Constrained Distance Based Clustering for Satellite Image Time-Series

International audienceThe advent of high-resolution instruments for time-series sampling poses added complexity for the formal definition of thematic classes in the remote sensing domain-required by supervised methods-while unsupervised methods ignore expert knowledge and intuition. Constrained clustering is becoming an increasingly popular approach in data mining because it offers a solution to these problems, however, its application in remote sensing is relatively unknown. This article addresses this divide by adapting publicly available constrained clustering implementations to use the dynamic time warping (DTW) dissimilarity measure, which is sometimes used for time-series analysis. A comparative study is presented, in which their performance is evaluated (using both DTW and Euclidean distances). It is found that adding constraints to the clustering problem results in an increase in accuracy when compared to unconstrained clustering. The output of such algorithms are homogeneous in spatially defined regions. Declarative approaches and k-Means based algorithms are simple to apply, requiring little or no choice of parameter values. Spectral methods, however, require careful tuning, which is unrealistic in a semi-supervised setting, although they offer the highest accuracy. These conclusions were drawn from two applications: crop clustering using 11 multi-spectral Landsat images non-uniformly sampled over a period of eight months in 2007; and tree-cut detection using 10 NDVI Sentinel-2 images non-uniformly sampled between 2016 and 2018

A synoptic view of solar transient evolution in the inner heliosphere using the Heliospheric Imagers on STEREO

By exploiting data from the STEREO/heliospheric imagers (HI) we extend a well-established technique developed for coronal analysis by producing time-elongation plots that reveal the nature of solar transient activity over a far more extensive region of the heliosphere than previously possible from coronagraph images. Despite the simplicity of these plots, their power in demonstrating how the plethora of ascending coronal features observed near the Sun evolve as they move antisunward is obvious. The time-elongation profile of a transient tracked by HI can, moreover, be used to establish its angle out of the plane-of-the-sky; an illustration of such analysis reveals coronal mass ejection material that can be clearly observed propagating out to distances beyond 1AU. This work confirms the value of the time-elongation format in identifying/characterising transient activity in the inner heliosphere, whilst also validating the ability of HI to continuously monitor solar ejecta out to and beyond 1A

Monitoring of Tectonic Deformation by Mining Satellite Image Time Series

National audienceCet article présente une nouvelle approche pour l'analyse de séries d'images satellite InSAR (Interferometric Synthetic Aperture Radar) et son application au monitoring de fluage le long d'une faille sismique active majeure. Les données InSAR permettent de mesurer les déformations du sol entre deux dates sur de grandes zones géographiques, mais la précision des mesures reste limitée par le bruit du aux variations en temps et en espace des conditions atmosphériques. L'approche proposée combine des techniques d'analyse d'images satellite et des techniques de fouille de données. Elle permet de traiter des séries d'images satellite InSAR de façon non supervisée, même avec des conditions atmosphériques variables, et fournit aux experts des cartes d'évolutions décrivant les déformations du sol. Des résultats expérimentaux sur une série d'images ENVISAT de la faille de Haiyuan (zone Nord-Est du plateau tibétain) sont présentés. Les cartes obtenues montrent un glissement asismique continu superficiel le long d'une portion de la faille, ce qui est consistant avec les modèles géophysiques actuels

Quantifying the Effect of Registration Error on Spatio-Temporal Fusion

It is challenging to acquire satellite sensor data with both fine spatial and fine temporal resolution, especially for monitoring at global scales. Among the widely used global monitoring satellite sensors, Landsat data have a coarse temporal resolution, but fine spatial resolution, while moderate resolution imaging spectroradiometer (MODIS) data have fine temporal resolution, but coarse spatial resolution. One solution to this problem is to blend the two types of data using spatio-temporal fusion, creating images with both fine temporal and fine spatial resolution. However, reliable geometric registration of images acquired by different sensors is a prerequisite of spatio-temporal fusion. Due to the potentially large differences between the spatial resolutions of the images to be fused, the geometric registration process always contains some degree of uncertainty. This article analyzes quantitatively the influence of geometric registration error on spatio-temporal fusion. The relationship between registration error and the accuracy of fusion was investigated under the influence of different temporal distances between images, different spatial patterns within the images and using different methods (i.e., spatial and temporal adaptive reflectance fusion model (STARFM), and Fit-FC; two typical spatio-temporal fusion methods). The results show that registration error has a significant impact on the accuracy of spatio-temporal fusion; as the registration error increased, the accuracy decreased monotonically. The effect of registration error in a heterogeneous region was greater than that in a homogeneous region. Moreover, the accuracy of fusion was not dependent on the temporal distance between images to be fused, but rather on their statistical correlation. Finally, the Fit-FC method was found to be more accurate than the STARFM method, under all registration error scenarios. © 2008-2012 IEEE

Radial Alignment in Simulated Clusters

Observational evidence for the radial alignment of satellites with their dark matter host has been accumulating steadily in the past few years. The effect is seen over a wide range of scales, from massive clusters of galaxies down to galaxy-sized systems, yet the underlying physical mechanism has still not been established. To this end, we have carried out a detailed analysis of the shapes and orientations of dark matter substructures in high-resolution N-body cosmological simulations. We find a strong tendency for radial alignment of the substructure with its host halo: the distribution of halo major axes is very anisotropic, with the majority pointing towards the center of mass of the host. The alignment peaks once the sub-halo has passed the virial radius of the host for the first time, but is not subsequently diluted, even after the halos have gone through as many as four pericentric passages. This evidence points to the existence of a very rapid dynamical mechanism acting on these systems and we argue that tidal torquing throughout their orbits is the most likely candidate.Comment: v2: 13 pages, 10 figures, ApJ in press. Revisions include a new section (4.2) comparing our results with observations, and a few added reference

Compressive sensing reconstruction of 3D wet refractivity based on GNSS and InSAR observations

In this work, the reconstruction quality of an approach for neutrospheric water vapor tomography based on Slant Wet Delays (SWDs) obtained from Global Navigation Satellite Systems (GNSS) and Interferometric Synthetic Aperture Radar (InSAR) is investigated. The novelties of this approach are (1) the use of both absolute GNSS and absolute InSAR SWDs for tomography and (2) the solution of the tomographic system by means of compressive sensing (CS). The tomographic reconstruction is performed based on (i) a synthetic SWD dataset generated using wet refractivity information from the Weather Research and Forecasting (WRF) model and (ii) a real dataset using GNSS and InSAR SWDs. Thus, the validation of the achieved results focuses (i) on a comparison of the refractivity estimates with the input WRF refractivities and (ii) on radiosonde profiles. In case of the synthetic dataset, the results show that the CS approach yields a more accurate and more precise solution than least squares (LSQ). In addition, the benefit of adding synthetic InSAR SWDs into the tomographic system is analyzed. When applying CS, adding synthetic InSAR SWDs into the tomographic system improves the solution both in magnitude and in scattering. When solving the tomographic system by means of LSQ, no clear behavior is observed. In case of the real dataset, the estimated refractivities of both methodologies show a consistent behavior although the LSQ and CS solution strategies differ

Spatiotemporal Data Augmentation of MODIS-LANDSAT Water Bodies Using Generative Adversarial Networks

The monitoring of the shape and area of a water body is an essential component for many Earth science and Hydrological applications. For this purpose, these applications require remote sensing data which provides accurate analysis of the water bodies. In this thesis the same is being attempted, first, a model is created that can map the information from one kind of satellite that captures the data from a distance of 500m to another data that is captured by a different satellite at a distance of 30m. To achieve this, we first collected the data from both of the satellites and translated the data from one satellite to another using our proposed Hydro-GAN model. This translation gives us the accurate shape, boundary, and area of the water body. We evaluated the method by using several different similarity metrics for the area and the shape of the water body. The second part of this thesis involves augmenting the data that we obtained from the Hydro-GAN model with the original data and using this enriched data to predict the area of a water body in the future. We used the case study of Great Salt lake for this purpose. The results indicated that our proposed model was creating accurate area and shape of the water bodies. When we used our proposed model to generate data at a resolution of 30m it gave us better areal and shape accuracy. If we get more data at this resolution, we can use that data to better predict coastal lines, boundaries, as well as erosion monitoring

Empiricism and stochastics in cellular automaton modeling of urban land use dynamics

An increasing number of models for predicting land use change in regions of rapidurbanization are being proposed and built using ideas from cellular automata (CA)theory. Calibrating such models to real situations is highly problematic and to date,serious attention has not been focused on the estimation problem. In this paper, wepropose a structure for simulating urban change based on estimating land usetransitions using elementary probabilistic methods which draw their inspiration fromBayes' theory and the related ?weights of evidence? approach. These land use changeprobabilities drive a CA model ? DINAMICA ? conceived at the Center for RemoteSensing of the Federal University of Minas Gerais (CSR-UFMG). This is based on aneight cell Moore neighborhood approach implemented through empirical land useallocation algorithms. The model framework has been applied to a medium-size townin the west of São Paulo State, Bauru. We show how various socio-economic andinfrastructural factors can be combined using the weights of evidence approach whichenables us to predict the probability of changes between land use types in differentcells of the system. Different predictions for the town during the period 1979-1988were generated, and statistical validation was then conducted using a multipleresolution fitting procedure. These modeling experiments support the essential logicof adopting Bayesian empirical methods which synthesize various information aboutspatial infrastructure as the driver of urban land use change. This indicates therelevance of the approach for generating forecasts of growth for Brazilian citiesparticularly and for world-wide cities in general