The domain adaptation of satellite images has recently gained increasing attention to overcome the limited generalization abilities of machine learning models when segmenting large-scale satellite images. Most of the existing approaches seek for adapting the model from one domain to another. However, such single-source and single-target setting prevents the methods from being scalable solutions since, nowadays, multiple sources and target domains having different data distributions are usually available. Besides, the continuous proliferation of satellite images necessitates the classifiers to adapt to continuously increasing data. We propose a novel approach, coined DAugNet, for unsupervised, multisource, multitarget, and life-long domain adaptation of satellite images. It consists of a classifier and a data augmentor. The data augmentor, which is a shallow network, is able to perform style transfer between multiple satellite images in an unsupervised manner, even when new data are added over time. In each training iteration, it provides the classifier with diversified data, which makes the classifier robust to large data distribution difference between the domains. Our extensive experiments prove that DAugNet significantly better generalizes to new geographic locations than the existing approaches.

Morphological hierarchies form a popular framework aiming at emphasizing the multiscale structure of digital image by performing an unsupervised spatial partitioning of the data. These hierarchies have been recently extended to cope with image sequences, and different strategies have been proposed to allow their construction from spatio-temporal data. In this paper, we compare these hierarchical representation strategies for image sequences according to their structural properties. We introduce a projection method to make these representations comparable. Furthermore, we extend one of these recent strategies in order to obtain more efficient hierarchical representations for image sequences. Experiments were conducted on both synthetic and real datasets, the latter being made of satellite image time series. We show that building one hierarchy by using spatial and temporal information together is more efficient comparing to other existing strategies.

This article shows the capacity a two-dimension spatio-temporal cross-correlation method for estimating wave velocity and inverse bathymetry. It is presented and applied to the image sequence of the submetric Pleiades satellite mission (Airbus/CNES) which allows to acquire a sequence of images at a regional scale (100 km2). A good agreement is found with the bathymetry obtained during the COMBI2017 Capbreton experiment (correlation of 0.8, RMSE=1.4 m). A saturation of the depth estimate is found for depths greater than 35 m, mainly in a deep canyon just off the coast. The results show that the accuracy increases with the number of images in the sequence. A new possibility of high-frequency signal reconstruction offered by the spatio-temporal correlation method is investigated, using dense spatial and sparse temporal data. Despite their noisy nature, newly available time-updated satellite bathymetries can be used to understand coastal evolution at several scales and improve risk mitigation strategies through modelling.

Wetlands, very sensitive and valuable ecosystem can be monitored in terms of water surfaces dynamics as well as vegetation characterisation and monitoring exploiting satellite data. The synergy between the recently launched Sentinel1 and Sentinel2 satellites have been investigated over the Poyang and Anhui lakes in PR China. Results highlight the gain in terms of operationality with a very high revisit exploiting the two systems, as well as for a thematic point of view with no was not yet reach at this resolution for water bodies and terrestrial, floating and submerged vegetation mapping and monitoring.

In recent years, remote sensing of the Earth surface using images acquired from aircraft or satellites has gained a lot of attention. The acquisition technology has been evolving fast and, as a consequence, many different kinds of sensors (e.g., optical, radar, multispectral, and hyperspectral) are now available to capture different features of the observed scene. One of the main objectives of remote sensing is to monitor changes on the Earth surface. Change detection has been thoroughly studied in the case of images acquired by the same sensors (mainly optical or radar sensors). However, due to the diversity and complementarity of the images, change detection between images acquired with different kinds of sensors (sometimes referred to as heterogeneous sensors) is clearly an interesting problem. A statistical model and a change detection strategy were recently introduced in [J. Prendes, M. Chabert, F. Pascal, A. Giros, and J.-Y. Tourneret, Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Florence, Italy, 2014; IEEE Trans. Image Process., 24 (2015), pp. 799--812] to deal with images captured by heterogeneous sensors. The main idea of the suggested strategy was to model the objects contained in an analysis window by mixtures of distributions. The manifold defined by these mixtures was then learned using training data belonging to unchanged areas. The changes were finally detected by thresholding an appropriate distance to the estimated manifold. This paper goes a step further by introducing a Bayesian nonparametric framework allowing us to deal with an unknown number of objects in analysis windows without specifying an upper bound for this number. A Markov random field is also introduced to account for the spatial correlation between neighboring pixels. The proposed change detector is validated using different sets of synthetic and real images (including pairs of optical images and pairs of optical and radar images) showing a significant improvement when compared to existing algorithms.

This paper introduces a Bayesian non parametric (BNP) model associated with a Markov random field (MRF) for detecting changes between remote sensing images acquired by homogeneous or heterogeneous sensors. The proposed model is built for an analysis window which takes advantage of the spatial information via an MRF. The model does not require any a priori knowledge about the number of objects contained in the window thanks to the BNP framework. The change detection strategy can be divided into two steps. First, the segmentation of the two images is performed using a region based approach. Second, the joint statistical properties of the objects in the two images allows an appropriate manifold to be defined. This manifold describes the relationships between the different sensor responses to the observed scene and can be learnt from a training unchanged area. It allows us to build a similarity measure between the images that can be used in many applications such as change detection or image registration. Simulation results conducted on synthetic and real optical and synthetic aperture radar (SAR) images show the efficiency of the proposed method for change detection.

Remote sensing images are commonly used to monitor the earth surface evolution. This surveillance can be conducted by detecting changes between images acquired at different times and possibly by different kinds of sensors. A representative case is when an optical image of a given area is available and a new image is acquired in an emergency situation (resulting from a natural disaster for instance) by a radar satellite. In such a case, images with heterogeneous properties have to be compared for change detection. This paper proposes a new approach for similarity measurement between images acquired by heterogeneous sensors. The approach exploits the considered sensor physical properties and specially the associated measurement noise models and local joint distributions. These properties are inferred through manifold learning. The resulting similarity measure has been successfully applied to detect changes between many kinds of images, including pairs of optical images and pairs of optical-radar images.

This paper introduces a new statistical model for homogeneous images acquired by the same kind of sensor (e.g., two optical images) and heterogeneous images acquired by different sensors (e.g., optical and synthetic aperture radar (SAR) images). The proposed model assumes that each image pixel is distributed according to a mixture of multi-dimensional distributions depending on the noise properties and on the transformation between the actual scene and the image intensities. The parameters of this new model can be estimated by the classical expectation-maximization algorithm. The estimated parameters are finally used to learn the relationships between the different images. This information can be used in many image processing applications, particularly those requiring a similarity measure (e.g., change detection or registration). Simulation results on synthetic and real images show the potential of the proposed model. A brief application to change detection between optical and SAR images is finally investigated.

Following Haiti earthquake in January 2010, the French Agence Nationale de la Recherche has funded a project named KAL-Haiti which aims at gathering remote sensing imagery as well as in-situ measurements and exogenous data into a knowledge base. Too often limited to the "response" phase, Earth Observation data should also be available for use by relevant agencies involved throughout the disaster management cycle, from recovery and mitigation to preparedness. The resulting geo-referenced database, seen as a shareable resource, can serve as a basis for helping the reconstruction of the country but also as a reference for scientific studies devoted to all phases of risk management. The database is freely available for scientific studies devoted to the different phases of risk management and for reconstruction tasks in Haiti. KAL-Haiti will also be enriched with new satellite images such as Pleiades, monitoring the evolution of Haiti over the next 10 years.

A visual information mining concept is proposed for spatio-temporal patterns discovery in remotely sensed image time-series (ITS). An information theory framework is adopted to first model information content. It results in the inference of a relevant directed graph characterizing ITS. Then the user conjecture is modeled via visual information representations: similarity measures between sub-graphs, which represents spatio-temporal events are derived and included in an interactive learning and probabilistic retrieval procedure of user-specific event-types. The present concept for ITS mining is demonstrated on multitemporal SPOT data.

One of the applications where remote sensing could be very useful is the management of major disasters. While remote sensing has shown its interest for recovery and inventory tasks after the crisis period, an assessment of its usefulness during the crisis period is needed. Periodic image acquisitions over any point of the Earth surface, with improved resolutions available today seem to fulfill the required specifications of a global monitoring system. Earth Observation satellites in orbit today were not designed for such a purpose. However, several initiatives have been proposed in order to use them in this kind of applications, as for instance, the International Charter Space and Major Disasters, or the CEOS Disaster Management Support Group. In this paper we discuss, based on past experiences, what are the real capabilities of present and near future satellites, which are their drawbacks and how they could be used at best for real cases of crisis management. A list of recommendations with regards to what could be improved at the system level (sensor, acquisition scheduling, ground segment data production) and the techniques for information extraction (image processing, sensor fusion), is given

With the advent of commercial satellite sensors producing images with resolutions better than 5 m., it is now possible to recognize man-made objects which were not visible at lower resolutions. In this work, an image processing chain for the detection of man-made objects in high resolution remote sensing images are presented. Detection is understood as finding the smallest rectangular area in the image containing the object. These algorithms are based on learning methods and on an example data base which contains 10 classes of objects

Multisensor image registration is needed in a large number of applications of remote sensing imagery. The accuracy achieved with usual methods (manual control points extraction, estimation of an analytical deformation model) is not satisfactory for many applications where a subpixel accuracy for each pixel of the image is needed (change detection or image fusion, for instance). Unfortunately, there are few works in the literature about the fine registration of multisensor images and even less about the extension of approaches similar to those based on fine correlation for the case of monomodal imagery. In this paper, we analyze the problem of the automatic multisensor image registration and we introduce similarity measures which can replace the correlation coefficient in a deformation map estimation scheme. We show an example where the deformation map between a radar image and an optical one is fully automatically estimated.

In this paper, a dynamic scene understanding concept is proposed and applied on multispectral image time series. Information mining enables the explorations and discovery of spatio temporal patterns localized in given spatio temporal windows. With this in mind, a hierarchical information representation is developed. It comprises different levels in which the data is modeled so that the relevant information is transmitted through the architecture, according to a query and to several assumptions made on the models employed. There are mainly four components: feature extraction, reduction of dimensionality, clustering and interactive exploration.