探索多源遥感图像不同组合比例对红树林群落分类的影响

IF 7.6 Q1 REMOTE SENSING
Bolin Fu , Shurong Zhang , Huajian Li , Hang Yao , Weiwei Sun , Mingming Jia , Yanli Yang , Hongchang He , Yuyang Li
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引用次数: 0

摘要

红树林是全球最重要的海洋生态系统之一,其空间分布对促进红树林生态系统的保护、恢复和可持续管理至关重要。本研究提出了一种新颖的 Unet 多尺度高分辨率视觉变换器(UHRViT)模型,用于利用无人机(UAV-RGB)、无人机激光雷达(UAV-LiDAR)和高分三号合成孔径雷达(GF-3 SAR)图像对红树林物种进行分类。UHRViT 采用多尺度高分辨率视觉转换器作为骨干网络,并设计成多分支 U 型网络结构,以逐层提取不同尺度的特征,并促进高低级语义信息的交互。通过与 HRViT 和 HRNetV2 算法的比较,我们进一步验证了 UHRViT 模型的分类性能优势。我们还系统地研究了主动-被动图像组合比例对红树林群落绘图的影响。结果显示与 UAV-LiDAR 和 GF-3 SAR 图像相比,UAV-RGB 图像表现出更高的红树林物种分类准确率(平均 F1 分数>95%);UHRViT 算法在 15 个数据集中的分类性能和稳定性优于 HRViT 和 HRNetV2 算法;将 UAV-RGB 与 GF-3 SAR 或 UAV-LiDAR 图像分别结合使用,分类效果均优于单一数据源。根据 UHRViT 算法,UAV-RGB 与 UAV-LiDAR 的组合对 Avicennia corniculatum(AC)的分类准确率最高(Iou = 0.944,MIou = 50.2 %)。当 UAV-RGB 与 GF-3 SAR 或 UAV-LiDAR 的组合比例为 3:1 时,Avicennia marina 和 AC 都获得了最佳分类精度,平均 F1 分数分别为 98.19 % 和 97.3 %。我们的研究揭示了多传感器图像组合比例下红树林群落分类精度的变化,证明我们的模型能有效提高红树林群落的分类精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring the effects of different combination ratios of multi-source remote sensing images on mangrove communities classification
Mangroves are one of the most important marine ecosystems globally, their spatial distribution is crucial for promoting mangrove ecosystems conservation, restoration, and sustainable managements. This study proposed a novel Unet-Multi-Scale High-Resolution Vision Transformer (UHRViT) model for classifying mangrove species using unmanned aerial vehicle (UAV-RGB), UAV-LiDAR, and Gaofen-3 Synthetic Aperture Radar (GF-3 SAR) images. The UHRViT utilized a multi-scale high-resolution visual Transformer as its backbone network and was designed to a multi-branch U-shaped network structure to extract features of different scales layer by layer, and to facilitate the interaction of high and low-level semantic information. We further verified the classification performance superiority of UHRViT model by comparing to HRViT and HRNetV2 algorithms. We also systematically investigated the effects of active–passive image combination ratios on mangrove communities mapping. The results revealed that: UAV-RGB images exhibited the better classification accuracy (mean F1-score>95 %) for mangrove species than UAV-LiDAR and GF-3 SAR images; The classification performances and stability of UHRViT algorithm in the fifteen datasets outperformed the HRViT and HRNetV2 algorithms; Combining UAV-RGB with GF-3 SAR or UAV-LiDAR images respectively, both achieved better classifications than the single data source. Based on the UHRViT algorithm, the combination of UAV-RGB and UAV-LiDAR achieved the highest classification accuracy (Iou = 0.944, MIou = 50.2 %) for Avicennia corniculatum (AC). When the combination ratio of UAV-RGB with GF-3 SAR or UAV-LiDAR was 3:1, Avicennia marina and AC both obtained the optimal classification accuracy with average F1-scores of 98.19 % and 97.3 %, respectively. Our works revealed that the changes in the classification accuracies of mangrove communities under multi-sensor image combination ratios, and demonstrated that our model could effectively improve the classification accuracy of mangrove communities.
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来源期刊
International journal of applied earth observation and geoinformation : ITC journal
International journal of applied earth observation and geoinformation : ITC journal Global and Planetary Change, Management, Monitoring, Policy and Law, Earth-Surface Processes, Computers in Earth Sciences
CiteScore
12.00
自引率
0.00%
发文量
0
审稿时长
77 days
期刊介绍: The International Journal of Applied Earth Observation and Geoinformation publishes original papers that utilize earth observation data for natural resource and environmental inventory and management. These data primarily originate from remote sensing platforms, including satellites and aircraft, supplemented by surface and subsurface measurements. Addressing natural resources such as forests, agricultural land, soils, and water, as well as environmental concerns like biodiversity, land degradation, and hazards, the journal explores conceptual and data-driven approaches. It covers geoinformation themes like capturing, databasing, visualization, interpretation, data quality, and spatial uncertainty.
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