scholarly journals Sentinel-2 Satellite Imagery for Urban Land Cover Classification by Optimized Random Forest Classifier

2021 ◽  
Vol 11 (2) ◽  
pp. 543
Author(s):  
Tianxiang Zhang ◽  
Jinya Su ◽  
Zhiyong Xu ◽  
Yulin Luo ◽  
Jiangyun Li
Keyword(s):  
Random Forest ◽  
Land Cover ◽  
Satellite Imagery ◽  
Parameter Optimization ◽  
Satellite Image ◽  
Land Cover Classification ◽  
Random Forest Classifier ◽  
Support Vector ◽  
Wide Range ◽  
Sentinel 2

Land cover classification is able to reflect the potential natural and social process in urban development, providing vital information to stakeholders. Recent solutions on land cover classification are generally addressed by remotely sensed imagery and supervised classification methods. However, a high-performance classifier is desirable but challenging due to the existence of model hyperparameters. Conventional approaches generally rely on manual tuning, which is time-consuming and far from satisfying. Therefore, this work aims to propose a systematic method to automatically tune the hyperparameters by Bayesian parameter optimization for the random forest classifier. The recently launched Sentinel-2A/B satellites are drawn to provide the remote sensing imageries for land cover classification case study in Beijing, China, which have the best spectral/spatial resolutions among the freely available satellites. The improved random forest with Bayesian parameter optimization is compared against the support vector machine (SVM) and random forest (RF) with default hyperparameters by discriminating five land cover classes including building, tree, road, water, and crop field. Comparative experimental results show that the optimized RF classifier outperforms the conventional SVM and the RF with default hyperparameters in terms of accuracy, precision, and recall. The effects of band/feature number and the band usefulness are also assessed. It is envisaged that the improved classifier for Sentinel-2 satellite image processing can find a wide range of applications where high-resolution satellite imagery classification is applicable.

scholarly journals UAV-Based Land Cover Classification for Hoverfly (Diptera: Syrphidae) Habitat Condition Assessment: A Case Study on Mt. Stara Planina (Serbia)

2021 ◽  
Vol 13 (16) ◽  
pp. 3272
Author(s):  
Bojana Ivošević ◽  
Predrag Lugonja ◽  
Sanja Brdar ◽  
Mirjana Radulović ◽  
Ante Vujić ◽  
...  
Keyword(s):  
Random Forest ◽  
Land Cover ◽  
Agricultural Land ◽  
Habitat Degradation ◽  
Land Cover Classification ◽  
Machine Learning Algorithms ◽  
Future Research ◽  
Support Vector ◽  
Anthropogenic Pressure ◽  
Study Sites

Habitat degradation, mostly caused by human impact, is one of the key drivers of biodiversity loss. This is a global problem, causing a decline in the number of pollinators, such as hoverflies. In the process of digitalizing ecological studies in Serbia, remote-sensing-based land cover classification has become a key component for both current and future research. Object-based land cover classification, using machine learning algorithms of very high resolution (VHR) imagery acquired by an unmanned aerial vehicle (UAV) was carried out in three different study sites on Mt. Stara Planina, Eastern Serbia. UAV land cover classified maps with seven land cover classes (trees, shrubs, meadows, road, water, agricultural land, and forest patches) were studied. Moreover, three different classification algorithms—support vector machine (SVM), random forest (RF), and k-NN (k-nearest neighbors)—were compared. This study shows that the random forest classifier performs better with respect to the other classifiers in all three study sites, with overall accuracy values ranging from 0.87 to 0.96. The overall results are robust to changes in labeling ground truth subsets. The obtained UAV land cover classified maps were compared with the Map of the Natural Vegetation of Europe (EPNV) and used to quantify habitat degradation and assess hoverfly species richness. It was concluded that the percentage of habitat degradation is primarily caused by anthropogenic pressure, thus affecting the richness of hoverfly species in the study sites. In order to enable research reproducibility, the datasets used in this study are made available in a public repository.

scholarly journals Impact of Various Atmospheric Corrections on Sentinel-2 Land Cover Classification Accuracy Using Machine Learning Classifiers

2020 ◽  
Vol 9 (4) ◽  
pp. 277 ◽  
Author(s):  
Luka Rumora ◽  
Mario Miler ◽  
Damir Medak
Keyword(s):  
Machine Learning ◽  
Land Cover ◽  
Classification Accuracy ◽  
Atmospheric Correction ◽  
Land Cover Classification ◽  
Support Vector ◽  
Atmospheric Effects ◽  
Surface Reflectance ◽  
The Impact ◽  
Sentinel 2

Atmospheric correction is one of the key parts of remote sensing preprocessing because it can influence and change the final classification result. This research examines the impact of five different atmospheric correction processing on land cover classification accuracy using Sentinel-2 satellite imagery. Those are surface reflectance (SREF), standardized surface reflectance (STDSREF), Sentinel-2 atmospheric correction (S2AC), image correction for atmospheric effects (iCOR), dark object subtraction (DOS) and top of the atmosphere (TOA) reflectance without any atmospheric correction. Sentinel-2 images corrected with stated atmospheric corrections were classified using four different machine learning classification techniques namely extreme gradient boosting (XGB), random forests (RF), support vector machine (SVM) and catboost (CB). For classification, five different classes were used: bare land, low vegetation, high vegetation, water and built-up area. SVM classification provided the best overall result for twelve dates, for all atmospheric corrections. It was the best method for both cases: when using Sentinel-2 bands and radiometric indices and when using just spectral bands. The best atmospheric correction for classification with SVM using radiometric indices is S2AC with the median value of 96.54% and the best correction without radiometric indices is STDSREF with the median value of 96.83%.

scholarly journals Evaluating the Performance of a Random Forest Kernel for Land Cover Classification

2019 ◽  
Vol 11 (5) ◽  
pp. 575 ◽  
Author(s):  
Azar Zafari ◽  
Raul Zurita-Milla ◽  
Emma Izquierdo-Verdiguier
Keyword(s):  
Random Forest ◽  
Land Cover ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Computational Cost ◽  
High Dimensional ◽  
Support Vector ◽  
Spectral Features ◽  
Salinas Valley ◽  
Rbf Kernel

The production of land cover maps through satellite image classification is a frequent task in remote sensing. Random Forest (RF) and Support Vector Machine (SVM) are the two most well-known and recurrently used methods for this task. In this paper, we evaluate the pros and cons of using an RF-based kernel (RFK) in an SVM compared to using the conventional Radial Basis Function (RBF) kernel and standard RF classifier. A time series of seven multispectral WorldView-2 images acquired over Sukumba (Mali) and a single hyperspectral AVIRIS image acquired over Salinas Valley (CA, USA) are used to illustrate the analyses. For each study area, SVM-RFK, RF, and SVM-RBF were trained and tested under different conditions over ten subsets. The spectral features for Sukumba were extended by obtaining vegetation indices (VIs) and grey-level co-occurrence matrices (GLCMs), the Salinas dataset is used as benchmarking with its original number of features. In Sukumba, the overall accuracies (OAs) based on the spectral features only are of 81.34 % , 81.08 % and 82.08 % for SVM-RFK, RF, and SVM-RBF. Adding VI and GLCM features results in OAs of 82 % , 80.82 % and 77.96 % . In Salinas, OAs are of 94.42 % , 95.83 % and 94.16 % . These results show that SVM-RFK yields slightly higher OAs than RF in high dimensional and noisy experiments, and it provides competitive results in the rest of the experiments. They also show that SVM-RFK generates highly competitive results when compared to SVM-RBF while substantially reducing the time and computational cost associated with parametrizing the kernel. Moreover, SVM-RFK outperforms SVM-RBF in high dimensional and noisy problems. RF was also used to select the most important features for the extended dataset of Sukumba; the SVM-RFK derived from these features improved the OA of the previous SVM-RFK by 2%. Thus, the proposed SVM-RFK classifier is as at least as good as RF and SVM-RBF and can achieve considerable improvements when applied to high dimensional data and when combined with RF-based feature selection methods.

scholarly journals Object-Based Multigrained Cascade Forest Method for Wetland Classification Using Sentinel-2 and Radarsat-2 Imagery

Water ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 82
Author(s):  
Huaxin Liu ◽  
Qigang Jiang ◽  
Yue Ma ◽  
Qian Yang ◽  
Pengfei Shi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Classification Scheme ◽  
Land Cover Classification ◽  
Support Vector ◽  
Wetland Classification ◽  
Positive Contribution ◽  
Object Based ◽  
Extreme Gradient Boosting ◽  
Sentinel 2

The development of advanced and efficient methods for mapping and monitoring wetland regions is essential for wetland resources conservation, management, and sustainable development. Although remote sensing technology has been widely used for detecting wetlands information, it remains a challenge for wetlands classification due to the extremely complex spatial patterns and fuzzy boundaries. This study aims to implement a comprehensive and effective classification scheme for wetland land covers. To achieve this goal, a novel object-based multigrained cascade forest (OGCF) method with multisensor data (including Sentinel-2 and Radarsat-2 remote sensing imagery) was proposed to classify the wetlands and their adjacent land cover classes in the wetland National Natural Reserve. Moreover, a hybrid selection method (ReliefF-RF) was proposed to optimize the feature set in which the spectral and polarimetric decomposition features are contained. We obtained six spectral features from visible and shortwave infrared bands and 10 polarimetric decomposition features from the H/A/Alpha, Pauli, and Krogager decomposition methods. The experimental results showed that the OGCF method with multisource features for land cover classification in wetland regions achieved the overall accuracy and kappa coefficient of 88.20% and 0.86, respectively, which outperformed the support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), and deep neural network (DNN). The accuracy of the wetland classes ranged from 75.00% to 97.53%. The proposed OGCF method exhibits a good application potential for wetland land cover classification. The classification scheme in this study will make a positive contribution to wetland inventory and monitoring and be able to provide technical support for protecting and developing natural resources.

Sign in / Sign up

Export Citation Format

Share Document