Graph-Based Active Learning for Surface Water and Sediment Detection in Multispectral Images
- Resource Type
- Conference
- Authors
- Chen, Bohan; Miller, Kevin; Bertozzi, Andrea L.; Schwenk, Jon
- Source
- IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium Geoscience and Remote Sensing Symposium, IGARSS 2023 - 2023 IEEE International. :5431-5434 Jul, 2023
- Subject
- Aerospace
Components, Circuits, Devices and Systems
Fields, Waves and Electromagnetics
Geoscience
Signal Processing and Analysis
Training
Support vector machines
Radio frequency
Pipelines
Training data
Rivers
Sensors
Remote Sensing
Surface Water Detection
Graph Learning
Active Learning
- Language
- ISSN
- 2153-7003
We develop a graph active learning pipeline (GAP) to detect surface water and in-river sediment pixels in satellite images. The active learning approach is applied within the training process to optimally select specific pixels to generate a hand-labeled training set. Our method obtains higher accuracy with far fewer training pixels than both standard and deep learning models. According to our experiments, our GAP trained on a set of 3270 pixels reaches a better accuracy than the neural network method trained on 2.1 million pixels.