Crop classification in a heterogeneous agricultural environment using ensemble classifiers and single-date Sentinel-2A imagery

Saini, Rashmi; Ghosh, S. K.

doi:10.1080/10106049.2019.1700556

Cited by 41 publications

(19 citation statements)

References 46 publications

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“…Among non-deep-learning classifiers that they used, XGBoost produced the best outcomes and its accuracy was slightly lower than the proposed deep learning model. Saini and Ghosh (2019) used Sentinel-2 images in their study and compared the efficiency of XGBoost, RF, and SVM algorithms in crop classification of agricultural environment [26]. They verified the outperformance of XGBoost method over the rest of the classifiers.…”

Section: Bagging Boostingmentioning

confidence: 96%

Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation

et al. 2021

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In recent years, several powerful machine learning (ML) algorithms have been developed for image classification, especially those based on ensemble learning (EL). In particular, Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) methods have attracted researchers’ attention in data science due to their superior results compared to other commonly used ML algorithms. Despite their popularity within the computer science community, they have not yet been well examined in detail in the field of Earth Observation (EO) for satellite image classification. As such, this study investigates the capability of different EL algorithms, generally known as bagging and boosting algorithms, including Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), XGBoost, LightGBM, and Random Forest (RF), for the classification of Remote Sensing (RS) data. In particular, different classification scenarios were designed to compare the performance of these algorithms on three different types of RS data, namely high-resolution multispectral, hyperspectral, and Polarimetric Synthetic Aperture Radar (PolSAR) data. Moreover, the Decision Tree (DT) single classifier, as a base classifier, is considered to evaluate the classification’s accuracy. The experimental results demonstrated that the RF and XGBoost methods for the multispectral image, the LightGBM and XGBoost methods for hyperspectral data, and the XGBoost and RF algorithms for PolSAR data produced higher classification accuracies compared to other ML techniques. This demonstrates the great capability of the XGBoost method for the classification of different types of RS data.

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Section: Bagging Boostingmentioning

confidence: 96%

Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation

et al. 2021

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“…In recent years, with the availability and accessibility of remote sensing data, a huge variety of applications like crop type classification, vegetation mapping, forestry, precision agriculture, landslide susceptibility mapping, built up extraction, etc. have attracted the attention of multidisciplinary researchers [1][2][3][4][5][6][7][8]. Vegetation mapping is one of the essential application needs which has to be addressed effectively for overall environmental monitoring [8].…”

Section: Introductionmentioning

confidence: 99%

“…have attracted the attention of multidisciplinary researchers [1][2][3][4][5][6][7][8]. Vegetation mapping is one of the essential application needs which has to be addressed effectively for overall environmental monitoring [8]. The utilization of remotely sensed data is the optimal way for vegetation mapping because of the free availability of medium and coarser spatial resolution data, having different spatial and spectral properties, cost effective and less time consuming in comparison to traditional field survey methods [7,8].…”

Section: Introductionmentioning

confidence: 99%

Integrating Vegetation Indices and Spectral Features for Vegetation Mapping from Multispectral Satellite Imagery Using AdaBoost and Random Forest Machine Learning Classifiers

Saini

2022

GaEE

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Vegetation mapping is an active research area in the domain of remote sensing. This study proposes a methodology for the mapping of vegetation by integrating several vegetation indices along with original spectral bands. The Land Use Land Cover classification was performed by two powerful Machine Learning techniques, namely Random Forest and AdaBoost. The Random Forest algorithm works on the concept of building multiple decision trees for the final prediction. The other Machine Learning technique selected for the classification is AdaBoost (adaptive boosting), converts a set of weak learners into strong learners. Here, multispectral satellite data of Dehradun, India, was utilised. The results demonstrate an increase of 3.87% and 4.32% after inclusion of selected vegetation indices by Random Forest and AdaBoost respectively. An Overall Accuracy (OA) of 91.23% (kappa value of 0.89) and 88.59% (kappa value of 0.86) was obtained by means of the Random Forest and AdaBoost classifiers respectively. Although Random Forest achieved greater OA as compared to AdaBoost, interestingly AdaBoost provided better class-specific accuracy for the Shrubland class compared to Random Forest. Furthermore, this study also evaluated the importance of each individual feature used in the classification. Results demonstrated that the NDRE, GNDVI, and RTVIcore vegetation indices, and spectral bands (NIR, and Red-Edge), obtained higher importance scores.

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“…For most of the researchers devoted to crop classification and mapping, traditional algorithms such as Maximum Likelihood Classifier (MLC) [24], Random Forest (RF) [25], and Support Vector Machine (SVM) [26,27] were used. These methods usually work at the pixel-level of remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Mapping of Paddy Rice Using Multi-Temporal Landsat Data Based on a Deep Semantic Segmentation Model

Huang

Wei

et al. 2022

Agronomy

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Timely, accurate, and repeatable crop mapping is vital for food security. Rice is one of the important food crops. Efficient and timely rice mapping would provide critical support for rice yield and production prediction as well as food security. The development of remote sensing (RS) satellite monitoring technology provides an opportunity for agricultural modernization applications and has become an important method to extract rice. This paper evaluated how a semantic segmentation model U-net that used time series Landsat images and Cropland Data Layer (CDL) performed when applied to extractions of paddy rice in Arkansas. Classifiers were trained based on time series images from 2017–2019, then were transferred to corresponding images in 2020 to obtain resultant maps. The extraction outputs were compared to those produced by Random Forest (RF). The results showed that U-net outperformed RF in most scenarios. The best scenario was when the time resolution of the data composite was fourteen day. The band combination including red band, near-infrared band, and Swir-1 band showed notably better performance than the six widely used bands for extracting rice. This study found a relatively high overall accuracy of 0.92 for extracting rice with training samples including five years from 2015 to 2019. Finally, we generated dynamic maps of rice in 2020. Rice could be identified in the heading stage (two months before maturing) with an overall accuracy of 0.86 on July 23. Accuracy gradually increased with the date of the mapping date. On September 17, overall accuracy was 0.92. There was a significant linear relationship (slope = 0.9, r2 = 0.75) between the mapped areas on July 23 and those from the statistical reports. Dynamic mapping is not only essential to assist farms and governments for growth monitoring and production assessment in the growing season, but also to support mitigation and disaster response strategies in the different growth stages of rice.

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Crop classification in a heterogeneous agricultural environment using ensemble classifiers and single-date Sentinel-2A imagery

Cited by 41 publications

References 46 publications

Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation

Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation

Integrating Vegetation Indices and Spectral Features for Vegetation Mapping from Multispectral Satellite Imagery Using AdaBoost and Random Forest Machine Learning Classifiers

Dynamic Mapping of Paddy Rice Using Multi-Temporal Landsat Data Based on a Deep Semantic Segmentation Model

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