Learning stochastic geometry models and convolutional neural networks. Application to multiple object detection in aerospatial data sets
- Authors
- Publication Date
- Dec 20, 2023
- Source
- HAL-Descartes
- Keywords
- Language
- English
- License
- Unknown
- External links
Abstract
Unmanned aerial vehicles and low-orbit satellites, including CubeSats, are increasingly used for wide-area surveillance, generating substantial data for processing. Satellite imagery acquisition is susceptible to atmospheric disruptions, occlusions, and limited resolution, resulting in limited visual data for small object detection. However, the objects of interest (e.g., small vehicles) are unevenly distributed in the image: there are some priors on the structure of the configurations.In recent years, convolutional neural network (CNN) models have excelled at extracting information from images, especially texture details. Yet, modeling object interactions requires a significant increase in model complexity and parameters. CNN models generally treat interaction as a post-processing step.In contrast, point processes aim to simultaneously model each point's likelihood in relation to the image (data term) and their interactions (prior term). Most point process models rely on contrast measures (foreground vs. background) for their data terms, which work well with clearly contrasted objects and minimal background clutter. However, small vehicles in satellite images exhibit varying contrast levels and a diverse range of background and false alarm objects.In this PhD thesis, we propose harnessing CNN models information extraction abilities in combination with point process interaction models, using CNN outputs as data terms. Additionally, we introduce a unified method for estimating point process model parameters. Our model demonstrates excellent performance on multiple remote sensing datasets, providing geometric regularization and enhanced noise robustness, all with a minimal parameter footprint.
