Fusion de données provenant de différents capteurs satellitaires pour le suivi de la qualité de l'eau en zones côtières. Application au littoral de la région PACA

Monitoring coastal areas requires both a good spatial resolution, good spectral resolution associated with agood signal to noise ratio and finally a good temporal resolution to visualize rapid changes in water color.Available now, and even those planed soon, sensors do not provide both a good spatial, spectral ANDtemporal resolution. In this study, we are interested in the image fusion of two future sensors which are bothpart of the Copernicus program of the European Space Agency: MSI on Sentinel-2 and OLCI on Sentinel-3.Such as MSI and OLCI do not provide image yet, it was necessary to simulate them. We then used thehyperspectral imager HICO and we then proposed three methods: an adaptation of the method ARSIS fusionof multispectral images (ARSIS), a fusion method based on the non-negative factorization tensors (Tensor)and a fusion method based on the inversion de matrices (Inversion).These three methods were first evaluated using statistical parameters between images obtained by fusionand the "perfect" image as well as the estimation results of biophysical parameters obtained by minimizingthe radiative transfer model in water.Le suivi des zones côtières nécessite à la fois une bonne résolution spatiale, une bonne résolution spectraleassociée à un bon rapport signal sur bruit et enfin une bonne résolution temporelle pour visualiser deschangements rapides de couleur de l’eau.Les capteurs disponibles actuellement, et même ceux prévus prochainement, n’apportent pas à la fois unebonne résolution spatiale, spectrale ET temporelle. Dans cette étude, nous nous intéressons à la fusion de 2futurs capteurs qui s’inscrivent tous deux dans le programme Copernicus de l’agence spatiale européenne:MSI sur Sentinel-2 et OLCI sur Sentinel-3.Comme les capteurs MSI et OLCI ne fournissent pas encore d’images, il a fallu les simuler. Pour cela nousavons eu recours aux images hyperspectrales du capteur HICO. Nous avons alors proposé 3 méthodes : uneadaptation de la méthode ARSIS à la fusion d’images multispectrales (ARSIS), une méthode de fusion baséesur la factorisation de tenseurs non-négatifs (Tenseur) et une méthode de fusion basée sur l’inversion dematrices (Inversion)Ces 3 méthodes ont tout d’abord été évaluées à l’aide de paramètres statistiques entre les images obtenuespar fusion et l’image « parfaite » ainsi que sur les résultats d’estimation de paramètres biophysiques obtenuspar minimisation du modèle de transfert radiatif dans l’eau

Target-adaptive CNN-based pansharpening

We recently proposed a convolutional neural network (CNN) for remote sensing image pansharpening obtaining a significant performance gain over the state of the art. In this paper, we explore a number of architectural and training variations to this baseline, achieving further performance gains with a lightweight network which trains very fast. Leveraging on this latter property, we propose a target-adaptive usage modality which ensures a very good performance also in the presence of a mismatch w.r.t. the training set, and even across different sensors. The proposed method, published online as an off-the-shelf software tool, allows users to perform fast and high-quality CNN-based pansharpening of their own target images on general-purpose hardware

An Unsupervised Algorithm for Change Detection in Hyperspectral Remote Sensing Data Using Synthetically Fused Images and Derivative Spectral Profiles

Multitemporal hyperspectral remote sensing data have the potential to detect altered areas on the earth’s surface. However, dissimilar radiometric and geometric properties between the multitemporal data due to the acquisition time or position of the sensors should be resolved to enable hyperspectral imagery for detecting changes in natural and human-impacted areas. In addition, data noise in the hyperspectral imagery spectrum decreases the change-detection accuracy when general change-detection algorithms are applied to hyperspectral images. To address these problems, we present an unsupervised change-detection algorithm based on statistical analyses of spectral profiles; the profiles are generated from a synthetic image fusion method for multitemporal hyperspectral images. This method aims to minimize the noise between the spectra corresponding to the locations of identical positions by increasing the change-detection rate and decreasing the false-alarm rate without reducing the dimensionality of the original hyperspectral data. Using a quantitative comparison of an actual dataset acquired by airborne hyperspectral sensors, we demonstrate that the proposed method provides superb change-detection results relative to the state-of-the-art unsupervised change-detection algorithms

Multiresolution based, multisensor, multispectral image fusion

Spaceborne sensors, which collect imagery of the Earth in various spectral bands, are limited by the data transmission rates. As a result the multispectral bands are transmitted at a lower resolution and only the panchromatic band is transmitted at its full resolution. The information contained in the multispectral bands is an invaluable tool for land use mapping, urban feature extraction, etc. However, the limited spatial resolution reduces the appeal and value of this information. Pan sharpening techniques enhance the spatial resolution of the multispectral imagery by extracting the high spatial resolution of the panchromatic band and adding it to the multispectral images. There are many different pan sharpening methods available like the ones based on the Intensity-Hue-Saturation and the Principal Components Analysis transformation. But these methods cause heavy spectral distortion of the multispectral images. This is a drawback if the pan sharpened images are to be used for classification based applications. In recent years, multiresolution based techniques have received a lot of attention since they preserve the spectral fidelity in the pan sharpened images. Many variations of the multiresolution based techniques exist. They differ based on the transform used to extract the high spatial resolution information from the images and the rules used to synthesize the pan sharpened image. The superiority of many of the techniques has been demonstrated by comparing them with fairly simple techniques like the Intensity-Hue-Saturation or the Principal Components Analysis. Therefore there is much uncertainty in the pan sharpening community as to which technique is the best at preserving the spectral fidelity. This research investigates these variations in order to find an answer to this question. An important parameter of the multiresolution based methods is the number of decomposition levels to be applied. It is found that the number of decomposition levels affects both the spatial and spectral quality of the pan sharpened images. The minimum number of decomposition levels required to fuse the multispectral and panchromatic images was determined in this study for image pairs with different resolution ratios and recommendations are made accordingly