Can ensemble techniques improve coral reef habitat classification accuracy using multispectral data?

Hossain, Mohammad Shawkat; Muslim, Aidy M.; Nadzri, Muhammad Izuan; Komatsu, Toshimitsu; David, D. J.; Khalil, Idham; Mohamad, Zaleha

doi:10.1080/10106049.2018.1557263

Cited by 10 publications

(4 citation statements)

References 38 publications

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“…Yet, while the PA and UA of the base classifiers varied significantly among classes, the MCSs had more robust performance among classes in most cases, especially at levels one and three, except for two specific categories in L3-namely, "mixed forest" and "Maritime pine"-likely due to the high within-class spectral variability. Nevertheless, improvement in per-class accuracy with the use of MCS has been reported in the literature [78], indicating that despite a marginal increase in OA, MCS can balance class accuracy differences. While in some applications-for example, binary class mapping-imbalanced balanced class accuracies might be advantageous for classification map quality fine-tuning [79], in forest management, balanced accuracy among classes is usually advantageous since this can limit uncertainty in forest management models and subsequent decisions.…”

Section: Discussionmentioning

confidence: 95%

“…The macro-averaged F1 score is deemed a more suitable metric when assessing the accuracy of maps that display an unbalanced class distribution [59]. Previous research has highlighted that, despite its simplicity, plurality voting is beneficial for merging the results of base classifiers [78]. Conversely, while approaches like LOP-UA, which weigh votes based on accuracy metrics, can be effective, they may encounter stability or transferability issues when applied across different biophysical covers [28].…”

Section: Discussionmentioning

confidence: 99%

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Evaluation of Multiple Classifier Systems for Mapping Different Hierarchical Levels of Forest Ecosystems in the Mediterranean Region Using Sentinel-2, Sentinel-1, and ICESat-2 Data

Mallinis,

Verde,

Siachalou

et al. 2023

Forests

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The conservation and management of forest areas require knowledge about their extent and attributes on multiple scales. The combination of multiple classifiers has been proposed as an attractive classification approach for improved accuracy and robustness that can efficiently exploit the complementary nature of diverse remote sensing data and the merits of individual classifiers. The aim of this study was to develop and evaluate multiple classifier systems (MCSs) within a cloud-based computing environment for multi-scale forest mapping in Northeastern Greece using passive and active remote sensing data. Five individual machine learning base classifiers were used for class discrimination across the three different hierarchy levels, and five ensemble approaches were used for combining them. In the case of the binary classification scheme in the upper level of the hierarchy for separating woody vegetation (forest and shrubs) from other land, the overall accuracy (OA) slightly increased with the use of the MCS approach, reaching 94%. At the lower hierarchical levels, when using the support vector machine (SVM) base classifier, OA reached 84.13% and 74.89% for forest type and species mapping, respectively, slightly outperforming the MCS approach. Yet, two MCS approaches demonstrated robust performance in terms of per-class accuracy, presenting the highest average F1 score across all classification experiments, indicating balanced misclassification errors across all classes. Since the competence of individual classifiers is dependent on individual scene settings and data characteristics, we suggest that the adoption of MCS systems in efficient computing environments (i.e., cloud) could alleviate the need for algorithm benchmarking for Earth’s surface cover mapping.

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Section: Discussionmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 99%

Evaluation of Multiple Classifier Systems for Mapping Different Hierarchical Levels of Forest Ecosystems in the Mediterranean Region Using Sentinel-2, Sentinel-1, and ICESat-2 Data

Mallinis,

Verde,

Siachalou

et al. 2023

Forests

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“…With a similar sensor and a slightly better resolution than IKONOS-2, the Quickbird-2 satellite provides images for several studies of reef mapping [58,[91][92][93][94][95][96]. Please note that the Quickbird-2 program was stopped in 2015.…”

Section: Satellite Datamentioning

confidence: 99%

Mapping of Coral Reefs with Multispectral Satellites: A Review of Recent Papers

et al. 2021

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Coral reefs are an essential source of marine biodiversity, but they are declining at an alarming rate under the combined effects of global change and human pressure. A precise mapping of coral reef habitat with high spatial and time resolutions has become a necessary step for monitoring their health and evolution. This mapping can be achieved remotely thanks to satellite imagery coupled with machine-learning algorithms. In this paper, we review the different satellites used in recent literature, as well as the most common and efficient machine-learning methods. To account for the recent explosion of published research on coral reel mapping, we especially focus on the papers published between 2018 and 2020. Our review study indicates that object-based methods provide more accurate results than pixel-based ones, and that the most accurate methods are Support Vector Machine and Random Forest. We emphasize that the satellites with the highest spatial resolution provide the best images for benthic habitat mapping. We also highlight that preprocessing steps (water column correction, sunglint removal, etc.) and additional inputs (bathymetry data, aerial photographs, etc.) can significantly improve the mapping accuracy.

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“…Large-scale (tens of square kilometers) and fine-scale (detailed mapping using pixels <10 m) studies on corals including reefs and their ecological communities require satellite or airborne images with a high spatial resolution. The unmanned aerial vehicle (UAV) is characterized by better spatial, temporal, and radiometric resolution than any airborne or satellite platform [6][7][8][9]. With multispectral and hyperspectral sensors mounted on UAV platforms, high-resolution, georeferenced data can be acquired for studying spatial and temporal changes in water quality [10] and coral state and bleaching [11,12].…”

Section: Introductionmentioning

confidence: 99%

Coral Reef Mapping of UAV: A Comparison of Sun Glint Correction Methods

et al. 2019

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Although methods were proposed for eliminating sun glint effects from airborne and satellite images over coral reef environments, a method was not proposed previously for unmanned aerial vehicle (UAV) image data. De-glinting in UAV image analysis may improve coral distribution mapping accuracy result compared with an uncorrected image classification technique. The objective of this research was to determine accuracy of coral reef habitat classification maps based on glint correction methods proposed by Lyzenga et al., Joyce, Hedley et al., and Goodman et al.. The UAV imagery collected from the coral-dominated Pulau Bidong (Peninsular Malaysia) on 20 April 2016 was analyzed in this study. Images were pre-processed with the following two strategies: Strategy-1 was the glint removal technique applied to the whole image, while Strategy-2 used only the regions impacted by glint instead of the whole image. Accuracy measures for the glint corrected images showed that the method proposed by Lyzenga et al. following Strategy-2 could eliminate glints over the branching coral—Acropora (BC), tabulate coral—Acropora + Montipora (TC), patch coral (PC), coral rubble (R), and sand (S) with greater accuracy than the other four methods using Strategy-1. Tested in two different coral environments (Site-1: Pantai Pasir Cina and Site-2: Pantai Vietnam), the glint-removed UAV imagery produced reliable maps of coral habitat distribution with finer details. The proposed strategies can potentially be used to remove glint from UAV imagery and may improve usability of glint-affected imagery, for analyzing spatiotemporal changes of coral habitats from multi-temporal UAV imagery.

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Can ensemble techniques improve coral reef habitat classification accuracy using multispectral data?

Cited by 10 publications

References 38 publications

Evaluation of Multiple Classifier Systems for Mapping Different Hierarchical Levels of Forest Ecosystems in the Mediterranean Region Using Sentinel-2, Sentinel-1, and ICESat-2 Data

Evaluation of Multiple Classifier Systems for Mapping Different Hierarchical Levels of Forest Ecosystems in the Mediterranean Region Using Sentinel-2, Sentinel-1, and ICESat-2 Data

Mapping of Coral Reefs with Multispectral Satellites: A Review of Recent Papers

Coral Reef Mapping of UAV: A Comparison of Sun Glint Correction Methods

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