Comparing deep neural networks, ensemble classifiers, and support vector machine algorithms for object-based urban land use/land cover classification
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 …
(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
… 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 …
… 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 …
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 land … land use/cover classification with a multiple classifier …
land cover, and generate a new land … land use/cover classification with a multiple classifier …
Deep and ensemble learning based land use and land cover classification
… 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…
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
… 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. …
defined as a composite of two processes: ensemble learning and predictions combination. …
Ensemble classifiers in remote sensing: A review
… of ensemble classifiers reviews the different ensemble … ensemble classifier. In this paper,
more emphasis is given to RF classifier and various application domain of this ensemble …
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
… 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 …
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 …
and knowledge-based decision level fusion (for annual composite maps) to produce annual …
[PDF][PDF] Land use/land cover classification using machine learning models.
… 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 …
are i) land use and land cover … data; iii) designing an ensemble model by combining the output …
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