A New Multivariate Statistical Model for Change Detection in Images Acquired by Homogeneous and Heterogeneous Sensors

International audienceRemote 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 associatedmeasurement 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

Change detection for optical and radar images using a Bayesian nonparametric model coupled with a Markov random field

International audienceThis 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

Analytic Expressions for Stochastic Distances Between Relaxed Complex Wishart Distributions

The scaled complex Wishart distribution is a widely used model for multilook full polarimetric SAR data whose adequacy has been attested in the literature. Classification, segmentation, and image analysis techniques which depend on this model have been devised, and many of them employ some type of dissimilarity measure. In this paper we derive analytic expressions for four stochastic distances between relaxed scaled complex Wishart distributions in their most general form and in important particular cases. Using these distances, inequalities are obtained which lead to new ways of deriving the Bartlett and revised Wishart distances. The expressiveness of the four analytic distances is assessed with respect to the variation of parameters. Such distances are then used for deriving new tests statistics, which are proved to have asymptotic chi-square distribution. Adopting the test size as a comparison criterion, a sensitivity study is performed by means of Monte Carlo experiments suggesting that the Bhattacharyya statistic outperforms all the others. The power of the tests is also assessed. Applications to actual data illustrate the discrimination and homogeneity identification capabilities of these distances.Comment: Accepted for publication in the IEEE Transactions on Geoscience and Remote Sensing journa

Code-Aligned Autoencoders for Unsupervised Change Detection in Multimodal Remote Sensing Images

Image translation with convolutional autoencoders has recently been used as an approach to multimodal change detection (CD) in bitemporal satellite images. A main challenge is the alignment of the code spaces by reducing the contribution of change pixels to the learning of the translation function. Many existing approaches train the networks by exploiting supervised information of the change areas, which, however, is not always available. We propose to extract relational pixel information captured by domain-specific affinity matrices at the input and use this to enforce alignment of the code spaces and reduce the impact of change pixels on the learning objective. A change prior is derived in an unsupervised fashion from pixel pair affinities that are comparable across domains. To achieve code space alignment, we enforce pixels with similar affinity relations in the input domains to be correlated also in code space. We demonstrate the utility of this procedure in combination with cycle consistency. The proposed approach is compared with the state-of-the-art machine learning and deep learning algorithms. Experiments conducted on four real and representative datasets show the effectiveness of our methodology

Unsupervised Image Regression for Heterogeneous Change Detection

Change detection (CD) in heterogeneous multitemporal satellite images is an emerging and challenging topic in remote sensing. In particular, one of the main challenges is to tackle the problem in an unsupervised manner. In this paper, we propose an unsupervised framework for bitemporal heterogeneous CD based on the comparison of affinity matrices and image regression. First, our method quantifies the similarity of affinity matrices computed from colocated image patches in the two images. This is done to automatically identify pixels that are likely to be unchanged. With the identified pixels as pseudotraining data, we learn a transformation to map the first image to the domain of the other image and vice versa. Four regression methods are selected to carry out the transformation: Gaussian process regression, support vector regression, random forest regression (RFR), and a recently proposed kernel regression method called homogeneous pixel transformation. To evaluate the potentials and limitations of our framework and also the benefits and disadvantages of each regression method, we perform experiments on two real data sets. The results indicate that the comparison of the affinity matrices can already be considered a CD method by itself. However, image regression is shown to improve the results obtained by the previous step alone and produces accurate CD maps despite of the heterogeneity of the multitemporal input data. Notably, the RFR approach excels by achieving similar accuracy as the other methods, but with a significantly lower computational cost and with fast and robust tuning of hyperparameters

Crystal nucleation in adroplet based microfluidic crystallizer

The study presented in this paper deals with the determination of eflucimibe nucleation rate in a droplet based microfluidic crystallizer. The experimental device allows the storage of up to 2000 monodispersed droplets to get nucleation statistics and crystal growth rates under static conditions. Supersaturation was generated by quenching the droplets down to 273 or 293 K. To determine the nucleation kinetics of eflucimibe, the number of appearing crystals is recorded as a function of time. At low time scale, it was found that eflucimibe in the droplets containing active centers (impurities) crystallizes first and thus yields a rapid initial rate. At higher time scale, once all the droplets containing impurities have crystallized, leaving only the droplets that are free of impurities, the nucleation rate falls allowing the determination of the homogeneous nucleation rate. The crystal–solution interfacial energy found in this system σ=3.12 mJ m−2 is in good agreement with the previously published results. Using the crystalnucleation and the growth rate determined experimentally, simulations were performed using a Monte Carlo method. Even if this method correctly predicts the number of droplets that remains empty during the experiments, it was not possible to predict correctly the number of crystals per drop obtained experimentally. The relationship between the growth and nucleation rates and the resultant number of crystals per drop is likely to be complex and dependent on a number of system parameters. The failure of the model may be attributed either to an overestimation of the crystal growth rate or to an enhancement of the nucleation rate due to the presence of seed crystals