In Europe, water levels in wetlands are widely controlled by environmental managers and farmers. However, the influence of these management practices on hydrodynamics and biodiversity remains poorly understood. This study assesses advantages of using radar data from the recently launched Sentinel-1A satellite to monitor hydrological dynamics of the Poitevin marshland in western France. We analyze a time series of 14 radar images acquired in VV and HV polarizations from December 2014 to May 2015 with a 12-day time step. Both polarizations are used with a hysteresis thresholding algorithm which uses both spatial and temporal information to distinguish open water, flooded vegetation and non-flooded grassland. Classification results are compared to in situ piezometric measurements combined with a Digital Terrain Model derived from LiDAR data. Results reveal that open water is successfully detected, whereas flooded grasslands with emergent vegetation and fine-grained patterns are detected with moderate accuracy. Five hydrological regimes are derived from the flood duration and mapped. Analysis of time steps in the time series shows that decreased temporal repetitivity induces significant differences in estimates of flood duration. These results illustrate the great potential to monitor variations in seasonal floods with the high temporal frequency of Sentinel-1A acquisitions.

A new method for local texture characterization in very high resolution (VHR) multispectral imagery is proposed based on a pointwise approach embedded into a graph model. Due to the fact that increasing the spatial resolution of satellite sensors leads to the lack of stationarity hypothesis in optical images, a pointwise approach based on a set of interest pixels only, not on the whole image pixels, seems to be relevant. Beside that no stationary condition is required, this approach could also provide the ability to deal with huge-size data as in case of VHR multispectral images. In this paper, our motivation is to exploit the radiometric, spectral as well as spatial information of characteristic pixels to describe textural features from a multispectral image. Then, a weighted graph is constructed to link these feature points based on the similarity between their previous pointwise-based descriptors. Finally, textural features can be characterized and extracted from the spectral domain of this graph. In order to evaluate the performance of the proposed method, a texture-based classification algorithm is implemented. Here, we propose to investigate both the spectral graph clustering and the spectral graph wavelet transform approaches for an unsupervised classification. Experimental results show the effectiveness of our method in terms of classification precision as well as low complexity requirement.

A novel efficient method for content-based image retrieval (CBIR) is developed in this paper using both texture and color features. Our motivation is to represent and characterize an input image by a set of local descriptors extracted from characteristic points (i.e., keypoints) within the image. Then, dissimilarity measure between images is calculated based on the geometric distance between the topological feature spaces (i.e., manifolds) formed by the sets of local descriptors generated from each image of the database. In this work, we propose to extract and use the local extrema pixels as our feature points. Then, the so-called local extrema-based descriptor (LED) is generated for each keypoint by integrating all color, spatial as well as gradient information captured by its nearest local extrema. Hence, each image is encoded by an LED feature point cloud and Riemannian distances between these point clouds enable us to tackle CBIR. Experiments performed on several color texture databases including Vistex, STex, color Brodazt, USPtex and Outex TC-00013 using the proposed approach provide very efficient and competitive results compared to the state-of-the-art methods.

Indonesia is particularly vulnerable to oil spill since Indonesian waters, which account for 80% of its territory, involve a very active maritime traffic and a large number of onshore and offshore oil platforms. Within the framework of Infrastructure Development Space Oceanography project, we address the operational synthetic aperture radar (SAR) based monitoring of oil spill in Indonesian waters from space. This work focuses on the assimilation of SAR observations into a 2-D Lagrangian trajectory model. The simulated surface oil trajectories are compared to the satellite observations in subsequent forecast cycles for veracity testing by estimating best leakage parameters of detected oil spills. As a case study, we consider a large oil spill event that took place in Indonesian waters in Fall 2009, referred to as the Montara oil spill. A novel methodology has been developed, which combines SAR-based oil spill detection using a Lagrangian analysis and numerical tools. Our model relies on four key parameters: wind-related and current-related drift parameters, and the origin and the duration of the oil leakage. Given an SAR-derived oil spill detection, a numerical inversion is used to optimize these model parameters, so that the simulated drift matches the SAR-derived observation. The demonstration of the relevance of the proposed scheme for the Montara oil spill and further discussion on operational interest for satellite-based oil spill monitoring can be useful in rapid response system in Indonesia.