scholarly journals Investigation of the effects of vegetation indices derived from UAV-based RGB imagery on land cover classification accuracy using advanced ensemble learning methods

2021 ◽  
Author(s):  
Muhammed ÖZTÜRK ◽  
İsmail ÇÖLKESEN
Keyword(s):  
Land Cover ◽  
Ensemble Learning ◽  
Classification Accuracy ◽  
Vegetation Indices ◽  
Land Cover Classification ◽  
Learning Methods

Deep and Ensemble Learning Based Land Use and Land Cover Classification

2021 ◽  
pp. 588-604
Author(s):  
Hicham Benbriqa ◽  
Ibtissam Abnane ◽  
Ali Idri ◽  
Khouloud Tabiti
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Ensemble Learning ◽  
Land Cover Classification

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

scholarly journals Assessment of Segmentation Parameters for Object-Based Land Cover Classification Using Color-Infrared Imagery

2018 ◽  
Vol 7 (11) ◽  
pp. 424 ◽  
Author(s):  
Ozgun Akcay ◽  
Emin Avsar ◽  
Melis Inalpulat ◽  
Levent Genc ◽  
Ahmet Cam
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Land Cover ◽  
Classification Accuracy ◽  
Land Cover Classification ◽  
Aerial Images ◽  
High Resolution Data ◽  
Area Of Interest ◽  
Object Based ◽  
Segmentation Parameters

Using object-based image analysis (OBIA) techniques for land use-land cover classification (LULC) has become an area of interest due to the availability of high-resolution data and segmentation methods. Multi-resolution segmentation in particular, statistically seen as the most used algorithm, is able to produce non-identical segmentations depending on the required parameters. The total effect of segmentation parameters on the classification accuracy of high-resolution imagery is still an open question, though some studies were implemented to define the optimum segmentation parameters. However, recent studies have not properly considered the parameters and their consequences on LULC accuracy. The main objective of this study is to assess OBIA segmentation and classification accuracy according to the segmentation parameters using different overlap ratios during image object sampling for a predetermined scale. With this aim, we analyzed and compared (a) high-resolution color-infrared aerial images of a newly-developed urban area including different land use types; (b) combinations of multi-resolution segmentation with different shape, color, compactness, bands, and band-weights; and (c) accuracies of classifications based on varied segmentations. The results of various parameters in the study showed an explicit correlation between segmentation accuracies and classification accuracies. The effect of changes in segmentation parameters using different sample selection methods for five main LULC types was studied. Specifically, moderate shape and compactness values provided more consistency than lower and higher values; also, band weighting demonstrated substantial results due to the chosen bands. Differences in the variable importance of the classifications and changes in LULC maps were also explained.

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