Examining region-based methods for land cover classification using stochastic distances-Reference-Cited by-同舟云学术

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Examining region-based methods for land cover classification using stochastic distances

Published:2016-04-11 Issue:8 Volume:37 Page:1902-1921
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Negri R. G.,Dutra L. V.,Sant'Anna S. J. S.,Lu D.

Funder

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

FAPESP

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

http://www.tandfonline.com/doi/pdf/10.1080/01431161.2016.1165883

Reference24 articles.

1. A Novel Context-Sensitive Semisupervised SVM Classifier Robust to Mislabeled Training Samples

2. Spring: Integrating remote sensing and gis by object-oriented data modelling

3. Semi-Supervised Graph-Based Hyperspectral Image Classification

4. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods

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1. Spectral–Spatial-Aware Unsupervised Change Detection With Stochastic Distances and Support Vector Machines;IEEE Transactions on Geoscience and Remote Sensing;2021-04

2. Comparing the Performance of Mathematical Morphology and Bhattacharyya Distance for Airport Extraction;IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium;2020-09-26

3. A Meta-Methodology for Improving Land Cover and Land Use Classification with SAR Imagery;Remote Sensing;2020-03-16

4. Avoiding Invalid Transitions in Land Cover Trajectory Classification With a Compound Maximum a Posteriori Approach;IEEE Access;2020

5. Sensoriamento remoto, processamento de imagens e estudos ambientais;Fronteiras da engenharia e ciências ambientais: perspectivas multidisciplinares;2020

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