Land Cover Classification Accuracy Assessment

2021 ◽  
pp. 105-118
Author(s):  
Courage Kamusoko
Keyword(s):  
Land Cover ◽  
Classification Accuracy ◽  
Accuracy Assessment ◽  
Land Cover Classification

Method for land cover classification accuracy assessment considering edges

2016 ◽  
Vol 59 (12) ◽  
pp. 2318-2327 ◽  
Author(s):  
Meng Liu ◽  
Xin Cao ◽  
Yang Li ◽  
Jin Chen ◽  
XueHong Chen
Keyword(s):  
Land Cover ◽  
Classification Accuracy ◽  
Accuracy Assessment ◽  
Land Cover Classification

scholarly journals Land Cover Classification Accuracy Assessment Using Full-Waveform LiDAR Data

2015 ◽  
Vol 26 (2-2) ◽  
pp. 169 ◽  
Author(s):  
Kuan-Tsung Chang ◽  
Feng-Chi Yu ◽  
Yi Chang ◽  
Jin-Tsong Hwang ◽  
Jin-King Liu ◽  
...  
Keyword(s):  
Land Cover ◽  
Classification Accuracy ◽  
Accuracy Assessment ◽  
Land Cover Classification ◽  
Lidar Data ◽  
Full Waveform ◽  
Waveform Lidar ◽  
Full Waveform Lidar

Stratified even sampling method for accuracy assessment of land use/land cover classification: a case study of Beijing, China

2020 ◽  
Vol 41 (16) ◽  
pp. 6427-6443
Author(s):  
Shiwei Dong ◽  
Ziyue Chen ◽  
Bingbo Gao ◽  
Hui Guo ◽  
Danfeng Sun ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Sampling Method ◽  
Accuracy Assessment ◽  
Land Cover Classification ◽  
Land Use Land Cover ◽  
Beijing China

scholarly journals Isolation Forests to Evaluate Class Separability and the Representativeness of Training and Validation Areas in Land Cover Classification

2019 ◽  
Vol 11 (24) ◽  
pp. 3000 ◽  
Author(s):  
Francisco Alonso-Sarria ◽  
Carmen Valdivieso-Ros ◽  
Francisco Gomariz-Castillo
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Land Cover ◽  
Classification Accuracy ◽  
Land Cover Classification ◽  
Landsat 8 ◽  
Commission Errors ◽  
Class Separability ◽  
Isolation Forest

Supervised land cover classification from remote sensing imagery is based on gathering a set of training areas to characterise each of the classes and to train a predictive model that is then used to predict land cover in the rest of the image. This procedure relies mainly on the assumptions of statistical separability of the classes and the representativeness of the training areas. This paper uses isolation forests, a type of random tree ensembles, to analyse both assumptions and to easily correct lack of representativeness by digitising new training areas where needed to improve the classification of a Landsat-8 set of images with Random Forest. The results show that the improved set of training areas after the isolation forest analysis is more representative of the whole image and increases classification accuracy. Besides, the distribution of isolation values can be useful to estimate class separability. A class separability parameter that summarises such distributions is proposed. This parameter is more correlated to omission and commission errors than other separability measures such as the Jeffries–Matusita distance.

scholarly journals Integrating Multitemporal Sentinel-1/2 Data for Coastal Land Cover Classification Using a Multibranch Convolutional Neural Network: A Case of the Yellow River Delta

2019 ◽  
Vol 11 (9) ◽  
pp. 1006 ◽  
Author(s):  
Quanlong Feng ◽  
Jianyu Yang ◽  
Dehai Zhu ◽  
Jiantao Liu ◽  
Hao Guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Deep Learning ◽  
Land Cover ◽  
Convolutional Neural Network ◽  
Classification Accuracy ◽  
Yellow River ◽  
Rapid Development ◽  
Land Cover Classification ◽  
Coastal Land

Coastal land cover classification is a significant yet challenging task in remote sensing because of the complex and fragmented nature of coastal landscapes. However, availability of multitemporal and multisensor remote sensing data provides opportunities to improve classification accuracy. Meanwhile, rapid development of deep learning has achieved astonishing results in computer vision tasks and has also been a popular topic in the field of remote sensing. Nevertheless, designing an effective and concise deep learning model for coastal land cover classification remains problematic. To tackle this issue, we propose a multibranch convolutional neural network (MBCNN) for the fusion of multitemporal and multisensor Sentinel data to improve coastal land cover classification accuracy. The proposed model leverages a series of deformable convolutional neural networks to extract representative features from a single-source dataset. Extracted features are aggregated through an adaptive feature fusion module to predict final land cover categories. Experimental results indicate that the proposed MBCNN shows good performance, with an overall accuracy of 93.78% and a Kappa coefficient of 0.9297. Inclusion of multitemporal data improves accuracy by an average of 6.85%, while multisensor data contributes to 3.24% of accuracy increase. Additionally, the featured fusion module in this study also increases accuracy by about 2% when compared with the feature-stacking method. Results demonstrate that the proposed method can effectively mine and fuse multitemporal and multisource Sentinel data, which improves coastal land cover classification accuracy.

Bayesian Markov Chain Random Field Cosimulation for Improving Land Cover Classification Accuracy

2014 ◽  
Vol 47 (2) ◽  
pp. 123-148 ◽  
Author(s):  
Weidong Li ◽  
Chuanrong Zhang ◽  
Michael R. Willig ◽  
Dipak K. Dey ◽  
Guiling Wang ◽  
...  
Keyword(s):  
Markov Chain ◽  
Land Cover ◽  
Random Field ◽  
Classification Accuracy ◽  
Land Cover Classification
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