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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Forests |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4907/14/7/1509 |
| _version_ | 1797589221691621376 |
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| author | Hanzhao Wang Chunhua Hu Ranyang Zhang Weijie Qian |
| author_facet | Hanzhao Wang Chunhua Hu Ranyang Zhang Weijie Qian |
| author_sort | Hanzhao Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T01:03:26Z |
| format | Article |
| id | doaj.art-24cc30ceafb04596bd8aee3e55bbfe9e |
| institution | Directory Open Access Journal |
| issn | 1999-4907 |
| language | English |
| last_indexed | 2024-03-11T01:03:26Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forests |
| spelling | doaj.art-24cc30ceafb04596bd8aee3e55bbfe9e2023-11-18T19:25:23ZengMDPI AGForests1999-49072023-07-01147150910.3390/f14071509SegForest: A Segmentation Model for Remote Sensing ImagesHanzhao Wang0Chunhua Hu1Ranyang Zhang2Weijie Qian3College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, ChinaThe 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.https://www.mdpi.com/1999-4907/14/7/1509segmentationremote sensingforest monitoringdeep learningfeature fusionforest image segmentation |
| spellingShingle | Hanzhao Wang Chunhua Hu Ranyang Zhang Weijie Qian SegForest: A Segmentation Model for Remote Sensing Images Forests segmentation remote sensing forest monitoring deep learning feature fusion forest image segmentation |
| title | SegForest: A Segmentation Model for Remote Sensing Images |
| title_full | SegForest: A Segmentation Model for Remote Sensing Images |
| title_fullStr | SegForest: A Segmentation Model for Remote Sensing Images |
| title_full_unstemmed | SegForest: A Segmentation Model for Remote Sensing Images |
| title_short | SegForest: A Segmentation Model for Remote Sensing Images |
| title_sort | segforest a segmentation model for remote sensing images |
| topic | segmentation remote sensing forest monitoring deep learning feature fusion forest image segmentation |
| url | https://www.mdpi.com/1999-4907/14/7/1509 |
| work_keys_str_mv | AT hanzhaowang segforestasegmentationmodelforremotesensingimages AT chunhuahu segforestasegmentationmodelforremotesensingimages AT ranyangzhang segforestasegmentationmodelforremotesensingimages AT weijieqian segforestasegmentationmodelforremotesensingimages |