Implementation of machine-learning classification in remote sensing: An applied review
Machine learning offers the potential for effective and efficient classification of remotely
sensed imagery. The strengths of machine learning include the capacity to handle data of …
sensed imagery. The strengths of machine learning include the capacity to handle data of …
Global characterization and monitoring of forest cover using Landsat data: opportunities and challenges
The compilation of global Landsat data-sets and the ever-lowering costs of computing now
make it feasible to monitor the Earth's land cover at Landsat resolutions of 30 m. In this …
make it feasible to monitor the Earth's land cover at Landsat resolutions of 30 m. In this …
Bagging and boosting ensemble classifiers for classification of multispectral, hyperspectral and PolSAR data: a comparative evaluation
In recent years, several powerful machine learning (ML) algorithms have been developed
for image classification, especially those based on ensemble learning (EL). In particular …
for image classification, especially those based on ensemble learning (EL). In particular …
[图书][B] Statistical pattern recognition
AR Webb - 2003 - books.google.com
Statistical pattern recognition is a very active area of study andresearch, which has seen
many advances in recent years. New andemerging applications-such as data mining, web …
many advances in recent years. New andemerging applications-such as data mining, web …
[图书][B] Computer processing of remotely-sensed images
Computer Processing of Remotely-Sensed Images A thorough introduction to computer
processing of remotely-sensed images, processing methods, and applications Remote …
processing of remotely-sensed images, processing methods, and applications Remote …
Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral …
JCW Chan, D Paelinckx - Remote Sensing of Environment, 2008 - Elsevier
Detailed land use/land cover classification at ecotope level is important for environmental
evaluation. In this study, we investigate the possibility of using airborne hyperspectral …
evaluation. In this study, we investigate the possibility of using airborne hyperspectral …
Coastal wetland mapping using ensemble learning algorithms: A comparative study of bagging, boosting and stacking techniques
Coastal wetlands are a critical component of the coastal landscape that are increasingly
threatened by sea level rise and other human disturbance. Periodically mapping wetland …
threatened by sea level rise and other human disturbance. Periodically mapping wetland …
Hydrogeochemical evaluation for human health risk assessment from contamination of coastal groundwater aquifers of Indo-Bangladesh Ramsar site
D Ruidas, SC Pal, I Chowdhuri, A Saha… - Journal of Cleaner …, 2023 - Elsevier
The suitability of groundwater (GW) and the corresponding health risk caused by heavy
metals become prime concerns in the recent era; so, the determination of GW quality most …
metals become prime concerns in the recent era; so, the determination of GW quality most …
Remote sensing image classification using an ensemble framework without multiple classifiers
Recently, ensemble multiple deep learning (DL) classifiers has been reported to be an
effective method for improving remote sensing classification accuracy. Although these …
effective method for improving remote sensing classification accuracy. Although these …
Mapping US forest biomass using nationwide forest inventory data and moderate resolution information
JA Blackard, MV Finco, EH Helmer, GR Holden… - Remote sensing of …, 2008 - Elsevier
A spatially explicit dataset of aboveground live forest biomass was made from ground
measured inventory plots for the conterminous US, Alaska and Puerto Rico. The plot data …
measured inventory plots for the conterminous US, Alaska and Puerto Rico. The plot data …