Development and Training of a Neural Network Filter for Satellite Images Processing
This paper is devoted to the study of the efficiency of using neural networks for filtering satellite images. The authors propose the use of convolutional noise suppressing autoencoders in order to minimize the filtering error variance. As part of the study, the architecture of the autoencoder was d...
Saved in:
| Main Authors: | N. Andriyanov, A. Kim |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2024-12-01
|
| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://isprs-archives.copernicus.org/articles/XLVIII-2-W5-2024/9/2024/isprs-archives-XLVIII-2-W5-2024-9-2024.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Fractal Neural Network Approach for Analyzing Satellite Images
by: Volodymyr Shymanskyi, et al.
Published: (2025-12-01) -
IMPACT OF IMAGE PRE-PROCESSING ON QUALITY OF TRAINING NEURAL NETWORK FOR RECOGNITION
by: Nikita A. Lagunov, et al.
Published: (2022-05-01) -
Neural Correlation Integrated Adaptive Point Process Filtering on Population Spike Trains
by: Mingdong Li, et al.
Published: (2025-01-01) -
High-throughput mesoscopic optical imaging data processing and parsing using differential-guided filtered neural networks
by: Hong Zhang, et al.
Published: (2024-12-01) -
A Ship Detector Design Based on Deep Convolutional Neural Networks for Satellite Images
by: Ferhat Ucar, et al.
Published: (2020-02-01)