MonoCrown for Crown-Level Tree Species Semantic Segmentation in Heterogeneous Forests Using UAV RGB Imagery.
Saved in:
| Title: | MonoCrown for Crown-Level Tree Species Semantic Segmentation in Heterogeneous Forests Using UAV RGB Imagery. |
|---|---|
| Authors: | Wen, Linzhi1 (AUTHOR), Chen, Guangsheng1 (AUTHOR) kjc_chen@nefu.edu.cn |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 9, p1338. 25p. |
| Subjects: | Crowns (Botany), Depth maps (Digital image processing), Mixed forests, Remote sensing, Forest mapping |
| Abstract: | Highlights: What are the main findings? MCrown enables crown-level tree species semantic segmentation from UAV RGB imagery by injecting frozen monocular-depth priors as geometric guidance. Cross-window global–local context with bidirectional cross-modal attention reduces inter-species confusion and sharpens crown boundaries in heterogeneous forests. What are the implications of the main findings? Achieves consistent gains on an in-house ten-class UAV benchmark and public datasets under both dense and sparse annotations. Provides a low-cost, deployable alternative to multispectral/LiDAR pipelines for large-area, fine-grained forest mapping. Crown-level tree species semantic segmentation enables fine-grained forest inventory and management. Current high-precision tree species classification typically relies on multi-source remote sensing data, the acquisition and processing of which remain costly for large-area applications, making low-cost unmanned aerial vehicle (UAV) RGB imagery an attractive option for large-scale forest mapping. However, in heterogeneous forests, complex canopy structures and the limited spectral discriminability of low-cost UAV RGB imagery make 2D appearance cues alone insufficient for reliable species discrimination, crown delineation, and accurate separation of adjacent crowns. This often leads to inter-class confusion, blurred crown boundaries, and poor recognition of small crowns. To address these limitations, this paper proposes MonoCrown (MCrown), which strengthens geometric and contextual representation for distinguishing visually similar species and delineating crowns from single-temporal UAV RGB imagery. To compensate for the insufficiency of appearance cues, MCrown introduces monocular depth inferred offline from the same RGB image as a frozen geometric prior, and integrates cross-window global–local attention (CW-GLA), bidirectional cross-modal attention (BiCoAttn), and depth-adaptive injection (DAI) to capture long-range dependencies and promote complementary use of appearance and geometric features, especially for small crowns with similar visual patterns in complex scenes. To validate the method's effectiveness, a crown-level UAV RGB dataset covering approximately 40 km2 was constructed. Systematic comparative experiments were conducted on the proposed dataset and on public benchmarks, supporting the effectiveness of the proposed approach across ten dominant classes, especially for small crowns and visually similar categories. Its mean Intersection over Union (mIoU) and overall accuracy (OA) reached 74.1% and 87.3%, respectively. The method achieves high-precision crown-level tree species semantic segmentation using single-temporal UAV RGB as the sole acquired modality, while monocular depth inferred from the same RGB image serves only as a frozen geometric prior, without requiring multispectral, multi-temporal, or active-sensor acquisitions. This offers a practical solution for crown-level tree species mapping in heterogeneous forests. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
Be the first to leave a comment!
