Journal ArticleDOI
Object-oriented mapping of landslides using Random Forests
TLDR
A supervised workflow is proposed in this study to reduce manual labor and objectify the choice of significant object features and classification thresholds and resulted in accuracies between 73% and 87% for the affected areas, and approximately balanced commission and omission errors.About:
This article is published in Remote Sensing of Environment.The article was published on 2011-10-17. It has received 569 citations till now. The article focuses on the topics: Image segmentation & Random forest.read more
Citations
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Journal ArticleDOI
Random forest in remote sensing: A review of applications and future directions
Mariana Belgiu,Lucian Drăguţ +1 more
TL;DR: This review has revealed that RF classifier can successfully handle high data dimensionality and multicolinearity, being both fast and insensitive to overfitting.
Journal ArticleDOI
Remote Sensing Image Scene Classification: Benchmark and State of the Art
TL;DR: A large-scale data set, termed “NWPU-RESISC45,” is proposed, which is a publicly available benchmark for REmote Sensing Image Scene Classification (RESISC), created by Northwestern Polytechnical University (NWPU).
Journal ArticleDOI
Landslide inventory maps: New tools for an old problem
Fausto Guzzetti,Alessandro Cesare Mondini,Mauro Cardinali,Federica Fiorucci,Michele Santangelo,Kang-Tsung Chang +5 more
TL;DR: In this article, the authors outline the principles for landslide mapping, and review the conventional methods for the preparation of landslide maps, including geomorphological, event, seasonal, and multi-temporal inventories.
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A survey on object detection in optical remote sensing images
Gong Cheng,Junwei Han +1 more
TL;DR: This survey focuses on more generic object categories including, but not limited to, road, building, tree, vehicle, ship, airport, urban-area, and proposes two promising research directions, namely deep learning- based feature representation and weakly supervised learning-based geospatial object detection.
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A review of statistically-based landslide susceptibility models
TL;DR: In this paper, a critical review of statistical methods for landslide susceptibility modelling and associated terrain zonations is presented, revealing a significant heterogeneity of thematic data types and scales, modelling approaches, and model evaluation criteria.
References
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Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
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Textural Features for Image Classification
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
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Bagging predictors
TL;DR: Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy.
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Learning from Imbalanced Data
Haibo He,E.A. Garcia +1 more
TL;DR: A critical review of the nature of the problem, the state-of-the-art technologies, and the current assessment metrics used to evaluate learning performance under the imbalanced learning scenario is provided.
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A review of feature selection techniques in bioinformatics
TL;DR: A basic taxonomy of feature selection techniques is provided, providing their use, variety and potential in a number of both common as well as upcoming bioinformatics applications.