing computer vision problems,...

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::5d0cb6f3f2d80ed348e2e75a842d5294
https://doi.org/10.1080/02564602.2020.1823252

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Towards Exploiting Convolutional Features for Remote Sensing Images Scene Classification

Autor: Ronald Tombe, Serestina Viriri

Publikováno v: Recent Challenges in Intelligent Information and Database Systems ISBN: 9789811616846
ACIIDS (Companion)

The developments in deep learning have shifting the research directions of remote sensing image scene understanding and classification from pixel-to-pixel handcrafted methods to scene-level image semantics analysis for scene classification tasks. The

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f4b67a810ae121d08692a05e5a7d18a8
https://doi.org/10.1007/978-981-16-1685-3_22

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Remote Sensing Scene Classification Based on Effective Feature Learning by Deep Residual Networks

Autor: Ronald Tombe, Serestina Viriri

Publikováno v: Machine Learning for Networking ISBN: 9783030708658
MLN

Remote sensing image scene interpretation has many applications on land use land covers; thanks to many satellite technologies innovations that generate high-quality images periodically for analysis and interpretation through computer vision techniqu

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::02aa059dcdc044e092c391d10a48eb1c
https://doi.org/10.1007/978-3-030-70866-5_21

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Scene Classification of Remote Sensing Images Based on ConvNet Features and Multi-grained Forest

Autor: Jean Vincent Fonou Dombeu, Ronald Tombe, Serestina Viriri

Publikováno v: Advances in Visual Computing ISBN: 9783030645557
ISVC (1)

Scene interpretation of remote sensing images entails effective spatial feature information extraction and application of an appropriate pattern recognition algorithm for feature learning. In literature, state-of-the-art results are attained in remot

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::7cfae029caf1e1d543ad1825d7828c8c
https://doi.org/10.1007/978-3-030-64556-4_57

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Local Descriptors Parameter characterization with Fisher vectors for remote sensing images

Autor: Ronald Tombe, Serestina Viriri

Publikováno v: ICTAS

Satellite technology yield huge quantities of high spatial resolution(HSR) images periodically. The HSR type of data is complex in its spatial arrangement with high intraclass and low interclass variability. Remote sensing images scene classification

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a9bcf21dfd82efc0a28225b8b9676db8
https://doi.org/10.1109/ictas.2019.8703623

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