Semantic Approach in Image Change Detection

International audienceChange detection is a main issue in various domains, and especially for remote sensing purposes. Indeed, plethora of geospatial images are available and can be used to update geographical databases. In this paper, we propose a classification-based method to detect changes between a database and a more recent image. It is based both on an efficient training point selection and a hierarchical decision process. This allows to take into account the intrinsic heterogeneity of the objects and themes composing a database while limiting false detection rates. The reliability of the designed framework method is first assessed on simulated data, and then successfully applied on very high resolution satellite images and two land-cover databases

Coupled retrieval of aerosol optical thickness, columnar water vapor and surface reflectance maps from ENVISAT/MERIS data over land

An algorithm for the derivation of atmospheric parameters and surface reflectance data from MEdium Resolution Imaging Specrometer Instrument (MERIS) on board ENVIronmental SATellite (ENVISAT) images has been developed. Geo-rectified aerosol optical thickness (AOT), columnar water vapor (CWV) and spectral surface reflectance maps are generated from MERIS Level-1b data over land. The algorithm has been implemented so that AOT, CWV and reflectance products are provided on an operational manner, making no use of ancillary parameters apart from those attached to MERIS products. For this reason, it has been named Self-Contained Atmospheric Parameters Estimation from MERIS data (SCAPE-M). The fundamental basis of the algorithm and applicable error figures are presented in the first part of this paper. In particular, errors of ± 0.03, ± 4% and ± 8% have been estimated for AOT, CWV and surface reflectance retrievals, respectively, by means of a sensitivity analysis based on a synthetic data set simulated under a usual MERIS scene configuration over land targets. The assumption of a fixed aerosol model, the coarse spatial resolution of the AOT product and the neglection of surface reflectance directional effects were also identified as limitations of SCAPE-M. Validation results are detailed in the second part of the paper. Comparison of SCAPE-M AOT retrievals with data from AErosol RObotic NETwork (AERONET) stations showed an average Root Mean Square Error (RMSE) of 0.05, and an average correlation coefficient R2 of about 0.7-0.8. R2 values grew up to more than 0.9 in the case of CWV after comparison with the same stations. A good correlation is also found with the MERIS Level-2 ESA CWV product. Retrieved surface reflectance maps have been successfully compared with reflectance data derived from the Compact High Resolution Imaging Spectrometer (CHRIS) on board the PRoject for On-Board Autonomy (PROBA) in the first place. Reflectance retrievals have also been compared with reflectance data derived from MERIS images by the Bremen AErosol Retrieval (BAER) method. A good correlation in the red and near-infrared bands was found, although a considerably higher proportion of pixels was successfully processed by SCAPE-M. © 2008 Elsevier Inc. All rights reserved

Nonlinear Retrieval of Atmospheric Profiles from MetOp-IASI and MTG-IRS Data

This paper evaluates the potential use of nonlinear retrieval methods to derive cloud, surface and atmospheric properties from hyperspectral MetOp-IASI and MTG-IRS spectra. The methods are compared in terms of both accuracy and speed with the current IASI and IRS L2 PPFP implementation, which consists of a principal component extraction, typically referred as to Empirical Orthogonal Functions (EOF), and a subsequent canonical linear regression. This research proposes the evaluation of some other methodological advances considering 1) other linear feature extraction methods instead of EOF, such as (orthonormalized) partial least squares, and 2) the linear combination of nonlinear regression models in the form of committee of experts. The nonlinear regression models considered in this work are artificial neural networks (NN) and kernel ridge regression (KRR) as nonparametric multioutput powerful regression tools. Results show that, in general, nonlinear models outperform the linear retrieval both in the presence of noise and noise-free settings, and for both IASI and IRS synthetic and real data. The combination of models makes the retrieval more robust, improves the accuracy, and decreases the estimated bias. These results confirm the validity of the proposed approach for retrieval of atmospheric profiles. © 2010 Copyright SPIE - The International Society for Optical Engineering

Estimation of solar-induced vegetation fluorescence from space measurements

A characteristic spectral emission is observed in vegitation chlorophyll under excitation by solar radiation. This emission, known as solar-induced chlorophyll fluorescence, occurs in the red and near infra-red spectral regions. In this paper a new methodology for the estimation of solar-induced chlorophyll fluorescence from spaceborne and airborne sensors is presented. The fluorescence signal is included in an atmospheric radiative transfer scheme so that chlorophyll fluorescence and surface reflectance are retrieved consistently from the measured at-sensor radiance. This methodology is tested on images acquired by the Medium Resolution Imaging Spectrometer (MERIS) on board the ENVIronmental SATellite (ENVISAT) taking advantage of its good charactefization of the O2-A absorption band. Validation of MERIS-derived fluorescence is carried out by applying the method to data acquired by the Compact Airborne Spectrographic Imager (CASI-1500) sensor concurrently to MERIS acquisitions. CASI-derived fluorescence is in turn compared with ground-based fluorescence measurements, a correlation coefficient R2 of 0.85 being obtained. Copyright 2007 by the American Geophysical Union

Cloud-screening algorithm for ENVISAT/MERIS multispectral images

This paper presents a methodology for cloud screening of multispectral images acquired with the Medium Resolution Imaging Spectrometer (MERIS) instrument on-board the Environmental Satellite (ENVISAT). The method yields both a discrete cloud mask and a cloud-abundance product from MERIS level-lb data on a per-pixel basis. The cloud-screening method relies on the extraction of meaningful physical features (e.g., brightness and whiteness), which are combined with atmospheric-absorption features at specific MERIS-band locations (oxygen and watervapor absorptions) to increase the cloud-detection accuracy. All these features are inputs to an unsupervised classification algorithm; the cloud-probability output is then combined with a spectral unmixing procedure to provide a cloud-abundance product instead of binary flags. The method is conceived to be robust and applicable to a broad range of actual situations with high variability of cloud types, presence of ground covers with bright and white spectra, and changing illumination conditions or observation geometry. The presented method has been shown to outperform the MERIS level-2 cloud flag in critical cloud-screening situations, such as over ice/snow covers and around cloud borders. The proposed modular methodology constitutes a general framework that can be applied to multispectral images acquired by spaceborne sensors working in the visible and near-infrared spectral range with proper spectral information to characterize atmospheric-oxygen and water-vapor absorptions. © 2007 IEEE

Developments for vegetation fluorescence retrieval from spaceborne high-resolution spectrometry in the O-2-A and O-2-B absorption bands

Solar-induced chlorophyll fluorescence is a weak electromagnetic signal emitted in the red and far-red spectral regions by vegetation chlorophyll under excitation by solar radiation. Chlorophyll fluorescence has been demonstrated to be a close proxy to vegetation physiological functioning. The basis for fluorescence retrieval from passive space measurements is the exploitation of the O2-A and O2-B atmospheric absorption features to isolate the fluorescence signal from the solar radiation reflected by the surface and the atmosphere. High spectral resolution measurements and a precise modeling of the atmospheric radiative transfer in the visible and near-infrared regions are mandatory. Recent developments for fluorescence retrieval from passive high spectral resolution spaceborne measurements are presented in this work, which has been performed in preparation of the FLuorescence EXplorer (FLEX) mission, which is currently under development by the European Space Agency. A large data set of FLEX-like measurements has been simulated for the purpose of methodology development and testing. Issues related to vegetation chlorophyll fluorescence retrieval from space, a description of the proposed methodology, initial results from simulated test cases, and general guidelines for the specification of fluorescence retrieval instruments are presented and discussed in this work. Copyright 2010 by the American Geophysical Union

Regularized Multiresolution Spatial Unmixing for ENVISAT/MERIS and Landsat/TM Image Fusion

Earth observation satellites currently provide a large volume of images at different scales. Most of these satellites provide global coverage with a revisit time that usually depends on the instrument characteristics and performance. Typically, medium-spatial-resolution instruments provide better spectral and temporal resolutions than mapping-oriented high-spatial-resolution multispectral sensors. However, in order to monitor a given area of interest, users demand images with the best resolution available, which cannot be reached using a single sensor. In this context, image fusion may be effective to merge information from different data sources. In this letter, an image fusion approach based on multiresolution and multisource spatial unmixing is used to obtain a composite image with the spectral and temporal characteristics of medium-spatial-resolution instrument along with the spatial resolution of high-spatial-resolution image. A time series of Landsat/TM and ENVISAT/MERIS Full Resolution images acquired in the 2004 European Space Agency (ESA) Spectra Barrax Campaign illustrates the method's capabilities. The qualitative and quantitative assessments of the product images are given. The proposed methodology is general enough to be applied to similar sensors, such as the multispectral instruments which will fly on board the ESA GMES Sentinel-2 and Sentinel-3 upcoming satellite series. © 2011 IEEE