SegForest: A Segmentation Model for Remote Sensing Images

The accurate estimation of forest area is of paramount importance for carbon sequestration projects, ecotourism and ecological safety. Forest segmentation using remote sensing images is a crucial technique for estimating forest area. However, due to the complex features, such as the size, shape and...

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Bibliographic Details
Published in:Forests 2023-07, Vol.14 (7), p.1509
Main Authors: Wang, Hanzhao, Hu, Chunhua, Zhang, Ranyang, Qian, Weijie
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Carbon
Carbon sequestration
China
Climate change
Comparative analysis
Datasets
Ecosystems
Ecotourism
Entropy
Entropy (Information theory)
Estimation
Forests
Forests and forestry
Image enhancement
Image processing
Image segmentation
Land surveys
Machine learning
Measurement
Methods
Modules
Neural networks
Remote sensing
Semantics
Technology application
Unmanned aerial vehicles
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Summary:The accurate estimation of forest area is of paramount importance for carbon sequestration projects, ecotourism and ecological safety. Forest segmentation using remote sensing images is a crucial technique for estimating forest area. However, due to the complex features, such as the size, shape and color of forest plots, traditional segmentation algorithms struggle to achieve accurate segmentation. Therefore, this study proposes a remote sensing image forest segmentation model named SegForest. To enhance the model, we introduce three new modules: multi-feature fusion (MFF), multi-scale multi-decoder (MSMD) and weight-based cross entropy loss function (WBCE) in the decoder. In addition, we propose two new forest remote sensing image segmentation binary datasets: DeepGlobe-Forest and Loveda-Forest. SegForest is compared with multiple advanced segmentation algorithms on these two datasets. On the DeepGlobe-Forest dataset, SegForest achieves a mean intersection over union (mIoU) of 83.39% and a mean accuracy (mAcc) of 91.00%. On the Loveda-Forest dataset, SegForest achieves a mIoU of 73.71% and a mAcc of 85.06%. These metrics outperform other algorithms in the comparative experiments. The experimental results of this paper demonstrate that by incorporating the three proposed modules, the SegForest model has strong performance and generalization ability in forest remote sensing image segmentation tasks.
ISSN:1999-4907
1999-4907
DOI:10.3390/f14071509