PNTM-CNN: an approach for saddle-point extraction integrating positive–negative terrain method and multiscale fusion CNN model
Saddle-point extraction is essential for accurately identifying topographic features and landforms and conducting geomorphological mapping. However, the widely used positive–negative terrain method (PNTM) is often plagued by a substantial number of false saddle points, a prevalent issue in many extr...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-08-01
|
| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2545583 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849224264119484416 |
|---|---|
| author | Zhe Zhou Hao Wu Zhenyu Zhang Bo Kong Min Yang Tinghua Ai Huafei Yu |
| author_facet | Zhe Zhou Hao Wu Zhenyu Zhang Bo Kong Min Yang Tinghua Ai Huafei Yu |
| author_sort | Zhe Zhou |
| collection | DOAJ |
| description | Saddle-point extraction is essential for accurately identifying topographic features and landforms and conducting geomorphological mapping. However, the widely used positive–negative terrain method (PNTM) is often plagued by a substantial number of false saddle points, a prevalent issue in many extraction techniques. To address this challenge, this study presents a novel model that combines the PNTM with a convolutional neural network (CNN) called PNTM-CNN. In this approach, candidate saddle points are first identified using the PNTM and then refined using a CNN that integrates multiscale topographic features. The experimental results indicate that the PNTM-CNN model, which leverages four scales of features (elevation, aspect, curvature, slope, and hillshade), effectively reduces the occurrence of false saddle points, achieving a precision of 89%, a recall of 83%, and an F1 score of 85%. This performance significantly exceeds that of the traditional moving window analysis and topological association methods. Although the automation level of the PNTM-CNN model requires improvement, the integration of deep learning methods offers new insights for addressing complex topographic feature extraction challenges and shows a promising application potential. |
| format | Article |
| id | doaj-art-0219c19f79eb43bf8a8fe5463edcf862 |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-0219c19f79eb43bf8a8fe5463edcf8622025-08-25T11:28:18ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2545583PNTM-CNN: an approach for saddle-point extraction integrating positive–negative terrain method and multiscale fusion CNN modelZhe Zhou0Hao Wu1Zhenyu Zhang2Bo Kong3Min Yang4Tinghua Ai5Huafei Yu6School of Resource and Environmental Sciences, Wuhan University, Wuhan, People’s Republic of ChinaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan, People’s Republic of ChinaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan, People’s Republic of ChinaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan, People’s Republic of ChinaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan, People’s Republic of ChinaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan, People’s Republic of ChinaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan, People’s Republic of ChinaSaddle-point extraction is essential for accurately identifying topographic features and landforms and conducting geomorphological mapping. However, the widely used positive–negative terrain method (PNTM) is often plagued by a substantial number of false saddle points, a prevalent issue in many extraction techniques. To address this challenge, this study presents a novel model that combines the PNTM with a convolutional neural network (CNN) called PNTM-CNN. In this approach, candidate saddle points are first identified using the PNTM and then refined using a CNN that integrates multiscale topographic features. The experimental results indicate that the PNTM-CNN model, which leverages four scales of features (elevation, aspect, curvature, slope, and hillshade), effectively reduces the occurrence of false saddle points, achieving a precision of 89%, a recall of 83%, and an F1 score of 85%. This performance significantly exceeds that of the traditional moving window analysis and topological association methods. Although the automation level of the PNTM-CNN model requires improvement, the integration of deep learning methods offers new insights for addressing complex topographic feature extraction challenges and shows a promising application potential.https://www.tandfonline.com/doi/10.1080/17538947.2025.2545583Saddle pointspositive–negative terrain methodmultiscale fusionconvolutional neural networks |
| spellingShingle | Zhe Zhou Hao Wu Zhenyu Zhang Bo Kong Min Yang Tinghua Ai Huafei Yu PNTM-CNN: an approach for saddle-point extraction integrating positive–negative terrain method and multiscale fusion CNN model International Journal of Digital Earth Saddle points positive–negative terrain method multiscale fusion convolutional neural networks |
| title | PNTM-CNN: an approach for saddle-point extraction integrating positive–negative terrain method and multiscale fusion CNN model |
| title_full | PNTM-CNN: an approach for saddle-point extraction integrating positive–negative terrain method and multiscale fusion CNN model |
| title_fullStr | PNTM-CNN: an approach for saddle-point extraction integrating positive–negative terrain method and multiscale fusion CNN model |
| title_full_unstemmed | PNTM-CNN: an approach for saddle-point extraction integrating positive–negative terrain method and multiscale fusion CNN model |
| title_short | PNTM-CNN: an approach for saddle-point extraction integrating positive–negative terrain method and multiscale fusion CNN model |
| title_sort | pntm cnn an approach for saddle point extraction integrating positive negative terrain method and multiscale fusion cnn model |
| topic | Saddle points positive–negative terrain method multiscale fusion convolutional neural networks |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2545583 |
| work_keys_str_mv | AT zhezhou pntmcnnanapproachforsaddlepointextractionintegratingpositivenegativeterrainmethodandmultiscalefusioncnnmodel AT haowu pntmcnnanapproachforsaddlepointextractionintegratingpositivenegativeterrainmethodandmultiscalefusioncnnmodel AT zhenyuzhang pntmcnnanapproachforsaddlepointextractionintegratingpositivenegativeterrainmethodandmultiscalefusioncnnmodel AT bokong pntmcnnanapproachforsaddlepointextractionintegratingpositivenegativeterrainmethodandmultiscalefusioncnnmodel AT minyang pntmcnnanapproachforsaddlepointextractionintegratingpositivenegativeterrainmethodandmultiscalefusioncnnmodel AT tinghuaai pntmcnnanapproachforsaddlepointextractionintegratingpositivenegativeterrainmethodandmultiscalefusioncnnmodel AT huafeiyu pntmcnnanapproachforsaddlepointextractionintegratingpositivenegativeterrainmethodandmultiscalefusioncnnmodel |