Geography
Document Type
Article
Abstract
Accuracy assessment is critical for evaluating map quality and improving algorithms for target detection in remotely sensed images. Traditional methods for assessing a target-detection task were often performed by estimating the relative pixels of correct and incorrect identifications (e.g., F1 score). However, this approach only considers the pixel-by-pixel agreement and ignores important aspects of positional errors and target object quality. To provide comprehensive diagnostic information, we propose a novel edge-based accuracy assessment framework. This framework firstly applied a newly proposed assessment tool, Dynamic Epsilon-band Accuracy Function (DEAF), which is a linear fitting function from a series of edge accuracies associated with expanded buffers on the object edges, and then extracted the level-off and the middle point from the DEAF curve respectively to derive edge-based accuracy metrics and to assess thematic and positional error components. We presented an experiment on synthetic maps to reveal that the new edge-based accuracy metrics better reflects under- and over-segmentation errors compared to traditional measurements (e.g., F1 score, over-/under-segmentation indices). Moreover, we applied the new framework for two practical target-detection tasks, agricultural fields and building rooftops. The results demonstrate the two innovative aspects of the edge-based framework, which are that it provides: 1) a more rigorous assessment of object quality, and 2) error attribution for both thematic and positional errors. The proposed framework therefore provides a new capability for assessing the accuracy of target detection maps, which is of vital importance for evaluating and improving state-of-the-art remote sensing products. © 2026 The Authors
Publication Title
International Journal of Applied Earth Observation and Geoinformation
Publication Date
8-2026
Volume
152
ISSN
1569-8432
DOI
10.1016/j.jag.2026.105458
Keywords
accuracy assessment, object-based, remote sensing, target detection
Repository Citation
Zhang, Yingfan; Hou, Weiyi; Estes, Lyndon; Lu, Rui; Zhu, Shijie; Zhang, Aiyin; Shi, Zhou; and Ye, Su, "A novel edge-based accuracy assessment framework for target detection from remotely sensed images" (2026). Geography. 1060.
https://commons.clarku.edu/faculty_geography/1060
Cross Post Location
Student Publications
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright Conditions
Chakraborty, J., & Esposito, A. (2026). Can a Critical Reflection-Based Intervention Effectively Teach Intersectional Awareness of Gendered Racism? An Empirical Study with College Students. Race and Social Problems, 18(3), 47. https://doi.org/10.1007/s12552-026-09511-2
