Isolation Forests to Evaluate Class Separability and the Representativeness of Training and Validation Areas in Land Cover Classification

Remote Sensing - Switzerland
doi 10.3390/rs11243000
Full Text
Abstract

Available in full text

Date
Authors
Publisher

MDPI AG

Related search

Contextual Land-Cover Classification: Incorporating Spatial Dependence in Land-Cover Classification Models Using Random Forests and the Getis Statistic

Remote Sensing Letters
EarthPlanetary SciencesElectricalElectronic Engineering
2010English

Land Cover Classification Method With Consideration of Mixel Existence and Representativeness of Training Data, Using Time-Series and Low Spatial Resolution Satellite Images.

Journal of the Japan society of photogrammetry and remote sensing
2001English

A Review of Fine-Scale Land Use and Land Cover Classification in Open-Pit Mining Areas by Remote Sensing Techniques

Remote Sensing
EarthPlanetary Sciences
2017English

Land Cover Classification of Urban and Peri-Urban Areas Using Object-Oriented Analysis of Multispectral Imagery

Hrvatski Geografski Glasnik
DevelopmentEarth-Surface ProcessesPlanningGeography
2015English

The Problem of Translation Representativeness and the Translatalogical Classification of Texts

RUDN Journal of Language Education and Translingual Practices
2017English

A Systematic Review of Meta-Analyses That Evaluate Risk Factors for Dementia to Evaluate the Quantity, Quality, and Global Representativeness of Evidence

Journal of Alzheimer's Disease
GerontologyClinical PsychologyMental HealthPsychiatryGeriatricsMedicineNeuroscience
2019English

Land Use and Land Cover Mapping Using Rule-Based Classification in Karbala City, Iraq

Lecture Notes in Civil Engineering
CivilStructural Engineering
2018English

A Hierarchical Approach of Hybrid Image Classification for Land Use and Land Cover Mapping

Geographica Pannonica
DevelopmentHospitality ManagementGeologyLeisureAtmospheric SciencePlanningEarth-Surface ProcessesTourismGeography
2018English

Potential of Different Optical and SAR Data in Forest and Land Cover Classification to Support REDD+ MRV

Remote Sensing
EarthPlanetary Sciences
2018English

Amanote Research

Note-taking for researchers

Follow Amanote

© 2024 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy