Bertrand Augereau

Bio: Bertrand Augereau is an academic researcher from University of Poitiers. The author has contributed to research in topics: Color image & Wavelet. The author has an hindex of 9, co-authored 44 publications receiving 305 citations. Previous affiliations of Bertrand Augereau include Centre national de la recherche scientifique.

A Bandelet-Based Inpainting Technique for Clouds Removal From Remotely Sensed Images

Abstract: It is well known that removing cloud-contaminated portions of a remotely sensed image and then filling in the missing data represent an important photo editing cumbersome task. In this paper, an efficient inpainting technique for the reconstruction of areas obscured by clouds or cloud shadows in remotely sensed images is presented. This technique is based on the Bandelet transform and the multiscale geometrical grouping. It consists of two steps. In the first step, the curves of geometric flow of different zones of the image are determined by using the Bandelet transform with multiscale grouping. This step allows an efficient representation of the multiscale geometry of the image's structures. Having well represented this geometry, the information inside the cloud-contaminated zone is synthesized by propagating the geometrical flow curves inside that zone. This step is accomplished by minimizing a functional whose role is to reconstruct the missing or cloud contaminated zone independently of the size and topology of the inpainting domain. The proposed technique is illustrated with some examples on processing aerial images. The obtained results are compared with those obtained by other clouds removal techniques.

IRIM at TRECVID2009: High Level Feature Extraction

Abstract: The IRIM group is a consortium of French teams working on Multimedia Indexing and Retrieval. This paper describes our participation to the TRECVID 2009 High Level Features detection task. We evaluated a large number of different descriptors (on TRECVID 2008 data) and tried different fusion strategies, in particular hierarchical fusion and genetic fusion. The best IRIM run has a Mean Inferred Average Precision of 0.1220, which is significantly above TRECVID 2009 HLF detection task median performance. We found that fusion of the classification scores from different classifier types improves the performance and that even with a quite low individual performance, audio descriptors can help.

Rushes summarization by IRIM consortium: redundancy removal and multi-feature fusion

Abstract: In this paper, we present the first participation of a consortium of French laboratories, IRIM, to the TRECVID 2008 BBC Rushes Summarization task. Our approach resorts to video skimming. We propose two methods to reduce redundancy, as rushes include several takes of scenes. We also take into account low and mid-level semantic features in an ad-hoc fusion method in order to retain only significant content

Missing Information Reconstruction of Remote Sensing Data: A Technical Review

Abstract: Because of sensor malfunction and poor atmospheric conditions, there is usually a great deal of missing information in optical remote sensing data, which reduces the usage rate and hinders the follow-up interpretation. In the past decades, missing information reconstruction of remote sensing data has become an active research field, and a large number of algorithms have been developed. However, to the best of our knowledge, there has not, to date, been a study that has been aimed at expatiating and summarizing the current situation. This is therefore our motivation in this review. This paper provides an introduction to the principles and theories of missing information reconstruction of remote sensing data. We classify the established and emerging algorithms into four main categories, followed by a comprehensive comparison of them from both experimental and theoretical perspectives. This paper also predicts the promising future research directions.

Recovering Quantitative Remote Sensing Products Contaminated by Thick Clouds and Shadows Using Multitemporal Dictionary Learning

Abstract: With regard to quantitative remote sensing products in the visible and infrared ranges, thick clouds and accompanying shadows are an inevitable source of noise. Due to the absence of adequate supporting information from the data themselves, it is a formidable challenge to accurately restore the surficial information underlying large-scale clouds. In this paper, dictionary learning is expanded into the multitemporal recovery of quantitative data contaminated by thick clouds and shadows. This paper proposes two multitemporal dictionary learning algorithms, expanding on their KSVD and Bayesian counterparts. In order to make better use of the temporal correlations, the expanded KSVD algorithm seeks an optimized temporal path, and the expanded Bayesian method adaptively weights the temporal correlations. In the experiments, the proposed algorithms are applied to a reflectance product and a land surface temperature product, and the respective advantages of the two algorithms are investigated. The results show that, from both the qualitative visual effect and the quantitative objective evaluation, the proposed methods are effective.

Automatic Road Pavement Assessment with Image Processing: Review and Comparison

Abstract: In the field of noninvasive sensing techniques for civil infrastructures monitoring, this paper addresses the problem of crack detection, in the surface of the French national roads, by automatic analysis of optical images. The first contribution is a state of the art of the image-processing tools applied to civil engineering. The second contribution is about fine-defect detection in pavement surface. The approach is based on a multi-scale extraction and a Markovian segmentation. Third, an evaluation and comparison protocol which has been designed for evaluating this difficult task—the road pavement crack detection—is introduced. Finally, the proposed method is validated, analysed, and compared to a detection approach based on morphological tools.

Cloud Removal From Multitemporal Satellite Images Using Information Cloning

Abstract: A cloud removal approach based on information cloning is introduced. The approach removes cloud-contaminated portions of a satellite image and then reconstructs the information of missing data utilizing temporal correlation of multitemporal images. The basic idea is to clone information from cloud-free patches to their corresponding cloud-contaminated patches under the assumption that land covers change insignificantly over a short period of time. The patch-based information reconstruction is mathematically formulated as a Poisson equation and solved using a global optimization process. Thus, the proposed approach can potentially yield better results in terms of radiometric accuracy and consistency compared with related approaches. Some experimental analyses on sequences of images acquired by the Landsat-7 Enhanced Thematic Mapper Plus sensor are conducted. The experimental results show that the proposed approach can process large clouds in a heterogeneous landscape, which is difficult for cloud removal approaches. In addition, quantitative and qualitative analyses on simulated data with different cloud contamination conditions are conducted using quality index and visual inspection, respectively, to evaluate the performance of the proposed approach.

Cloud removal for remotely sensed images by similar pixel replacement guided with a spatio-temporal MRF model

Abstract: Cloud cover is generally present in remotely sensed images, which limits the potential of the images for ground information extraction. Therefore, removing the clouds and recovering the ground information for the cloud-contaminated images is often necessary in many applications. In this paper, an effective method based on similar pixel replacement is developed to solve this task. A missing pixel is filled using an appropriate similar pixel within the remaining region of the target image. A multitemporal image is used as the guidance to locate the similar pixels. A pixel-offset based spatio-temporal Markov random fields (MRF) global function is built to find the most suitable similar pixel. The proposed method was tested on MODIS and Landsat images and their land surface temperature products, and the experiments verify that the proposed method can achieve highly accurate results and is effective at dealing with the obvious atmospheric and seasonal differences between multitemporal images.