Land Cover Mapping for Green House Gas Inventories in Eastern and Southern Africa Using Landsat and High Resolution Imagery: Approach and Lessons Learnt

2016 ◽  
pp. 85-116
Author(s):  
Phoebe Oduor ◽  
Jaffer Ababu ◽  
Robinson Mugo ◽  
Hussein Farah ◽  
Africa Flores ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Southern Africa ◽  
Land Cover Mapping ◽  
Green House ◽  
Green House Gas ◽  
Lessons Learnt ◽  
High Resolution Imagery ◽  
Eastern And Southern Africa

scholarly journals Comparative Analysis of Land Cover Classification Using ML and SVM Classifier for LISS-iv Data

2019 ◽  
Author(s):  
Rahul Neware
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Land Cover Classification ◽  
Optical Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Land Cover Mapping ◽  
High Resolution Imagery ◽  
Step Procedure ◽  
Object Based

This paper focuses on the crucial role that remote sensing plays in divining land features. Data that is collected distantly provides information in spectral, spatial, temporal and radiometric domains, with each domain having the specific resolution to information collected. Diverse sectors such as hydrology, geology, agriculture, land cover mapping, forestry, urban development and planning, oceanography and others are known to use and rely on information that is gathered remotely from different sensors. In the present study, IRS LISS IV Multi-spectral data is used for land cover mapping. It is known, however, that the task of classifying high-resolution imagery of land cover through manual digitizing consumes time and is way too costly. Therefore, this paper proposes accomplishing classifications by way of enforcing algorithms in computers. These classifications fall in three classes: supervised, unsupervised, and object-based classification. In the case of supervised classification, two approaches are relied upon for land cover classification of high-resolution LISS-IV multispectral image. These approaches are Maximum Likelihood and Support Vector Machine (SVM). Finally, the paper proposes a step-by-step procedure for optical image classification methodology. This paper concludes that in optical data classification, SVM classification gives a better result than the ML classification technique.

High-Resolution Land Cover Mapping Through Learning With Noise Correction

2021 ◽  
pp. 1-13
Author(s):  
Runmin Dong ◽  
Weizhen Fang ◽  
Haohuan Fu ◽  
Lin Gan ◽  
Jie Wang ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Land Cover Mapping ◽  
Learning With Noise ◽  
Noise Correction

Training Deep Convolutional Neural Networks for Land–Cover Classification of High-Resolution Imagery

2017 ◽  
Vol 14 (4) ◽  
pp. 549-553 ◽  
Author(s):  
Grant J. Scott ◽  
Matthew R. England ◽  
William A. Starms ◽  
Richard A. Marcum ◽  
Curt H. Davis
Keyword(s):  
Neural Networks ◽  
High Resolution ◽  
Land Cover ◽  
Convolutional Neural Networks ◽  
Land Cover Classification ◽  
Deep Convolutional Neural Networks ◽  
High Resolution Imagery

Recurrent Shadow Attention Model (RSAM) for shadow removal in high-resolution urban land-cover mapping

2020 ◽  
Vol 247 ◽  
pp. 111945 ◽  
Author(s):  
Yindan Zhang ◽  
Gang Chen ◽  
Jelena Vukomanovic ◽  
Kunwar K. Singh ◽  
Yong Liu ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Urban Land ◽  
Land Cover Mapping ◽  
Shadow Removal ◽  
Attention Model ◽  
Urban Land Cover

scholarly journals Mapping of land cover with open‐source software and ultra‐high‐resolution imagery acquired with unmanned aerial vehicles

2020 ◽  
Vol 6 (4) ◽  
pp. 487-497 ◽  
Author(s):  
Ned Horning ◽  
Erica Fleishman ◽  
Peter J. Ersts ◽  
Frank A. Fogarty ◽  
Martha Wohlfeil Zillig
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Unmanned Aerial Vehicles ◽  
Open Source ◽  
Open Source Software ◽  
Aerial Vehicles ◽  
High Resolution Imagery
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