Using an Area-Weighted Loss Function to Address Class Imbalance in Deep Learning-Based Mapping of Small Water Bodies in a Low-Latitude Region

Using an Area-Weighted Loss Function to Address Class Imbalance in Deep Learning-Based Mapping of Small Water Bodies in a Low-Latitude Region

Recent advances in very high resolution PlanetScope imagery and deep-learning techniques have enabled effective mapping of small water bodies (SWBs), including ponds and ditches. SWBs typically occupy a minor proportion of remote-sensing imagery. This creates significant class imbalance that introdu...

Full description

Saved in:
Bibliographic Details
Main Authors: Pu Zhou, Giles Foody, Yihang Zhang, Yalan Wang, Xia Wang, Sisi Li, Laiyin Shen, Yun Du, Xiaodong Li
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Remote Sensing
Subjects:
PlanetScope
deep learning
loss function
small water bodies
class imbalance problem
area-based weighted binary cross-entropy (AWBCE)
Online Access:https://www.mdpi.com/2072-4292/17/11/1868
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://www.mdpi.com/2072-4292/17/11/1868

Similar Items