Natural Color Satellite Image Mosaicking Using Quadratic Programming in Decorrelated Color Space

International audienceGenerating mosaics of orthorectified remote sensing images is a challenging task because of the colorimetric differences between adjacent images introduced by land use, surface illumination, atmospheric conditions, and sensor. Most of the existing color correction methods involve pairwise techniques, which are limited when the collection of images is large with numerous overlaps. Besides , available techniques do not operate in a color space suited for true-color processing. This paper presents a simple and robust method to perform the global colorimetric harmonization of multiple overlaping remote sensing images in natural colors (RGB). Our parameter-free method deals simultaneously with any number of images, with any spatial layout, and without any single reference image. It is based on the resolution of a quadratic programming optimization problem. It operates in the lαβ decorrelated color space, which is well suited for human vision of natural scenes. The results obtained from the mosaicking of 132 RapidEye color orthoimages over mainland France demonstrate good potential for performing colorimetric harmonization automatically and effectively.La génération de mosaïques d'images orthorectifiées en télédétection est une tâche difficile en raison des différences colorimétriques entre les images adjacentes introduites par l'utilisation des terres, l'éclairage de surface, les conditions atmosphériques, et le capteur. La plupart des méthodes existantes de correction des couleurs impliquent des techniques par paires, qui sont limitées lorsque la collection d'images est grand avec de nombreux chevauchements. En outre, les techniques disponibles ne fonctionnent pas dans un espace de couleur adapté pour le traitement des vraies couleurs. Cet article présente une méthode de androbust simple à réaliser l'harmonisation colorimétrique mondial de distance chevauchent plusieurs images de détection dans des couleurs naturelles (RGB). Nos méthode sans paramètres traite simultanément avec un certain nombre d'images et avec un aménagement de l'espace, et sans aucune image de référence unique. Elle est basée sur la résolution d'un problème d'optimisation quadratique programmation (QP). Elle opère dans l'espace de couleur de lab décorrélés, ce qui est bien adapté pour la vision humaine de scènes naturelles. Les résultats obtenus à partir du mosaïquage de 132 RapidEye ortho-images de couleurs plus France métropolitaine démontrent un bon potentiel pour réaliser l'harmonisation colorimétrique automatiquement et efficacement

Multitemporal Observations of Sugarcane by TerraSAR-X Images

The objective of this study is to investigate the potential of TerraSAR-X (X-band) in monitoring sugarcane growth on Reunion Island (located in the Indian Ocean). Multi-temporal TerraSAR data acquired at various incidence angles (17°, 31°, 37°, 47°, 58°) and polarizations (HH, HV, VV) were analyzed in order to study the behaviour of SAR (synthetic aperture radar) signal as a function of sugarcane height and NDVI (Normalized Difference Vegetation Index). The potential of TerraSAR for mapping the sugarcane harvest was also studied. Radar signal increased quickly with crop height until a threshold height, which depended on polarization and incidence angle. Beyond this threshold, the signal increased only slightly, remained constant, or even decreased. The threshold height is slightly higher with cross polarization and higher incidence angles (47° in comparison with 17° and 31°). Results also showed that the co-polarizations channels (HH and VV) were well correlated. High correlation between SAR signal and NDVI calculated from SPOT-4/5 images was observed. TerraSAR data showed that after strong rains the soil contribution to the backscattering of sugarcane fields can be important for canes with heights of terminal visible dewlap (htvd) less than 50 cm (total cane heights around 155 cm). This increase in radar signal after strong rains could involve an ambiguity between young and mature canes. Indeed, the radar signal on TerraSAR images acquired in wet soil conditions could be of the same order for fields recently harvested and mature sugarcane fields, making difficult the detection of cuts. Finally, TerraSAR data at high spatial resolution were shown to be useful for monitoring sugarcane harvest when the fields are of small size or when the cut is spread out in time. The comparison between incidence angles of 17°, 37° and 58° shows that 37° is more suitable to monitor the sugarcane harvest. The cut is easily detectable on TerraSAR images for data acquired less than two or three months after the cut. The radar signal decreases about 5dB for images acquired some days after the cut and 3 dB for data acquired two month after the cut (VV-37°). The difference in radar signal becomes negligible (<1 dB) between harvested fields and mature canes for sugarcane harvested since three months or more

Comparison of convolutional neural networks for cloudy optical images reconstruction from single or multitemporal joint SAR and optical images

With the increasing availability of optical and synthetic aperture radar (SAR) images thanks to the Sentinel constellation, and the explosion of deep learning, new methods have emerged in recent years to tackle the reconstruction of optical images that are impacted by clouds. In this paper, we focus on the evaluation of convolutional neural networks that use jointly SAR and optical images to retrieve the missing contents in one single polluted optical image. We propose a simple framework that ease the creation of datasets for the training of deep nets targeting optical image reconstruction, and for the validation of machine learning based or deterministic approaches. These methods are quite different in terms of input images constraints, and comparing them is a problematic task not addressed in the literature. We show how space partitioning data structures help to query samples in terms of cloud coverage, relative acquisition date, pixel validity and relative proximity between SAR and optical images. We generate several datasets to compare the reconstructed images from networks that use a single pair of SAR and optical image, versus networks that use multiple pairs, and a traditional deterministic approach performing interpolation in temporal domain.Comment: 17 page

Clear-cuts detection services for the monitoring needs of the french Ministry of agriculture

International audienceIn France, clear-cuts are a logging practice regulated by law under the Forest Code. The French Ministry of Agriculture, in charge of forest policies, needs exhaustive mapping methods to monitor the clear-cuts and allow its agents to efficiently take actions in the field. We present here the evolution of a clear-cut detection service relying on optical satellite data, from very high spatial resolution images to Sentinel-2 time series. Its nationwide deployment was possible due to a favorable context both technical and institutional

A Generic Framework for the Development of Geospatial Processing Pipelines on Clusters

International audienceThe amount of remote sensing data available to applications is constantly growing due to the rise of very-high-resolution sensors and short repeat cycle satellites. Consequently, tackling computational complexity in Earth Observation information extraction is rising as a major challenge. Resorting to High Performance Computing (HPC) is becoming a common practice, since it provides environments and programming facilities able to speed-up processes. In particular, clusters are flexible, cost-effective systems able to perform data-intensive tasks ideally fulfilling any computational requirement. However, their use typically implies a significant coding effort to build proper implementations of specific processing pipelines. This paper presents a generic framework for the development of RS images processing applications targeting cluster computing. It is based on common open sources libraries, and leverages the parallelization of a wide variety of image processing pipelines in a transparent way. Performances on typical RS tasks implemented using the proposed framework demonstrate a great potential for the effective and timely processing of large amount of data

Large Scale Segmentation Algorithm for Object Based Image Analysis suitable for HPC architectures in hybrid distributed-shared memory context

International audienceThe amount of remote sensing images is constantly growing due to the increasing amount of satellites with Very High Resolution or very short revisiting time. In this context, exact large scale OBIA segmentation algorithms have been proposed to process remote sensing images of arbitrary size [1,2], to overcome the memory limitation issue. It is important to note that in this context, The word exact means that algorithms ensure resulting segments to beidentical to those obtained without tiling, while using a tiling strategy with a proper stability margin However those solutions have been proposed for sequential execution, and current implementations can use only one single processing unit on one single computer. Thus the execution time can be very important, which still prevents those approaches to be used for OBIA processing in an operational context. Meanwhile, resorting to High Performance Computing (HPC) is becoming a common practice to tackle the computational complexity in Earth Observation information extraction, since it provides environments and programming facilities able to speed-up processes. In this paper, we focus on the parallelization of segmentation algorithms to process large images in a reasonable time, using HPC techniques. Our approach consists in a generic strategy of parallelization using the Message Passing Interface (MPI) standard with the concept of remote window. While MPI enables the scaling through multiple processing nodes, threaded parallelism is used in the shared memory context to optimize core algorithms. A specific attention has also been paid to IO operations which is a well known bottleneck in HPC environments. We have successfully applied our method to two state of the art segmentation algorithms commonly used in remote sensing images processing, namely the Generic Region Merging [2] and the Mean Shift Smoothing based segmentation [1]. The hybrid distributed-shared memory approach enables algorithms to benefit from both multiple CPUs of one processing node, and also multiple nodes connected through network thank to the MPI. As our aim is to enable those algorithms for operational applications, we have implemented our approach for the two segmentation algorithms cited above in the Orfeo ToolBox, a well known open-source library for geospatial images processing [3]. In the final paper and presentation, trends of execution times for different configurations of real remote sensing input data and HPC ressources will be presented and discussed. The source code corresponding to the application presented in this paper is available for download [4], and will be integrated in the forthcoming releases of the Orfeo Toolbox [5]. [1]Michel, J., Youssefi, D., & Grizonnet, M. (2015). Stable mean-shift algorithm and its application to the segmentation of arbitrarily large remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 53(2), 952-964. [2]Lassalle, P., Inglada, J., Michel, J., Grizonnet, M., & Malik, J. (2015). A scalable tile-based framework for region-merging segmentation. IEEE Transactions on Geoscience and Remote Sensing, 53(10),5473-5485. [3]Grizonnet, M., Michel, J., Poughon, V., Inglada, J., Savinaud, M., & Cresson, R. (2017). Orfeo ToolBox: open source processing of remote sensing images. Open Geospatial Data, Software and Standards, 2(1),15. [4]LSOBIA:https://github.com/RTOBIA/LSOBIA [5]OrfeoToolBox:www.orfeo-toolbox.or

A Two-Branch CNN Architecture for Land Cover Classification of PAN and MS Imagery

International audienceThe use of Very High Spatial Resolution (VHSR) imagery in remote sensing applications is nowadays a current practice whenever fine-scale monitoring of the earth’s surface is concerned. VHSR Land Cover classification, in particular, is currently a well-established tool to support decisions in several domains, including urban monitoring, agriculture, biodiversity, and environmental assessment. Additionally, land cover classification can be employed to annotate VHSR imagery with the aim of retrieving spatial statistics or areas with similar land cover. Modern VHSR sensors provide data at multiple spatial and spectral resolutions, most commonly as a couple of a higher-resolution single-band panchromatic (PAN) and a coarser multispectral (MS) imagery. In the typical land cover classification workflow, the multi-resolution input is preprocessed to generate a single multispectral image at the highest resolution available by means of a pan-sharpening process. Recently, deep learning approaches have shown the advantages of avoiding data preprocessing by letting machine learning algorithms automatically transform input data to best fit the classification task. Following this rationale, we here propose a new deep learning architecture to jointly use PAN and MS imagery for a direct classification without any prior image sharpening or resampling process. Our method, namely MultiResoLCC , consists of a two-branch end-to-end network which extracts features from each source at their native resolution and lately combine them to perform land cover classification at the PAN resolution. Experiments are carried out on two real-world scenarios over large areas with contrasted land cover characteristics. The experimental results underline the quality of our method while the characteristics of the proposed scenarios underline the applicability and the generality of our strategy in operational settings