Skip to Main content Skip to Navigation
New interface
Conference papers

CNN-based energy learning for MPP object detection in satellite images

Abstract : This article presents a method combining marked point processes and convolutional neural networks in order to detect small objects in optical satellite images. In this setting, objects are scattered densely: the energy based formulation of a point process allows us to factor in priors to account for object interactions. Classical marked point process approaches use contrast measures to account for object location and shape, which prove limited in complex scenes. Instead, we use convolutional neural networks to learn energy terms that are more resilient to object and context visual diversity. Finally we present a procedure to learn the relative weights of prior and likelihood terms. We test our approach on remote sensing images and compare it to contrast based approaches. Code is available at https://github.com/Ayana-Inria/MPP CNN RS object detection.
Complete list of metadata

https://hal.inria.fr/hal-03715331
Contributor : Jules Mabon Connect in order to contact the contributor
Submitted on : Wednesday, July 6, 2022 - 12:09:18 PM
Last modification on : Tuesday, August 2, 2022 - 4:06:40 PM

File

MLSP22_Mabon_AuthorVersion.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-03715331, version 1

Citation

Jules Mabon, Mathias Ortner, Josiane Zerubia. CNN-based energy learning for MPP object detection in satellite images. MLSP 2022 - IEEE International workshop on machine learning for signal processing, Aug 2022, Xi'an, China. ⟨hal-03715331⟩

Share

Metrics

Record views

97

Files downloads

21