Chen Xingyu, Zhang Xiuyu, Zhuang Changwei, Dai Xuejiao, Kong Lingling, Xie Zixia, Hu Xibang
Institute of Ecological Civilization and Green Development, Guangdong Provincial Academy of Environmental Science, Guangzhou 510045, China.
Ecological Environment Remote Sensing Research Center, Guangdong Provincial Academy of Environmental Science, Guangzhou 510045, China.
Sensors (Basel). 2025 Apr 17;25(8):2540. doi: 10.3390/s25082540.
Mangrove ecosystems are important in tropical and subtropical coastal zones, contributing to marine biodiversity and maintaining marine ecological balance. It is crucial to develop more efficient, intelligent, and accurate monitoring methods for mangroves to understand better and protect mangrove ecosystems. This study promotes a novel model, MangroveNet, for integrating multi-scale spectral and spatial information and detecting mangrove area. In addition, we also present an improved model, AttCloudNet+, to identify the distribution of mangrove species based on high-resolution multispectral drone images. These models incorporate spectral and spatial attention mechanisms and have been shown to effectively address the limitations of traditional methods, which have been prone to inaccuracy and low efficiency in mangrove species identification. In this study, we compare the results from MangroveNet with SegNet, UNet, and DeepUNet, etc. The findings demonstrate that the MangroveNet exhibits superior generalization learning capabilities and more accurate extraction outcomes than other deep learning models. The accuracy, F1_Score, mIoU, and precision of MangroveNet were 99.13%, 98.84%, 98.11%, and 99.14%, respectively. In terms of identifying mangrove species, the prediction results from AttCloudNet+ were compared with those obtained from traditional supervised and unsupervised classifications and various machine learning and deep learning methods. These include K-means clustering, ISODATA cluster analysis, Random Forest (RF), Support Vector Machines (SVM), and others. The comparison demonstrates that the mangrove species identification results obtained using AttCloudNet+ exhibit the most optimal performance in terms of the Kappa coefficient and the overall accuracy (OA) index, reaching 0.81 and 0.87, respectively. The two comparison results confirm the effectiveness of the two models developed in this study for identifying mangroves and their species. Overall, we provide an efficient solution based on deep learning with a dual attention mechanism in the acceptable real-time monitoring of mangroves and their species using high-resolution multispectral drone imagery.
红树林生态系统在热带和亚热带沿海地区至关重要，有助于维护海洋生物多样性和保持海洋生态平衡。开发更高效、智能和准确的红树林监测方法对于更好地了解和保护红树林生态系统至关重要。本研究提出了一种新型模型MangroveNet，用于整合多尺度光谱和空间信息并检测红树林面积。此外，我们还提出了一种改进模型AttCloudNet+，用于基于高分辨率多光谱无人机图像识别红树林物种的分布。这些模型纳入了光谱和空间注意力机制，并已证明能有效解决传统方法的局限性，传统方法在红树林物种识别中容易出现不准确和效率低的问题。在本研究中，我们将MangroveNet的结果与SegNet、UNet和DeepUNet等进行了比较。结果表明，MangroveNet比其他深度学习模型具有更强的泛化学习能力和更准确的提取结果。MangroveNet的准确率、F1_Score、mIoU和精确率分别为99.13%、98.84%、98.11%和99.14%。在识别红树林物种方面，将AttCloudNet+的预测结果与传统监督和非监督分类以及各种机器学习和深度学习方法（包括K均值聚类、ISODATA聚类分析、随机森林（RF）、支持向量机（SVM）等）的结果进行了比较。比较表明，使用AttCloudNet+获得的红树林物种识别结果在卡帕系数和总体准确率（OA）指标方面表现最优，分别达到0.81和0.87。这两个比较结果证实了本研究开发的两种模型在识别红树林及其物种方面的有效性。总体而言，我们基于深度学习提供了一种高效解决方案，该方案具有双重注意力机制，可用于使用高分辨率多光谱无人机图像对红树林及其物种进行可接受的实时监测。
