Crater detection based on marked point processes

Giulia Troglio*, Jon A. Benediktsson, Gabriele Moser, Sebastiano B. Serpico

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)


A novel automatic method is developed for the detection of features in planetary images. Although many automatic feature extraction methods have been proposed for for remote sensing images of the Earth, these methods are typically unfeasible for planetary data that generally present low contrast and uneven illumination characteristics. Here, a novel technique for crater detection, based on a marked point process, is proposed. The main idea behind marked point processes is to model objects within a stochastic framework: They provide a powerful and methodologically rigorous framework to efficiently map and detect objects and structures in an image with an excellent robustness to noise. These methods are new and promising: They represent the last frontier of the stochastic image modeling. They have been used in different areas of the terrestrial remote sensing, but have not been applied to planetary image analysis yet. The proposed method for crater detection has many other areas applications. One such application area is image registration by matching the extracted features.

Original languageEnglish
Title of host publication2010 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010
Number of pages4
Publication statusPublished - 2010
Event2010 30th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010 - Honolulu, HI, United States
Duration: 25 Jul 201030 Jul 2010

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)


Conference2010 30th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010
Country/TerritoryUnited States
CityHonolulu, HI


Dive into the research topics of 'Crater detection based on marked point processes'. Together they form a unique fingerprint.

Cite this