Land-use land-cover classification by machine learning classifiers for satellite observations—A review
Rapid and uncontrolled population growth along with economic and industrial development,
especially in develo** countries during the late twentieth and early twenty-first centuries …
especially in develo** countries during the late twentieth and early twenty-first centuries …
[HTML][HTML] A review of supervised object-based land-cover image classification
L Ma, M Li, X Ma, L Cheng, P Du, Y Liu - ISPRS Journal of Photogrammetry …, 2017 - Elsevier
Object-based image classification for land-cover map** purposes using remote-sensing
imagery has attracted significant attention in recent years. Numerous studies conducted over …
imagery has attracted significant attention in recent years. Numerous studies conducted over …
[HTML][HTML] Performance analysis of the water quality index model for predicting water state using machine learning techniques
Existing water quality index (WQI) models assess water quality using a range of
classification schemes. Consequently, different methods provide a number of interpretations …
classification schemes. Consequently, different methods provide a number of interpretations …
Comparison of random forest and support vector machine classifiers for regional land cover map** using coarse resolution FY-3C images
T Adugna, W Xu, J Fan - Remote Sensing, 2022 - mdpi.com
The type of algorithm employed to classify remote sensing imageries plays a great role in
affecting the accuracy. In recent decades, machine learning (ML) has received great …
affecting the accuracy. In recent decades, machine learning (ML) has received great …
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 …
[HTML][HTML] Application of machine learning approaches for land cover monitoring in northern Cameroon
Abstract Machine learning (ML) models are a leading analytical technique used to monitor,
map and quantify land use and land cover (LULC) and its change over time. Models such as …
map and quantify land use and land cover (LULC) and its change over time. Models such as …
Deep recurrent neural network for agricultural classification using multitemporal SAR Sentinel-1 for Camargue, France
The development and improvement of methods to map agricultural land cover are currently
major challenges, especially for radar images. This is due to the speckle noise nature of …
major challenges, especially for radar images. This is due to the speckle noise nature of …
Remote sensing for wetland classification: A comprehensive review
Wetlands are valuable natural resources that provide many benefits to the environment.
Therefore, map** wetlands is crucially important. Several review papers on remote …
Therefore, map** wetlands is crucially important. Several review papers on remote …
Effects of training set size on supervised machine-learning land-cover classification of large-area high-resolution remotely sensed data
The size of the training data set is a major determinant of classification accuracy.
Nevertheless, the collection of a large training data set for supervised classifiers can be a …
Nevertheless, the collection of a large training data set for supervised classifiers can be a …
Supervised machine learning techniques to the prediction of tunnel boring machine penetration rate
Predicting the penetration rate is a complex and challenging task due to the interaction
between the tunnel boring machine (TBM) and the rock mass. Many studies highlight the …
between the tunnel boring machine (TBM) and the rock mass. Many studies highlight the …