A Hybrid Data Balancing Method for Classification of Imbalanced Training Data within Google Earth Engine: Case Studies from Mountainous Regions

Naboureh, Amin; Li, Ainong; Bian, Jinhu; Lei, Guangbin; Amani, Meisam

doi:10.3390/rs12203301

Cited by 30 publications

(13 citation statements)

References 59 publications

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“…To date, three approaches have been proposed to address the class imbalance problem: (1) applying specific classification methods by focusing on the learning of minority classes, (2) assigning higher weights on minority classes by adjusting classifiers, and (3) rebalancing training datasets (e.g., oversampling and under-sampling techniques). A hybrid data-balancing method, called the Partial Random Over-Sampling and Random Under-Sampling (PROSRUS), was proposed in [96] to resolve the class imbalance issue. The class imbalance problem reduces classification accuracy for infrequent and rare LC classes.…”

Section: Land Cover Classificationmentioning

confidence: 99%

“…The authors in [96] proposed a hybrid data balancing method, called the Partial Random Over-Sampling and Random Under-Sampling (PROSRUS), to resolve the class imbalance issue. PROSRUS used a partial balancing approach with hundreds of fractions for majority and minority classes to balance datasets.…”

Section: Appendix a The Accompanying Interactive Web App Tool For The...mentioning

confidence: 99%

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Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review

et al. 2022

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Remote sensing (RS) plays an important role gathering data in many critical domains (e.g., global climate change, risk assessment and vulnerability reduction of natural hazards, resilience of ecosystems, and urban planning). Retrieving, managing, and analyzing large amounts of RS imagery poses substantial challenges. Google Earth Engine (GEE) provides a scalable, cloud-based, geospatial retrieval and processing platform. GEE also provides access to the vast majority of freely available, public, multi-temporal RS data and offers free cloud-based computational power for geospatial data analysis. Artificial intelligence (AI) methods are a critical enabling technology to automating the interpretation of RS imagery, particularly on object-based domains, so the integration of AI methods into GEE represents a promising path towards operationalizing automated RS-based monitoring programs. In this article, we provide a systematic review of relevant literature to identify recent research that incorporates AI methods in GEE. We then discuss some of the major challenges of integrating GEE and AI and identify several priorities for future research. We developed an interactive web application designed to allow readers to intuitively and dynamically review the publications included in this literature review.

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Section: Land Cover Classificationmentioning

confidence: 99%

Section: Appendix a The Accompanying Interactive Web App Tool For The...mentioning

confidence: 99%

Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review

et al. 2022

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“…As can be seen in Table 4 and Table 5, both minority and majority classes show acceptable UA and PA values while it has been reported that standard ML classifiers (i.e., RF) often fail to achieve reasonable accuracies for minority classes [62,63]. The reason can be that the proposed classification scheme divides the study area into smaller sub-areas and substantially avoids the occurrence of the data imbalance issue.…”

Section: B Performance Of the Proposed Classification Schemementioning

confidence: 92%

National Scale Land Cover Classification Using the Semiautomatic High-Quality Reference Sample Generation (HRSG) Method and an Adaptive Supervised Classification Scheme

Naboureh

Bian

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The advent of new high-performance cloud computing platforms (e.g., Google Earth Engine (GEE)) and freely available satellite data provides a great opportunity for land cover (LC) mapping over large-scale areas. However, the shortage of reliable and sufficient reference samples still hinders large-scale LC classification. Here, selecting Turkey as the case study, we presented a Semi-automatic High-quality Reference Sample Generation (HRSG) method using the publicly available scientific LC products and the linear spectral unmixing analysis to generate high-quality ground samples for the years 1995 and 2020 within the GEE platform. Furthermore, we developed an adaptive random forest classification scheme based on Köppen-Geiger climate zone classification system. Our rationale was related to the fact that large-scale study areas often contain multiple climate zones where the spectral signature of the same LC class may vary within different climate zones that can lead to a poor LC classification accuracy. To have a robust assessment, the generated LC maps were evaluated against independent test datasets. In regard to the proposed sample generation method, it was observed that HRSG can generate high-quality samples independent of the characteristics of scientific LC products. The high overall accuracy of 92% for 2020 and 90% for 1995 and satisfactory results for producer's accuracy (ranging between 83.4% and 99.3%) and user's accuracy (ranging between 86.1% and 99.7%) of nine LC classes demonstrated the effectiveness of the proposed framework. The presented methodologies can be incorporated into future studies related to large scale LC mapping and LC change monitoring studies.

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“…However, the proposed methodologies have primarily examined specific landscapes, and their performances in different landscapes have not been investigated. For example, Naboureh et al [20] proposed a hybrid data balancing method for mountainous regions. In another study, Waldner et al [21] investigated the impact of different data balancing techniques for mapping crops.…”

Section: Introductionmentioning

confidence: 99%

Integration of Sentinel-1 and Sentinel-2 Data with the G-SMOTE Technique for Boosting Land Cover Classification Accuracy

et al. 2021

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The importance of Land Cover (LC) classification is recognized by an increasing number of scholars who employ LC information in various applications (i.e., address global climate change and achieve sustainable development). However, studying the roles of balancing data, image integration, and performance of different machine learning algorithms in various landscapes has not received as much attention from scientists. Therefore, the present study investigates the performance of three frequently used Machine Learning (ML) algorithms, including Extreme Learning Machines (ELM), Support Vector Machines (SVM), and Random Forest (RF) in LC mapping at six different landscapes. Moreover, the Geometric Synthetic Minority Over-sampling Technique (G-SMOTE) was adopted to deal with the class imbalance problem. In this work, the time-series of Sentinel-1 and Sentinel-2 data were integrated to improve LC mapping accuracy, taking advantage of both data. Moreover, Support Vector Machine-Recursive Feature Elimination (SVM-RFE) was implemented to distinguish the most informative features. Based on the results, the RF integrated with G-SMOTE showed the best result for four landscapes (coastal, cropland, desert, and semi-arid). SVM integrated with G-SMOTE had the highest accuracy in the remaining two landscapes (plain and mountain). Applied ML algorithms showed good performances in various landscapes, ranging Overall Accuracy (OA) from 85% to 93% for RF, 83% to 94% for SVM, and 84% to 92% for ELM. The outcomes exhibit that although applying G-SMOTE may slightly decrease OA values, it generally boosts the results of LC classification accuracies in various landscapes, particularly for minority classes.

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A Hybrid Data Balancing Method for Classification of Imbalanced Training Data within Google Earth Engine: Case Studies from Mountainous Regions

Cited by 30 publications

References 59 publications

Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review

Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review

National Scale Land Cover Classification Using the Semiautomatic High-Quality Reference Sample Generation (HRSG) Method and an Adaptive Supervised Classification Scheme

Integration of Sentinel-1 and Sentinel-2 Data with the G-SMOTE Technique for Boosting Land Cover Classification Accuracy

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