A comprehensive evaluation of classification algorithms for coral reef habitat mapping: challenges related to quantity, quality, and impurity of training samples

Chegoonian, Amir M.; Mokhtarzade, Mehdi; Zoej, Mohammad Javad Valadan

doi:10.1080/01431161.2017.1317934

Cited by 15 publications

(9 citation statements)

References 58 publications

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“…Classification trees suffer from unbalanced sample sizes because the largest number of samples tend to determine the class label [74]. On the other hand, challenges in respect of the quantity and quality of the training samples also affect the performance of the supervised classification [75]. In the reef island sites, the "others" category always had the lowest accuracy, while the "no change" type, which accounted for the largest area of each study site, gained the highest accuracy.…”

Section: Pros and Cons Of The Proposed Obcd Methodsmentioning

confidence: 99%

Change Detection in Coral Reef Environment Using High-Resolution Images: Comparison of Object-Based and Pixel-Based Paradigms

Zhou

et al. 2018

IJGI

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Despite increases in the spatial resolution of satellite imagery prompting interest in object-based image analysis, few studies have used object-based methods for monitoring changes in coral reefs. This study proposes a high accuracy object-based change detection (OBCD) method intended for coral reef environment, which uses QuickBird and WorldView-2 images. The proposed methodological framework includes image fusion, multi-temporal image segmentation, image differencing, random forests models, and object-area-based accuracy assessment. For validation, we applied the method to images of four coral reef study sites in the South China Sea. We compared the proposed OBCD method with a conventional pixel-based change detection (PBCD) method by implementing both methods under the same conditions. The average overall accuracy of OBCD exceeded 90%, which was approximately 20% higher than PBCD. The OBCD method was free from salt-and-pepper effects and was less prone to images misregistration in terms of change detection accuracy and mapping results. The object-area-based accuracy assessment reached a higher overall accuracy and per-class accuracy than the object-number-based and pixel-number-based accuracy assessment. methods for coral reef change detection. However, because of the limitations of medium-resolution sensors such as Landsat and SPOT, it is difficult to distinguish coral reef geomorphological dynamics from sea level rise [10], and to detect changes on the scale of a few meters in coral reef habitats [11]. With continued refinement of the spatial resolution of satellite imagery, conventional per-pixel methods have been found susceptible to a number of challenges in relation to change detection, including image misregistration [6,12] and salt-and-pepper effects [13,14]. Coral reef images, lack of distinct and stable texture features, are difficult to be accurately registered to each other [15], which makes the traditional pixel-based approach less promising in coral reef change detection using high-resolution images.Recent years have seen an increase in the number of studies using object-based image analysis (OBIA) [13]. OBIA has also been applied in coral reef environment, from geomorphological mapping to benthic community discrimination [16][17][18]. OBIA represents an effective combination of both the contextual analysis of visual interpretation and the quantitative analysis of the pixel-based method [19]. It has been proven that image registration error greatly affects the per-pixel change detection accuracy while the object-based method is less sensitive to image misregistration [20,21]. However, to the best of our knowledge, few studies have used object-based change detection (OBCD) methods in coral reef change detection. Generally, there are two possible strategies for OBCD methods: post-classification comparison and multi-temporal image object analysis [22]. The essence of the post-classification comparison approach lies in the initial classification, i.e., images acquired at different times are classifi...

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Section: Pros and Cons Of The Proposed Obcd Methodsmentioning

confidence: 99%

Change Detection in Coral Reef Environment Using High-Resolution Images: Comparison of Object-Based and Pixel-Based Paradigms

Zhou

et al. 2018

IJGI

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“…Underwater images can also be used jointly with satellite images. They can be obtained from underwater photos taken by divers [94,237,238], as well as underwater videos taken from a boat [239].…”

Section: Additional Inputs To Coral Mappingmentioning

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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“…These studies mostly focus on the specific species and have different source data, mapping models, and final targets. Technically, they mainly employ classification or regression models on the independent multi-dimensional vector contained in each pixel of remote sensing images, to predict discrete habitat categories [30] or continuous habitat index [31]. This pixel-based scheme successfully identifies, verifies, and explains the habitat characteristic at the pixel level.…”

Section: Habitat Mappingmentioning

confidence: 99%

Exploration in Mapping Kernel-Based Home Range Models from Remote Sensing Imagery with Conditional Adversarial Networks

Zheng

Yan

et al. 2018

Remote Sensing

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Kernel-based home range models are widely-used to estimate animal habitats and develop conservation strategies. They provide a probabilistic measure of animal space use instead of assuming the uniform utilization within an outside boundary. However, this type of models estimates the home ranges from animal relocations, and the inadequate locational data often prevents scientists from applying them in long-term and large-scale research. In this paper, we propose an end-to-end deep learning framework to simulate kernel home range models. We use the conditional adversarial network as a supervised model to learn the home range mapping from time-series remote sensing imagery. Our approach enables scientists to eliminate the persistent dependence on locational data in home range analysis. In experiments, we illustrate our approach by mapping the home ranges of Bar-headed Geese in Qinghai Lake area. The proposed framework outperforms all baselines in both qualitative and quantitative evaluations, achieving visually recognizable results and high mapping accuracy. The experiment also shows that learning the mapping between images is a more effective way to map such complex targets than traditional pixel-based schemes.

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A comprehensive evaluation of classification algorithms for coral reef habitat mapping: challenges related to quantity, quality, and impurity of training samples

Cited by 15 publications

References 58 publications

Change Detection in Coral Reef Environment Using High-Resolution Images: Comparison of Object-Based and Pixel-Based Paradigms

Change Detection in Coral Reef Environment Using High-Resolution Images: Comparison of Object-Based and Pixel-Based Paradigms

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

Exploration in Mapping Kernel-Based Home Range Models from Remote Sensing Imagery with Conditional Adversarial Networks

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