Deep Convolutional Neural Network for Mangrove Mapping

Iovan, Corina; Kulbicki, Michel; Mermet, Éric

doi:10.1109/igarss39084.2020.9323802

Cited by 14 publications

(7 citation statements)

References 19 publications

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“…We used LabelMe 4.5 software, which is a tool for image annotation, to label the pixels in satellite images as either mangrove or non-mangrove (background). Similar to the method used in some studies on mangrove classification (e.g., [14]) 2020), labels were created through visual interpretation based on collected reference samples from high-resolution satellite imagery and previous mangrove maps. We removed the images that had less than 1% of mangrove pixels, as they are not suitable for mangrove detection.…”

Section: Model Performance Evaluation Methodsmentioning

confidence: 99%

“…With the development of deep learning in the field of image recognition, the application of deep learning in mangrove monitoring has also become a research hotspot. Iovan et al [14] proposed a deep convolutional neural network (CNN) that automatically detects mangroves over Fidji in the South Pacific Ocean from Sentinel-2 and World-View 2 images with resolutions of 10 m and 50 m, respectively. Huang et al [15] applied the LeNet-5 network to extract mangrove species information from unmanned aerial vehicle (UAV) images, resulting in an overall recognition rate of 87.31%.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Classification of High-Resolution Satellite Images for Mangrove Mapping

Cheng²,

Yin

et al. 2023

Applied Sciences

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Detailed information about mangroves is crucial for ecological and environmental protection and sustainable development. It is difficult to capture small patches of mangroves from satellite images with relatively low to medium resolution. In this study, high-resolution (0.8–2 m) images from Chinese GaoFen (GF) and ZiYuan (ZY) series satellites were used to map the distribution of mangroves in coastal areas of Guangdong Province, China. A deep-learning network, U2-Net, with attention gates was applied to extract multi-scale information of mangroves from satellite images. The results showed that the attention U2-Net model performed well on mangrove classification. The overall accuracy, precision, and F1-score values were 96.5%, 92.0%, and 91.5%, respectively, which were higher than those obtained from other machine-learning methods such as Random Forest or U-Net. Based on the high-resolution mangrove maps generated from long satellite image time series, we also investigated the spatiotemporal evolution of the mangrove forest in Shuidong Bay. The results can provide crucial information for government administrators, scientists, and other stakeholders to monitor the dynamic changes in mangroves.

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Section: Model Performance Evaluation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Classification of High-Resolution Satellite Images for Mangrove Mapping

Cheng²,

Yin

et al. 2023

Applied Sciences

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“…Lin et al [42] used Convex Deep Mangrove Mapping (CODE-MM) to map mangroves in several countries, along with Sentinel-2 images and training data of around 4-50 million pixels, to achieve an overall accuracy of 86.16-97.65%. Iovan et al [43] used a Deep Convolutional Neural Network for mangrove mapping in Fiji, South Pacific Ocean with Sentinel-2 and World-View 2 imagery. Diniz et al [44] mapped mangroves using a random forest (RF) model, with Landsat-8, Landsat-7, and Landsat-5 imagery.…”

Section: Introductionmentioning

confidence: 99%

LinkNet-Spectral-Spatial-Temporal Transformer Based on Few-Shot Learning for Mangrove Loss Detection with Small Dataset

Panuntun,

Jamaluddin,

Chen

et al. 2024

Remote Sensing

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Mangroves grow in intertidal zones in tropical and subtropical regions, offering numerous advantages to humans and ecosystems. Mangrove monitoring is one of the important tasks to understand the current status of mangrove forests regarding their loss issues, including deforestation and degradation. Currently, satellite imagery is widely employed to monitor mangrove ecosystems. Sentinel-2 is an optical satellite imagery whose data are available for free, and which provides satellite imagery at a 5-day temporal resolution. Analyzing satellite images before and after loss can enhance our ability to detect mangrove loss. This paper introduces a LSST-Former model that considers the situation before and after mangrove loss to categorize non-mangrove areas, intact mangroves, and mangrove loss categories using Sentinel-2 images for a limited number of labels. The LSST-Former model was developed by integrating a fully convolutional network (FCN) and a transformer base with few-shot learning algorithms to extract information from spectral-spatial-temporal Sentinel-2 images. The attention mechanism in the transformer algorithm may effectively mitigate the issue of limited labeled samples and enhance the accuracy of learning correlations between samples, resulting in more successful classification. The experimental findings demonstrate that the LSST-Former model achieves an overall accuracy of 99.59% and an Intersection-over-Union (IoU) score of 98.84% for detecting mangrove loss, and the validation of universal applicability achieves an overall accuracy of more than 92% and a kappa accuracy of more than 89%. LSST-Former demonstrates superior performance compared to state-of-the-art deep-learning models such as random forest, Support Vector Machine, U-Net, LinkNet, Vision Transformer, SpectralFormer, MDPrePost-Net, and SST-Former, as evidenced by the experimental results and accuracy metrics.

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“…Exploring classification models is an effective way since it can make up the limitations of previous proposed classifiers. Various related studies mainly used ensemble learning [14]- [16], support vector machine (SVM) [17]- [19], decision tree (DT) [20], [21], and deep learning [22]- [24].…”

Section: Introductionmentioning

confidence: 99%

“…• Deep learning is gaining widespread popularity in the remote sensing community recently since it can extract high-level features directly from the raw input data. For example, Iovan et al [22] designed a model based on deep convolutional neural network (CNN) using WorldView-2 and Sentinel-2 images. Guo et al [23] also utilized CNN, but the difference is that they embedded three modules to improve performance.…”

Section: Introductionmentioning

confidence: 99%

Generalized Composite Mangrove Index for Mapping Mangroves Using Sentinel-2 Time Series Data

Xue

Qian

2022

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

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Monitoring mangroves is critical to protect the coastal ecosystems. Some studies resorted to remote sensing for constructing mangrove indices (MIs). However, there are still some drawbacks in existing MIs. On the one hand, difficulty still persists in distinguishing mangroves from non-mangrove vegetation and non-vegetated areas at the same time. On the other hand, the existing MIs have not fully utilized the phenological trajectories, which can greatly help to distinguish mangroves from other land covers. To overcome these issues, we built a novel mangrove index, namely generalized composite mangrove index (GCMI) by compositing vegetation indices (VIs) and water indices (WIs) based on Sentinel-2 time series data. Firstly, to determine the optimal indices, a similarity trend distance (ST distance) measure was proposed based on Pearson correlation coefficient and dynamic time warping (DTW). Secondly, in order to optimize the weights of selected indices, a population reconstruction genetic algorithm (PRGA) was designed. Finally, mangroves were mapped by feeding the time series of GCMI into random forest (RF) classifier. Experiments conducted over three areas along the southern coast of China demonstrate that: 1) GCMI enhances the separability between mangroves and other land covers compared to the existing VIs, WIs, and MIs, with an averaged OA of 91.45%; 2) ST distance outperforms Euclidean distance, Cosine distance, Pearson correlation coefficient, and DTW in optimizing the weights of GCMI; and 3) PRGA greatly improves the probability of attaining global optimal result. The innovation lies in the presented GCMI considering both the vegetation trajectory information and water inundation using time series.

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Deep Convolutional Neural Network for Mangrove Mapping

Cited by 14 publications

References 19 publications

Deep Learning-Based Classification of High-Resolution Satellite Images for Mangrove Mapping

Deep Learning-Based Classification of High-Resolution Satellite Images for Mangrove Mapping

LinkNet-Spectral-Spatial-Temporal Transformer Based on Few-Shot Learning for Mangrove Loss Detection with Small Dataset

Generalized Composite Mangrove Index for Mapping Mangroves Using Sentinel-2 Time Series Data

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