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RANDOM FOREST CLASSIFICATION OF JAMBI AND SOUTH SUMATERA USING ALOS PALSAR DATA

Mulia Inda Rahayu, Katmoko Ari Sambodo
2014 International Journal of Remote Sensing and Earth Sciences (IJReSES)  
The objective of this study was to determine an alternative method for land cover classification of ALOS-PALSAR data using Random Forest (RF) classifier.  ...  In this paper, the performance of the RF classifier for land cover classification of a complex area was explored using ALOS PALSAR data (25m mosaic, dual polarization) in the area of Jambi and South Sumatra  ...  ACKNOWLEDGMENT The authors would like to thank JAXA for providing the ALOS PALSAR 25m mosaic data within the framework of the JAXA Kyoto & Carbon Initiative.  ... 
doi:10.30536/j.ijreses.2013.v10.a1852 fatcat:pdgogjfudjcdbgg34fjszxljpe

Leveraging Google Earth Engine user interface for semi-automated wetland classification in the Great Lakes Basin at 10 m with optical and radar geospatial datasets

Vanessa Lynne Valenti, Erica C. Carcelen, Kathleen Lange, Nicholas J. Russo, Bruce Chapman
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
WET leverages cloud-computing for multisource processing of moderate resolution remote sensing data, and employs a user interface in Google Earth Engine that wetland managers and conservationists can use  ...  In this study, we present a graphical user interface constructed in Google Earth Engine called the Wetland Extent Tool (WET), which allows semiautomatic wetland classification according to a user-input  ...  American Land Change Monitoring System (NALCMS) 2015 land cover, which is the latest version of 30 m land cover data for the US and Canada based on Landsat 7 imagery.  ... 
doi:10.1109/jstars.2020.3023901 fatcat:yib7ndbqtvggbotuwj264s5hwq

Multisource Data Fusion Framework for Land Use/Land Cover Classification Using Machine Vision

Salman Qadri, Dost Muhammad Khan, Syed Furqan Qadri, Abdul Razzaq, Nazir Ahmad, Mutiullah Jamil, Ali Nawaz Shah, Syed Shah Muhammad, Khalid Saleem, Sarfraz Ahmad Awan
2017 Journal of Sensors  
A novel framework for multispectral and texture feature based data fusion is designed to identify the land use/land cover data types correctly.  ...  This study describes the data fusion of five land use/cover types, that is, bare land, fertile cultivated land, desert rangeland, green pasture, and Sutlej basin river land derived from remote sensing.  ...  Dell Nantt, CROPSCAN Corporation, Minnesota, USA, for their technical support.  ... 
doi:10.1155/2017/3515418 fatcat:wibv2q6xozhwlmjupvxfphrx4e

Urban Land Use and Land Cover Classification Using Multisource Remote Sensing Images and Social Media Data

Shi, Qi, Liu, Niu, Zhang
2019 Remote Sensing  
A method that integrates object-based image analysis, decision trees, and random forests was used for LULC classification.  ...  Remotely sensed images are commonly used for land cover classification but hardly identifies urban land use and functional areas because of the semantic gap (i.e., different definitions of similar or identical  ...  Acknowledgments: We would like to gratefully thank the anonymous reviewers for their insightful and helpful comments to improve the manuscript.  ... 
doi:10.3390/rs11222719 fatcat:uo2verzynratvc2udnho6ufc6q

Development of a global 30 m impervious surface map using multisource and multitemporal remote sensing datasets with the Google Earth Engine platform

Xiao Zhang, Liangyun Liu, Changshan Wu, Xidong Chen, Yuan Gao, Shuai Xie, Bing Zhang
2020 Earth System Science Data  
The results indicated that the global impervious surface map produced using the proposed multisource, multitemporal random forest classification (MSMT_RF) method was the most accurate of the maps, having  ...  Then, the local adaptive random forest classifiers, allowing for a regional adjustment of the classification parameters to take into account the regional characteristics, were trained and used to generate  ...  We gratefully acknowledge the free access to the GlobeLand30 land-cover products provided by the National Geomatics Center of China, the FROM-GLC land-cover products provided by Tsinghua University, the  ... 
doi:10.5194/essd-12-1625-2020 fatcat:45htn3rkx5gmfb7ys2e5iwpja4

Multisensor earth observations to characterize wetlands and malaria epidemiology in Ethiopia

Alemayehu Midekisa, Gabriel B. Senay, Michael C. Wimberly
2014 Water Resources Research  
We evaluated three random forests classification models using remotely sensed topographic and spectral data based on Shuttle Radar Topographic Mission (SRTM) and Landsat TM/ETM1 imagery, respectively.  ...  The model that integrated data from both sensors yielded more accurate land cover classification than single-sensor models.  ...  The MODIS data were obtained from the NASA Land Processes Distributed Active Archive Center (LP DAAC), located at the USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, SD, USA (http  ... 
doi:10.1002/2014wr015634 pmid:25653462 pmcid:PMC4303930 fatcat:alomhtq7lvhadha2npfsh3xl24

Land Cover Mapping Based on Multisource Spatial Data Mining Approach for Climate Simulation: A Case Study in the Farming-Pastoral Ecotone of North China

Feng Wu, Jinyan Zhan, Haiming Yan, Chenchen Shi, Juan Huang
2013 Advances in Meteorology  
CAS classification to land cover data of IGBP classification.  ...  By comparing the results obtained with different decision tree classifiers with the WEKA toolkit for data mining, it was found that the C4.5 algorithm was more suitable for converting land use data of  ...  Data supports from projects of the National Natural Science Foundation of China (no. 71225005) and the Exploratory Forefront Project for the Strategic Science Plan in IGSNRR, CAS are also appreciated.  ... 
doi:10.1155/2013/520803 fatcat:7pp564eu2bh5doxxyqozp46ffm

Long Time Series High-Quality and High-Consistency Land Cover Mapping Based on Machine Learning Method at Heihe River Basin

Bo Zhong, Aixia Yang, Kunsheng Jue, Junjun Wu
2021 Remote Sensing  
Thirdly, the random forest model is employed to train the selected yearly samples and a land cover map for every year is subsequently made.  ...  Firstly, the high-quality land cover datasets at HRB from 2011–2015, which were retrieved using the LCMM method, are used for quickly and accurately making training samples.  ...  Sample Transferring Strategy for Earlier Years The trained random forest model looks perfect to be used for land cover mapping directly in earlier years (before 2011); however, the seasonal surface reflectance  ... 
doi:10.3390/rs13081596 fatcat:s7uqbcc5sjdyxmkiber2fetdg4

Classification of multisource and hyperspectral data based on decision fusion

J.A. Benediktsson, I. Kanellopoulos
1999 IEEE Transactions on Geoscience and Remote Sensing  
For the second stage, a neural network is used to classify the rejected samples.  ...  The proposed methods are applied in the classification of multisource and hyperdimensional data sets, and the results compared to accuracies obtained with conventional classification schemes.  ...  They first classified the imagery into nine classes of land cover including one of forest and then eight different forest classes were extracted from the generic forest land cover area.  ... 
doi:10.1109/36.763301 fatcat:vpjgl55xybd4diwzvptp4tfage

Embedded Zero Tree Wavelet based Artificial Neural Network Image Classification Algorithm - A Study

T. Karthikeyan, P. Manikandaprabhu
2015 Indian Journal of Science and Technology  
These features data are used for the classification process.  ...  Here, we used various classification algorithms namely, Radial Basis Function, SMO, Multilayer Perceptron and Random Forest are implemented.  ...  land cover mapping and they have been adapted widely for land cover monitoring and categorization.  ... 
doi:10.17485/ijst/2015/v8i20/54554 fatcat:hukuswnbpjdffmmbl2gryv3zvm

Population Spatialization in Beijing City Based on Machine Learning and Multisource Remote Sensing Data

Miao He, Yongming Xu, Ning Li
2020 Remote Sensing  
Random forest achieved the highest accuracy and therefore was employed for population spatialization.  ...  Feature selection was performed to determine the optimal variable combinations for population modeling by random forest.  ...  Acknowledgments: The authors would like to thank the National Bureau of Statistics of China for providing census data, NOAA Earth Observation Group (EOG) for providing NPP/VIIRS night-time data, US Geological  ... 
doi:10.3390/rs12121910 fatcat:mbh4bjxtdrbaljaqyphweqhvm4

GENERALIZED KNOWLEDGE DISTILLATION FOR MULTI-SENSOR REMOTE SENSING CLASSIFICATION: AN APPLICATION TO LAND COVER MAPPING

D. Ienco, Y. J. E. Gbodjo, R. Gaetano, R. Interdonato
2020 ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
With the aim to provide a proof of concept of GKD in the context of multi-source Earth Observation analysis, here we provide a Generalized Knowledge Distillation framework for land use land cover mapping  ...  Evaluations are carried out on a real-world study area in the southwest of France, namely Dordogne, considering a mapping task involving seven different land use land cover classes.  ...  However, common machine learning techniques used to per- * Corresponding author form Land Use Land Cover (LULC) mapping (e.g., Random Forest, Support Vector Machine or Convolutional Neural Networks) make  ... 
doi:10.5194/isprs-annals-v-2-2020-997-2020 fatcat:nlvet7w6ajbzlby3qto5tra5de

Global land cover mapping using Earth observation satellite data: Recent progresses and challenges

Yifang Ban, Peng Gong, Chandra Giri
2015 ISPRS journal of photogrammetry and remote sensing (Print)  
We are pleased to offer this theme issue to the scientific community, and hope that it has accomplished our goal of highlighting recent progresses and challenges in global land cover mapping.  ...  Derek Lichti, for his support to this theme issue.  ...  Global land-cover observation capacity from earth observation satellites and data quality: Two papers are focused on this topic.  ... 
doi:10.1016/j.isprsjprs.2015.01.001 fatcat:xi4zwowmlvgehnm7drdb74b45i

Fusion Classification of HSI and MSI Using a Spatial-Spectral Vision Transformer for Wetland Biodiversity Estimation

Yunhao Gao, Xiukai Song, Wei Li, Jianbu Wang, Jianlong He, Xiangyang Jiang, Yinyin Feng
2022 Remote Sensing  
The rapid development of remote sensing technology provides wealthy data for earth observation. Land-cover mapping indirectly achieves biodiversity estimation at a coarse scale.  ...  This paper constructs a systematic framework for multisource remote sensing image processing.  ...  In [15] , the land-cover in coastal wetland were classified using an object-oriented random forest algorithm.  ... 
doi:10.3390/rs14040850 fatcat:i7ua5e62fvfx5cxltbjwzwl44i

2020 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 13

2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
., +, JSTARS 2020 4399-4409 A Multiscale Random Forest Kernel for Land Cover Classification.  ...  Zhu, C., +, JSTARS 2020 1206-1217 Pattern classification A Multiscale Random Forest Kernel for Land Cover Classification.  ...  A New Deep-Learning-Based Approach for Earthquake-Triggered Landslide Detection From Single-Temporal RapidEye Satellite Imagery. Yi, Y., +, JSTARS 2020  ... 
doi:10.1109/jstars.2021.3050695 fatcat:ycd5qt66xrgqfewcr6ygsqcl2y
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