Antoine Lefebvre

Estimation of the orientation of textured patterns via wavelet analysis

This paper is concerned with the estimation of the dominant orientation of textured patches that appear in a number of images (remote sensing, biology or natural sciences for instance). It is based on the maximization of a criterion that deals with the coefficients enclosed in the different bands of a wavelet decomposition of the original image. More precisely, we search for the orientation that best concentrates the energy of the coefficients in a single direction. To compare the wavelet coefficients between the different bands, we use the Kullback-Leibler divergence on their distribution, this latter being assumed to behave like a Generalized Gaussian Density. The space-time localization of the wavelet transform allows to deal with any polygon that may be contained in a single image. This is of key importance when one works with (non-rectangular) segmented objects. We have applied the same methodology but using other criteria to compare the distributions, in order to highlight the benefit of the Kullback-Leibler divergence. In addition, the methodology is validated on synthetic and real situations and compared with a state-of-the-art approach devoted to orientation estimation.

Monitoring Urban Areas with Sentinel-2A Data: Application to the Update of the Copernicus High Resolution Layer Imperviousness Degree

Monitoring with high resolution land cover and especially of urban areas is a key task that is more and more required in a number of applications (urban planning, health monitoring, ecology, etc.). At the moment, some operational products, such as the “Copernicus High Resolution Imperviousness Layer”, are available to assess this information, but the frequency of updates is still limited despite the fact that more and more very high resolution data are acquired. In particular, the recent launch of the Sentinel-2A satellite in June 2015 makes available data with a minimum spatial resolution of 10 m, 13 spectral bands, wide acquisition coverage and short time revisits, which opens a large scale of new applications. In this work, we propose to exploit the benefit of Sentinel-2 images to monitor urban areas and to update Copernicus Land services, in particular the High Resolution Layer imperviousness. The approach relies on independent image classification (using already available Landsat images and new Sentinel-2 images) that are fused using the Dempster–Shafer theory. Experiments are performed on two urban areas: a large European city, Prague, in the Czech Republic, and a mid-sized one, Rennes, in France. Results, validated with a Kappa index over 0.9, illustrate the great interest of Sentinel-2 in operational projects, such as Copernicus products, and since such an approach can be conducted on very large areas, such as the European or global scale. Though classification and data fusion are not new, our process is original in the way it optimally combines uncertainties issued from classifications to generate more confident and precise imperviousness maps. The choice of imperviousness comes from the fact that it is a typical application where research meets the needs of an operational production. Moreover, the methodology presented in this paper can be used in any other land cover classification task using regular acquisitions issued, for example, from Sentinel-2.

Object-Oriented Approach and Texture Analysis for Change Detection in Very High Resolution Images

The objective of this paper is to develop an object-based change detection method able to qualify the nature of changes in landscapes from remotely sensed images, in terms of geometry and content. The originality of the approach consists in jointly dealing with the analysis of the object contours and the analysis of texture evolution. The method is applied on grassy strips, which are landscape buffers between crops and hydrologic networks. A geometric change index that quantify the intensity of change and that qualify it according its properties is proposed. We also present a content change index that discriminates partial change from diffuse change that affect the object of interest. It turned out that the geometric changes, most of abrupt content changes and some of subtle changes have been accurately detected. Lastly, our approach is suitable on airborne data with a Very High Resolution data and can be generalized to spaceborne images.

Segmentation of very high spatial resolution panchromatic images based on wavelets and evidence theory

This paper is concerned with the segmentation of very high spatial resolution panchromatic images. We propose a method for unsupervised segmentation of remotely sensed images based on texture information and evidence theory. We first perform a segmentation of the image using a watershed on some coefficients issued from a wavelet decomposition of the initial image. This yields an over-segmented map where the similar objects, from a textural point of view, are aggregated together in a step forward. The information of texture is obtained by analyzing the wavelet coefficients of the original image. At each band of the wavelet decomposition, we compute an indicator of similarity between two objects. All the indicators are then fused using some rules of evidence theory to derive a unique criterion of similarity between two objects.

Mapping tree cover in European cities: Comparison of classification algorithms for an operational production framework

In the framework of the Urban Atlas 2012 production, this paper investigated a set of generative models (Maximum likelihood, k-means) and discriminative models (k Nearest Neighbors, Support Vector Machine and Neural Network) to extract urban-tree cover at a European scale. Based on SPOT-5 images and a training on a large coarse resolution dataset, this study tested the performance of these algorithms on 3 cities regarding their geographical location, urban morphology and acquisition dates. Result reveals that discriminative models are more robust than generative ones. It shows that overall accuracy varies from 75% for the k-means classifier to 85% for the neural network. It also shows that neural networks provide the most balanced results (ratio between commission and omission errors) leading to be most suitable algorithm to process different cities with heterogeneous data.

A measure for change detection in very high resolution remote sensing images based on texture analysis

This paper address the problem of change detection in very high resolution remote sensing images. To that end, we define a measure of the observed change based on the distribution of the coefficients issued from a wavelet transform, taking care to be rotation invariant. The dissimilarities are obtained through the Kullback-Liebler distance and a change features vector is defined from all the distances between the bands of the wavelet decomposition. This measurement is able to classify the nature of the change between two images. We present two applications: the first one uses a decision tree to classify several changes (homogeneous or oriented texture, abrupt or subtle change) whereas the second one detects some particular changes from a pair of images (an aerial and a satellite image). These experiments bring out the efficiency of the proposed technique to discriminate correctly the different textures and to interpret each change.

Vineyard identification and characterization based on texture analysis in the Helderberg Basin (South Africa)

In this paper, a methodology for the spatial identification and characterization of vineyards using texture analysis is proposed to meet the need of ongoing and further viticultural “terroir” studies. The proposed method is based on the maximization of a criteria that deals with the coefficients enclosed in the different bands of a wavelet decomposition of the original image. More precisely, we search for the orientation that best concentrates the energy of the coefficients in a single direction. For each texture pattern, a degree of anisotropy and the angle of the main orientation is extracted. The methodology is validated on aerial-photographs in the Helderberg Basin (South Africa). The degree of anisotropy is a reliable information able to discriminate vineyards to other land-uses. Moreover, the row orientation turns out to be a relevant information for all applications related to mesoscale atmospheric modeling in vineyard areas.

Monitoring the Morphological Transformation of Beijing Old City Using Remote Sensing Texture Analysis

This paper is concerned with the morphological analysis of Beijing old citys dynamics from 1966 to 2015. This area has been continuously submitted to internal transformations since the opening of China to a market economy. In particular, districts of small traditional houses are being replaced by large buildings, entailing a fast reorganization of the inner city. To monitor this phenomenon, we propose to characterize urban patterns with very-high-resolution images using texture analysis. To this end, dedicated urban descriptors at various scales (based on local variance, cooccurrence matrices, and wavelets) are evaluated and selected to highlight informations related to different urban patterns. These features, whose scales are essential for a reliable description, are used to highlight changes in the city of Beijing in 21 images from 1969 to 2015. The experimental results show good performances and are in accordance with expert knowledge issued from Beijing urban planning studies. About 50% of the old urban pattern has been destroyed and most of these changes occurred before 2001.

Monitoring urban transformation in the old city of Shanghai and its former foreign settlements from 1985 to 2014

In this paper, we monitored the urban morphology of the old city of Shanghai and its former foreign concessions from 1985 to 2014. Based on a time-series of Landsat 5, 7 and 8 images, this study used Iteratively Reweighted Multivariate Alteration Detection (IRMAD) analysis to detect land-use modifications from traditional urban pattern to new constructions. Results show that urban transformation mainly started in 1995 and perpetuate at an average rate of 88 ha per year. It also brings out that about 55% of the old urban pattern was modified in 2014. A detailed interpretation highlights the development of modern high-rise buildings, roads and subway networks, green infrastructures but also the conservation of protected historical buildings.

Wavelet and evidence theory for object-oriented classification: Application to change detection in Rennes metropolitan area

RÉSUMÉ. L’étude des changements d’occupation et d’utilisation des sols à une échelle locale par télédétection repose sur des méthodes de classification nécessitant une forte intervention des opérateurs, ce qui limite fortement leur transposabilité. Cet article présente une méthode de classification orientée objet basée sur une analyse en ondelettes et la théorie des évidences de Dempster-Shafer. L’objectif de ce travail est de développer une méthode de classification simple à utiliser reposant sur un critère de classification unique déterminé à partir de mesures de luminance et de texture. La méthode a été appliquée sur des images à très haute résolution spatiale afin de caractériser et quantifier des changements d’occupation du sol en milieu périurbain. Cette application met en évidence la transposabilité de la méthode sur différents types d’images de télédétection et son utilité dans le cadre d’études de détection de changements.