scholarly journals Land cover classification in an era of big and open data: Optimizing localized implementation and training data selection to improve mapping outcomes

2022 ◽  
Vol 268 ◽  
pp. 112780
Author(s):  
Txomin Hermosilla ◽  
Michael A. Wulder ◽  
Joanne C. White ◽  
Nicholas C. Coops
Keyword(s):  
Land Cover ◽  
Open Data ◽  
Land Cover Classification ◽  
Training Data ◽  
Data Selection ◽  
Training Data Selection ◽  
And Training

A Method on Land Cover Classification by Combining Unsupervised Algorithm and Training Data

1999 ◽  
Vol 14 (4) ◽  
pp. 15-20 ◽  
Author(s):  
Chen Xiuwan ◽  
Hu Heping ◽  
Ryutaro Tateishi ◽  
Chung-Hyun Ahn
Keyword(s):  
Land Cover ◽  
Land Cover Classification ◽  
Training Data ◽  
And Training

Local and Regional Hour-Ahead Forecasts of Solar Irradiance with Training Data Selection and Support Vector Regression

2016 ◽  
Vol 136 (12) ◽  
pp. 898-907 ◽  
Author(s):  
Joao Gari da Silva Fonseca Junior ◽  
Hideaki Ohtake ◽  
Takashi Oozeki ◽  
Kazuhiko Ogimoto
Keyword(s):  
Support Vector Regression ◽  
Solar Irradiance ◽  
Training Data ◽  
Data Selection ◽  
Support Vector ◽  
Training Data Selection

Data-driven method for training data selection for deep learning

2021 ◽  
Author(s):  
C. Lacombe ◽  
I. Hammoud ◽  
J. Messud ◽  
H. Peng ◽  
T. Lesieur ◽  
...  
Keyword(s):  
Deep Learning ◽  
Training Data ◽  
Data Selection ◽  
Data Driven ◽  
Selection For ◽  
Training Data Selection

scholarly journals Land Cover Classification from fused DSM and UAV Images Using Convolutional Neural Networks

2019 ◽  
Vol 11 (12) ◽  
pp. 1461 ◽  
Author(s):  
Husam A. H. Al-Najjar ◽  
Bahareh Kalantar ◽  
Biswajeet Pradhan ◽  
Vahideh Saeidi ◽  
Alfian Abdul Halin ◽  
...  
Keyword(s):  
Neural Networks ◽  
Land Cover ◽  
Convolutional Neural Networks ◽  
Land Cover Classification ◽  
Training Data ◽  
Surface Model ◽  
Kappa Index ◽  
Diverse Range ◽  
Average Accuracy ◽  
Uav Images

In recent years, remote sensing researchers have investigated the use of different modalities (or combinations of modalities) for classification tasks. Such modalities can be extracted via a diverse range of sensors and images. Currently, there are no (or only a few) studies that have been done to increase the land cover classification accuracy via unmanned aerial vehicle (UAV)–digital surface model (DSM) fused datasets. Therefore, this study looks at improving the accuracy of these datasets by exploiting convolutional neural networks (CNNs). In this work, we focus on the fusion of DSM and UAV images for land use/land cover mapping via classification into seven classes: bare land, buildings, dense vegetation/trees, grassland, paved roads, shadows, and water bodies. Specifically, we investigated the effectiveness of the two datasets with the aim of inspecting whether the fused DSM yields remarkable outcomes for land cover classification. The datasets were: (i) only orthomosaic image data (Red, Green and Blue channel data), and (ii) a fusion of the orthomosaic image and DSM data, where the final classification was performed using a CNN. CNN, as a classification method, is promising due to hierarchical learning structure, regulating and weight sharing with respect to training data, generalization, optimization and parameters reduction, automatic feature extraction and robust discrimination ability with high performance. The experimental results show that a CNN trained on the fused dataset obtains better results with Kappa index of ~0.98, an average accuracy of 0.97 and final overall accuracy of 0.98. Comparing accuracies between the CNN with DSM result and the CNN without DSM result for the overall accuracy, average accuracy and Kappa index revealed an improvement of 1.2%, 1.8% and 1.5%, respectively. Accordingly, adding the heights of features such as buildings and trees improved the differentiation between vegetation specifically where plants were dense.

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