Feature Fusion to Improve YOLOv8 for Segmenting and Classifying Aerial Images of Tree Crowns
Instance segmentation techniques based on convolutional neural networks (CNNs) is a vital tool for accurately identifying and segmenting individual tree crowns, which plays an essential role in environmental monitoring and forest management. In varied rural landscapes, canopy imagery often includes...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/11024197/ |
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| _version_ | 1849472986103414784 |
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| author | Ziyi Sun Bing Xue Mengjie Zhang Jan Schindler |
| author_facet | Ziyi Sun Bing Xue Mengjie Zhang Jan Schindler |
| author_sort | Ziyi Sun |
| collection | DOAJ |
| description | Instance segmentation techniques based on convolutional neural networks (CNNs) is a vital tool for accurately identifying and segmenting individual tree crowns, which plays an essential role in environmental monitoring and forest management. In varied rural landscapes, canopy imagery often includes a mix of tiny, small, and medium tree objects scattered across diverse terrains, from standalone trees to densely clustered forest stands. This variability poses significant challenges to traditional instance segmentation methods. To achieve this, we introduce a new method named YOLOv8-FF, which incorporates a feature fusion (FF) technique based on the YOLOv8 architecture. We first design a network architecture based on YOLOv8 that is optimized for the characteristics of our dataset, enabling effective segmentation of densely distributed tiny and small tree crowns. Moreover, YOLOv8-FF incorporates a FF mechanism that includes both cross-scale and same-scale fusion methods, enhancing the model’s ability to integrate information across different layers and scales, thereby improving segmentation performance. We incorporate Sparse Large Kernel Network, whose large convolution kernel can effectively extract key features, helping the model capture richer and deeper global information in the image. Experimental results on the tree crown dataset demonstrate that YOLOv8-FF outperforms several recent peer competitors, making it a promising tool for accurate and efficient tree crown instance segmentation. |
| format | Article |
| id | doaj-art-c35e585f574342629da9a47babb82f62 |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-c35e585f574342629da9a47babb82f622025-08-20T03:24:21ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118147521476510.1109/JSTARS.2025.357678011024197Feature Fusion to Improve YOLOv8 for Segmenting and Classifying Aerial Images of Tree CrownsZiyi Sun0https://orcid.org/0000-0002-2011-4553Bing Xue1https://orcid.org/0000-0002-4865-8026Mengjie Zhang2https://orcid.org/0000-0003-4463-9538Jan Schindler3Center for Data Science and Artificial Intelligence & School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New ZealandCenter for Data Science and Artificial Intelligence & School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New ZealandCenter for Data Science and Artificial Intelligence & School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New ZealandManaaki Whenua-Landcare Research, Wellington, New ZealandInstance segmentation techniques based on convolutional neural networks (CNNs) is a vital tool for accurately identifying and segmenting individual tree crowns, which plays an essential role in environmental monitoring and forest management. In varied rural landscapes, canopy imagery often includes a mix of tiny, small, and medium tree objects scattered across diverse terrains, from standalone trees to densely clustered forest stands. This variability poses significant challenges to traditional instance segmentation methods. To achieve this, we introduce a new method named YOLOv8-FF, which incorporates a feature fusion (FF) technique based on the YOLOv8 architecture. We first design a network architecture based on YOLOv8 that is optimized for the characteristics of our dataset, enabling effective segmentation of densely distributed tiny and small tree crowns. Moreover, YOLOv8-FF incorporates a FF mechanism that includes both cross-scale and same-scale fusion methods, enhancing the model’s ability to integrate information across different layers and scales, thereby improving segmentation performance. We incorporate Sparse Large Kernel Network, whose large convolution kernel can effectively extract key features, helping the model capture richer and deeper global information in the image. Experimental results on the tree crown dataset demonstrate that YOLOv8-FF outperforms several recent peer competitors, making it a promising tool for accurate and efficient tree crown instance segmentation.https://ieeexplore.ieee.org/document/11024197/Instance segmentationremote sensingtree crownstree speciesYOLOv8 |
| spellingShingle | Ziyi Sun Bing Xue Mengjie Zhang Jan Schindler Feature Fusion to Improve YOLOv8 for Segmenting and Classifying Aerial Images of Tree Crowns IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Instance segmentation remote sensing tree crowns tree species YOLOv8 |
| title | Feature Fusion to Improve YOLOv8 for Segmenting and Classifying Aerial Images of Tree Crowns |
| title_full | Feature Fusion to Improve YOLOv8 for Segmenting and Classifying Aerial Images of Tree Crowns |
| title_fullStr | Feature Fusion to Improve YOLOv8 for Segmenting and Classifying Aerial Images of Tree Crowns |
| title_full_unstemmed | Feature Fusion to Improve YOLOv8 for Segmenting and Classifying Aerial Images of Tree Crowns |
| title_short | Feature Fusion to Improve YOLOv8 for Segmenting and Classifying Aerial Images of Tree Crowns |
| title_sort | feature fusion to improve yolov8 for segmenting and classifying aerial images of tree crowns |
| topic | Instance segmentation remote sensing tree crowns tree species YOLOv8 |
| url | https://ieeexplore.ieee.org/document/11024197/ |
| work_keys_str_mv | AT ziyisun featurefusiontoimproveyolov8forsegmentingandclassifyingaerialimagesoftreecrowns AT bingxue featurefusiontoimproveyolov8forsegmentingandclassifyingaerialimagesoftreecrowns AT mengjiezhang featurefusiontoimproveyolov8forsegmentingandclassifyingaerialimagesoftreecrowns AT janschindler featurefusiontoimproveyolov8forsegmentingandclassifyingaerialimagesoftreecrowns |