Comparing deep neural networks, ensemble classifiers, and support vector machine algorithms for object-based urban land use/land cover classification

SE Jozdani, BA Johnson, D Chen - Remote Sensing, 2019 - mdpi.com
With the advent of high-spatial resolution (HSR) satellite imagery, urban land use/land cover
(LULC) mapping has become one of the most popular applications in remote sensing. Due …

Deep neural network ensembles for remote sensing land cover and land use classification

B Ekim, E Sertel - International Journal of Digital Earth, 2021 - Taylor & Francis
… available to be used for the Land Cover and Land Use (LCLU) classification task aiming to
… The ensembles of classifiers are constituted by aggregating multiple classifiers trained to …

An assessment of the effectiveness of a rotation forest ensemble for land-use and land-cover mapping

T Kavzoglu, I Colkesen - International journal of remote sensing, 2013 - Taylor & Francis
… in land-use and land-cover (LULC) mapping, and its performance was compared with
performances of the six most widely used ensemble … Studies have shown that ensemble classifiers

An ensemble learning approach for land use/land cover classification of arid regions for climate simulation: A case study of xinjiang, northwest China

H Du, M Li, Y Xu, C Zhou - IEEE Journal of Selected Topics in …, 2023 - ieeexplore.ieee.org
ensemble learning approach to divide Xinjiang into different subregions, accurately classify
land cover, and generate a new landland use/cover classification with a multiple classifier

Deep and ensemble learning based land use and land cover classification

H Benbriqa, I Abnane, A Idri, K Tabiti - … 13–16, 2021, Proceedings, Part III …, 2021 - Springer
… Finally, HybridEnsembles are ensembles combining all the classifiers we … use this newly
constructed dataset, to develop Land use & Land cover monitoring systems of Moroccan lands

Aggregative model-based classifier ensemble for improving land-use/cover classification of Landsat TM Images

X Li, X Liu, L Yu - International Journal of Remote Sensing, 2014 - Taylor & Francis
classifiers to achieve a high classification accuracy. In this article, classifier ensemble is
defined as a composite of two processes: ensemble learning and predictions combination. …

Ensemble classifiers in remote sensing: A review

R Saini, SK Ghosh - 2017 International Conference on …, 2017 - ieeexplore.ieee.org
… of ensemble classifiers reviews the different ensembleensemble classifier. In this paper,
more emphasis is given to RF classifier and various application domain of this ensemble

An ensemble learning approach for urban land use mapping based on remote sensing imagery and social sensing data

Z Huang, H Qi, C Kang, Y Su, Y Liu - Remote Sensing, 2020 - mdpi.com
… In addition, in order to find the best classifier for this task, we also test the … various classifiers
such as random forest. By comparing different classifiers, we find that the XGBoost classifier

Mapping long-term land use and land cover change in the central Himalayan region using a tree-based ensemble classification approach

A Chakraborty, K Sachdeva, PK Joshi - Applied Geography, 2016 - Elsevier
… We relied on using a combination of both ensemble classifier (for seasonal classified maps)
and knowledge-based decision level fusion (for annual composite maps) to produce annual …

[PDF][PDF] Land use/land cover classification using machine learning models.

S Swetanisha, AR Panda, DK Behera - International Journal of …, 2022 - academia.edu
… An ensemble classifier model makes the classification more … The contributions of the work
are i) land use and land cover … data; iii) designing an ensemble model by combining the output …