Support vector machine versus random forest for remote sensing image classification: A meta-analysis and systematic review
M Sheykhmousa, M Mahdianpari… - IEEE Journal of …, 2020 - ieeexplore.ieee.org
Several machine-learning algorithms have been proposed for remote sensing image
classification during the past two decades. Among these machine learning algorithms …
classification during the past two decades. Among these machine learning algorithms …
Automation in agriculture by machine and deep learning techniques: A review of recent developments
Recently, agriculture has gained much attention regarding automation by artificial
intelligence techniques and robotic systems. Particularly, with the advancements in machine …
intelligence techniques and robotic systems. Particularly, with the advancements in machine …
Comparison of land use land cover classifiers using different satellite imagery and machine learning techniques
Accurate land use land cover (LULC) classification is vital for the sustainable management
of natural resources and to learn how the landscape is changing due to climate. For …
of natural resources and to learn how the landscape is changing due to climate. For …
Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data
AM Abdi - GIScience & Remote Sensing, 2020 - Taylor & Francis
In recent years, the data science and remote sensing communities have started to align due
to user-friendly programming tools, access to high-end consumer computing power, and the …
to user-friendly programming tools, access to high-end consumer computing power, and the …
Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using Sentinel-2 imagery
P Thanh Noi, M Kappas - Sensors, 2017 - mdpi.com
In previous classification studies, three non-parametric classifiers, Random Forest (RF), k-
Nearest Neighbor (kNN), and Support Vector Machine (SVM), were reported as the foremost …
Nearest Neighbor (kNN), and Support Vector Machine (SVM), were reported as the foremost …
Land use/land cover prediction and analysis of the middle reaches of the Yangtze River under different scenarios
Land use and land cover (LULC) projections are critical for climate models to predict the
impacts of LULC change on the Earth system. Different assumptions and policies influence …
impacts of LULC change on the Earth system. Different assumptions and policies influence …
Phenology-assisted supervised paddy rice mapping with the Landsat imagery on Google Earth Engine: Experiments in Heilongjiang Province of China from 1990 to …
Accurate spatial distribution maps of paddy rice played crucial roles in food security and
market stability. Decades-spanning Landsat images were useful for long-term paddy rice …
market stability. Decades-spanning Landsat images were useful for long-term paddy rice …
[HTML][HTML] A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud …
Mapping high resolution (30-m or better) cropland extent over very large areas such as
continents or large countries or regions accurately, precisely, repeatedly, and rapidly is of …
continents or large countries or regions accurately, precisely, repeatedly, and rapidly is of …
Radiomics in breast cancer classification and prediction
Breast Cancer (BC) is the common form of cancer in women. Its diagnosis and screening are
usually performed through different imaging modalities such as mammography, magnetic …
usually performed through different imaging modalities such as mammography, magnetic …
A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach
Accurate and timely spatial classification of crop types based on remote sensing data is
important for both scientific and practical purposes. Spatially explicit crop-type information …
important for both scientific and practical purposes. Spatially explicit crop-type information …