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Supervised Classification High-Resolution Remote-Sensing Image Based on Interval Type-2 Fuzzy Membership Function
2018
Remote Sensing
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Because of the degradation of classification accuracy that is caused by the uncertainty of pixel class and classification decisions of high-resolution remote-sensing images, we proposed a supervised classification ...
We analyze the data features of a high-resolution remote-sensing image and construct a type-1 membership function model in a homogenous region by supervised sampling in order to characterize the uncertainty ...
Benefiting from the rich and the detailed information of ground objects, high-resolution remote-sensing images have good application prospects and advantages in large-scale and accurate object classification ...
doi:10.3390/rs10050710
fatcat:cjbriucadndpxoctyaw5dodhv4
An Overview and Recent Advances in Fuzzy ARTMAP Classifier Usage for Mapping Purposes Using Remotely Sensed Data
2020
Journal of Environmental Informatics Letters
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This paper presents an overview and recent advances on the usage of Fuzzy ARTMAP artificial neural network architecture (and its variants) for mapping purposes using remotely sensed data. ...
Possible gaps in the literature related to Fuzzy ARTMAP classifier usage for mapping are suggested, leading to paths for future developments in this field of research. ...
This study was financed in part by the Coordenaç ã o de Aperfeiç oamento de Pessoal de Ní vel Superior-Brasil (CA-PES)-Finance Code 001. ...
doi:10.3808/jeil.202000032
fatcat:2havklimhng5nd7ywda6sr452y
Urban mapping, accuracy, & image classification: A comparison of multiple approaches in Tsukuba City, Japan
2009
Applied Geography
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The fuzzy supervised approach yielded a better accuracy (87.67%) than the supervised and unsupervised approaches. ...
Intensive fieldwork was conducted to collect ground truth data. A random stratified sampling method was chosen to generate geographic reference data for each map to assess the accuracy. ...
Partial financial support for this research from the Health Project, Grant-in-Aid, Ministry of Health, Labour and Welfare (Grant number H17-Health-004, Chief: Teruichi Shimomitsu, Professor of Tokyo Medical ...
doi:10.1016/j.apgeog.2008.08.001
fatcat:f73hhuj4cncdjbg3jul7izyfza
Data Fusion and Accuracy Analysis of Multi-Source Land Use/Land Cover Datasets along Coastal Areas of the Maritime Silk Road
2019
ISPRS International Journal of Geo-Information
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High-precision land use/land cover classification mapping derived from remote sensing supplies essential datasets for scientific research on environmental assessment, climate change simulation, geographic ...
in terms of the correctly classified contributions and misclassified influences of different land use/land cover types in the fusion data; furthermore, the overall accuracy and Kappa coefficient between ...
Acknowledgments: We sincerely thank the authors of the datasets that we used in this study.
Conflicts of Interest: The authors declare no conflicts of interest. ...
doi:10.3390/ijgi8120557
fatcat:jd5jdgecarc7fijhirysez6b74
Discrimination of Seasonal Snow Cover in Astore Basin, Western Himalaya using Fuzzy Membership Function of Object-Based Classification
2018
International Journal of Economic and Environment Geology
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Although OBIA is a strong technique that has been successfully applied in numerous research problems of remote sensing regarding cryosphere, but due to hindrances (i.e. ...
The range of 0.4-1.0 was used as a threshold value for fuzzy membership function in OBIA to delineate the snow cover more precisely. ...
Remote sensing provides the benefits of acquiring snow data like snow water equivalent and snow covered area to predict the snow-melt runoff in real time, which is an essential factor for a dynamic physical ...
doaj:e871793ef6f1437fb4fd833c0cb5a822
fatcat:6ks5xkrwtffybl5hgot7zo2e7e
Land Use/Cover Classification Techniques Using Optical Remotely Sensed Data in Landscape Planning
[chapter]
2012
Landscape Planning
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Accurate geometric rectification or image registration of remotely sensed data is a prerequisite for a combination of different source data in a classification process. ...
Criteria
Soft (fuzzy) classifiers Defining "what is in a pixel?" numerically, very important for understanding the earth surface in remote sensing science. ...
Land Use/Cover Classification Techniques Using Optical Remotely Sensed Data in Landscape Planning, Landscape Planning, Dr. ...
doi:10.5772/31351
fatcat:5dd5zcp7cbathmoxicu265fvuu
Cloud detection methodologies: variants and development—a review
2019
Complex & Intelligent Systems
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The concept of classification to build a decision tree for cloud detection on the snow cover area is used in [7] . ...
Authors of [9] experimented through a convolutional neural network (CNN) and deep forest. They have used a segmented super pixel level of remote-sensing image database. ...
Authors of [27] proposed a cloud detection method for multi-spectral remote-sensing images from Landsat 8. ...
doi:10.1007/s40747-019-00128-0
fatcat:ftol5w36vzdwzpuqeijsz2dct4
Adaptive Neuro-fuzzy Inference System based Earth Surface Features Classification System
2020
Communications on Applied Electronics
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An accuracy level of 98.66 -99.88 % with a RMSE of 0.0218 -0.0506 were obtained. ...
Conventional methods of classifying earth features (Normalized Difference Vegetation Index, NDVI, and Normalized Difference Water Index, NDWI) were first used to generate the data for the training of the ...
The use of digital image processing for soil survey and mapping was initiated with the establishment of National Remote Sensing Agency and Regional Remote Sensing Service Centers. ...
doi:10.5120/cae2020652856
fatcat:xiv5ocpllvbttjxztkxxxrcu4i
Comparing stability in random forest models to map Northern Great Plains plant communities in pastures occupied by prairie dogs using Pleiades imagery
2020
Biogeosciences
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The results show that while RF models may predict with a high degree of accuracy, overlap of plant communities and interannual differences in rainfall may cause instability in fitted models. ...
Dakota, assess the stability of RF models among different years, and determine the utility of utilizing remote sensing techniques to identify prairie dog colony extent. ...
Department of Agriculture for funding this research as well as North Dakota State University. ...
doi:10.5194/bg-17-1281-2020
fatcat:n7qk2x337ndxvjtv6vhrvbbomu
Comparing Stability in Random Forest Models to Map Northern Great Plains Plant Communities Using 2015 and 2016 Pleiades Imagery
2019
Biogeosciences Discussions
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Random forest (RF) is a machine learning technique that has gained considerable traction in remote sensing applications due to its ability to produce accurate classifications with highly dimensional data ...
The results show that while RF models may predict with a high degree of accuracy, overlap of plant communities and inter-annual differences in rainfall may cause instability in fitted models. ...
Department of Agriculture (Grant ...
doi:10.5194/bg-2019-194
fatcat:qm67pqnhzbhvvgrpuycicizbui
Remote Sensing Satellite Image Processing Techniques for Image Classification: A Comprehensive Survey
2017
International Journal of Computer Applications
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This paper is a brief survey of advance technological aspects of Digital Image Processing which are applied to remote sensing images obtained from various satellite sensors. ...
In remote sensing, the image processing techniques can be categories in to four main processing stages: Image preprocessing, Enhancement, Transformation and Classification. ...
Authors found that, method yield high classification accuracy in classifying urban areas of 1m resolution data. ...
doi:10.5120/ijca2017913306
fatcat:2bzxqgiy2nfkldftt5wgj4v4e4
Remote Sensing of Ecology, Biodiversity and Conservation: A Review from the Perspective of Remote Sensing Specialists
2010
Sensors
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This review focuses on the spaceborne remote sensing of EBC from the perspective of remote sensing specialists, i.e., it is organized in the context of state-of-the-art remote sensing technology, including ...
, vegetation index (VI), inversion algorithm, data fusion, and the integration of remote sensing (RS) and geographic information system (GIS). ...
The first author also would like to acknowledge the scholarship supports of Department of Geography and Planning, and College of Graduate Studies and Research at the University of Saskatchewan. ...
doi:10.3390/s101109647
pmid:22163432
pmcid:PMC3231003
fatcat:fioutsgar5aqnn76elireshjpm
A Review on Extraction of Lakes from Remotely Sensed Optical Satellite Data with a Special Focus on Cryospheric Lakes
2015
Advances in Remote Sensing
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A Synergetic fusion of various remote sensing methods is also proposed to improve water information extraction accuracies. ...
To our knowledge, almost all of the published research studies on the extraction of surface lakes in cryospheric environments have essentially used satellite remote sensing data and geospatial methods. ...
Rajan, Director, NCAOR for his encouragement and motivation of this research. We also thank Ms. Prachi Vaidya, India for her constructive comments on the draft version of the manuscript. ...
doi:10.4236/ars.2015.43016
fatcat:vc6tamwdbre63g7x22ftwurqoq
A multiprocess model of adaptable complexity for impervious surface detection
2011
International Journal of Remote Sensing
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This research was partially supported by the National Science Foundation (award GRS-0648393), the National Aeronautics and Space Administration (awards NNX08AR11G, NNX09AK16G) and the Syracuse Center of ...
Remotely sensed data (from both satellite and aerial platforms) and methods have become increasingly important in impervious surface detection. ...
The hybrid multiprocess model The potential benefits of building an integration framework for collaborative operation of multiple algorithms in remote sensing applications have been described previously ...
doi:10.1080/01431161.2010.532177
fatcat:jvpved5vibbxrhect2pugb4j5a
Improving the Accuracy of Vegetation Classifications in Mountainous Areas
2013
Mountain Research and Development
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A review of assessing the accuracy of classifications of
of ATCOR3 topographic correction method for forest cover mapping in mountain remotely sensed data. ...
A comparison of methods for monitoring
for classification of remotely sensed data. ...
doi:10.1659/mrd-journal-d-12-00011.1
fatcat:tjgiv7aj3vhvnkzrid7sojnztq
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