Remote Sensing for Non‐Technical Survey

This chapter describes the research activities of the Royal Military Academy on remote sensing applied to mine action. Remote sensing can be used to detect specific features that could lead to the suspicion of the presence, or absence, of mines. Work on the automatic detection of trenches and craters is presented here. Land cover can be extracted and is quite useful to help mine action. We present here a classification method based on Gabor filters. The relief of a region helps analysts to understand where mines could have been laid. Methods to be a digital terrain model from a digital surface model are explained. The special case of multi‐spectral classification is also addressed in this chapter. Discussion about data fusion is also given. Hyper‐spectral data are also addressed with a change detection method. Synthetic aperture radar data and its fusion with optical data have been studied. Radar interferometry and polarimetry are also addressed

INTERPRETATION ET RECALAGE D'IMAGES SAR POLARIMETRIQUES DE HAUTE RESOLUTION

PARIS-Télécom ParisTech (751132302) / SudocSudocFranceF

Facing the Cover-Source Mismatch on JPHide using Training-Set Design

International audienceThis short paper investigates the influence of the image processing pipeline (IPP) on the cover-source mismatch (CSM) for the popular JPHide steganographic scheme. We propose to deal with CSM by combining a forensics and a steganalysis approach. A multi-classifier is first trained to identify the IPP, and secondly a specific training set is designed to train a targeted classifier for steganal-ysis purposes. We show that the forensic step is immune to the steganographic embedding. The proposed IPP-informed steganaly-sis outperforms classical strategies based on training on a mixture of sources and we show that it can provide results close to a detector specifically trained on the appropriate source

Long Range Automatic Detection of Small Targets in Sequences of Noisy Thermal Infrared Images

In this paper, an approach to the automatic detection of vehicles at long range using sequences of thermal infrared images is presented. The vehicles in the sequences can be either moving or stationary. The sensor can also be mounted on a moving platform. The targetarea in the images is very small, typically less than 10 pixels on target. The proposed method consists of two independent parts. The first part seeks for possible targets in individual images and then merges the results for a subsequence of images. The decision for this part of the algorithm is based on temporal and spatial consistency of the targets through the considered image subsequence. The second part of the algorithm specifically focuses on finding moving objects in the scene. Clearly, as the sensor may itself be moving too, the effect of this motion on the images has to be eliminated first. This was done using a model based registration technique. The algorithm proposed in this paper was implemented and tested on a ..