A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research
Reads0
Chats0
TLDR
A statistical meta-analysis of the past 15 years of research on supervised per-pixel image classification revealed that inclusion of texture information yielded the greatest improvement in overall accuracy of land-cover classification with an average increase of 12.1%.About:
This article is published in Remote Sensing of Environment.The article was published on 2016-05-01 and is currently open access. It has received 410 citations till now. The article focuses on the topics: Contextual image classification & Statistical classification.read more
Citations
More filters
Journal ArticleDOI
Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data
TL;DR: A multilevel DL architecture that targets land cover and crop type classification from multitemporal multisource satellite imagery outperforms the one with MLPs allowing us to better discriminate certain summer crop types.
Journal ArticleDOI
Optical remotely sensed time series data for land cover classification: A review
TL;DR: In this article, the authors present the issues and opportunities associated with generating and validating time-series informed annual, large-area, land cover products, and identify methods suited to incorporating time series information and other novel inputs for land cover characterization.
Journal ArticleDOI
Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery
Phan Thanh Noi,Martin Kappas +1 more
TL;DR: This study examined and compared the performances of the RF, kNN, and SVM classifiers for land use/cover classification using Sentinel-2 image data and found that SVM produced the highest OA with the least sensitivity to the training sample sizes.
Journal ArticleDOI
A review of supervised object-based land-cover image classification
TL;DR: It is found that supervised object- based classification is currently experiencing rapid advances, while development of the fuzzy technique is limited in the object-based framework, and spatial resolution correlates with the optimal segmentation scale and study area, and Random Forest shows the best performance inobject-based classification.
Journal ArticleDOI
Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas
TL;DR: This work aims at demonstrating the ability of state-of-the-art classifiers, such as Random Forests (RF) or Support Vector Machines (SVM), to classify HR-SITS, and selecting the best feature set used as input data in order to establish the classifier accuracy over large areas.
References
More filters
Journal ArticleDOI
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.
Journal ArticleDOI
Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement
TL;DR: Moher et al. as mentioned in this paper introduce PRISMA, an update of the QUOROM guidelines for reporting systematic reviews and meta-analyses, which is used in this paper.
Journal Article
Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement.
TL;DR: The QUOROM Statement (QUality Of Reporting Of Meta-analyses) as mentioned in this paper was developed to address the suboptimal reporting of systematic reviews and meta-analysis of randomized controlled trials.
Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Journal ArticleDOI
Learning representations by back-propagating errors
TL;DR: Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.