Satellite imaging has proven to be crucial to monitor methane emissions and help reduce them. In this paper, we propose an automatic practical methodology to use time series from geostationary satellites like GOES-16. While these satellites offer a poor spatial and spectral resolution, their revisit time is unmatched: GOES-16 delivers an image every five minutes for the CONUS region. The proposed approach takes advantage of this fast revisit time to monitor the evolution of large methane emissions. We show the performance on an emission in Mexico and another in the US. This is the first step toward a real time monitoring of methane emissions using geostationary satellite imagery.

We propose a statistically based method to detect hotspots in nighttime short-wave infrared Landsat data. The method assumes an independent and identical Gaussian distribution on the background data and looks for parts of the images with abnormally high values. For this, the variance of the background model is estimated using a robust estimator, being able to provide a good estimation even in the presence of outliers (hotspots). Then, a region growing algorithm is used to extract 4-connected regions with high values. Finally, a statistical test is used to decide whether the sum of the values of each region is significantly higher than expected on the background model. Only regions detected in the two shortwave infrared bands are validated. The test level is selected in order to control the number of false detections. Compared to classical pixel-based methods, our approach allows the detection of hotspots with lower radiance while keeping a low commission error rate. Experiments on a time-series of acquisitions over an oil and gas-producing region showed that this greatly increases the number of detections.

Reducing methane emissions is essential to tackle climate change. Here, we address the problem of detecting automatically point source methane leaks using high resolution hyperspectral images from the PRISMA satellite. We propose an improvement of the classical matched filter method by using an adjustment coefficient. We introduce this new method under the name: Model Adjusted Matched Filter (MAMF). We show that the MAMF method reduces the fraction of false detections compared to the Matched Filter (MF) and the Adaptive Cosine Estimator (ACE) without preventing the detection of plumes. To validate the method, we use a dataset of manually annotated plumes on PRISMA images. We then show that our method outperforms the matched filter and the adaptive cosine estimator in terms of F1 score.

This paper presents a methodology for hotspot detection in multispectral images, utilizing the Reed-Xiaoli anomaly detection algorithm. By leveraging short-wave infrared data from Landsat, the Reed-Xiaoli algorithm identifies hotspots with adaptability to sensors similar to OLI in spectral coverage. The proposed approach is formulated as an a-contrario method, eliminating the need for manually set thresholds for hotspot detection and allows for the control of false detections. The application of this method extends to monitoring the activity status of cement plants, demonstrating robust performance across both daytime and nighttime images. The results show the efficacy of the proposed methodology in hotspot detection for monitoring the activity of industrial facilities such as cement plants.

In this article, we examine the possibility of using the interferometric phase on some fixed corners of storage tanks to infer the tank fill ratio. For the study, we use floating roof tanks for which the fill ratio can be inferred from the floating roof position. We observe a correlation between the phase double difference taken at the fixed roof of neighboring tanks and the fill ratio double difference. We highlight some challenges that require further investigation and the development of adapted InSAR techniques.

We explore an iterative annotation strategy adapted to recurrent multispectral imagery provided by constellations such as Sentinel-2 and applied to the monitoring of events that develop over time. Our key example is the tracking of the progress in the installation of solar power plants. This problem has four difficulties that seem hard to tackle with automatic methods: an unknown and variable spectrum for the solar panels, a variable background, a variable orientation of the panels causing variable cover of the ground, and a variability of lighting and atmosphere transparency due to cloud shadows and water vapor density. We found that each site is different and that only the interactive annotation of the time series can give an acceptable segmentation of the panels. At this point, we describe a weakly monitored interactive annotation tool that predicts the annotation of new paneled zones from an annotation at a previous date. In that way, the human operator intervention is aided and limited to a few corrections from date to date.

We propose a statistically based method to detect hot sources in night-time data from the Visible Infrared Imaging Radiometer Suite (VIIRS). This instrument is aboard three satellites and collects nearly global night-time imagery every day. Our method looks for bright pixels in at least two spectrally adjacent bands, and for groups of bright pixels close on the ground. Four near- to short-wave infrared bands are used, as well as a middle-wave infrared band after background subtraction. Thresholds are set automatically using the a-contrario framework. The algorithm has thus only one parameter with a clear signification: the expected number of false detections that can be tolerated in one image. A comparison to detections reported in VIIRS Nightfire shows that the proposed technique enables the detection of some supplementary points with weak signals.

Reducing methane emissions is essential to tackle climate change. Here, we address the problem of detecting automatically point source methane leaks using high resolution hyperspectral images from the PRISMA satellite. We use a variation of the Matched Filter (MF) called the Adjusted Spectral Matched Filter (ASMF) to detect methane plumes in satellite images. To remove false positives, the detected plumes are confirmed by comparing their orientation to the wind direction extracted from the standard meteorological reanalysis product ERA5. The ASMF reduces the fraction of false detections compared to the MF and without preventing the detection of plumes. To validate the method, we use a recently proposed dataset of manually annotated plumes on PRISMA images. We also compare our detection rate to the detection rate of methods using deep learning or the standard matched filter. We then show that our method outperforms those methods in terms of F1 score.

The detection of smoke plumes by satellite imagery is a comprehensive research topic that can be used to better monitor activity and emissions from the energy and industrial sectors. In this study, we propose a machine learning methodology based on the extraction of relevant features from Sentinel-2 images to perform industrial smoke plume detection. This computer vision problem is modeled as an image classification task based on the presence or absence of plumes from previously identified sources. A dataset of nearly 17,000 hand-labeled images of smoke plumes for activity classification has been compiled to train and evaluate our detection models. The final Gradient Boosting model only uses the 3 RGB bands of Sentinel-2 and after a post-processing step reaches an accuracy of 95%.

Satellite images are becoming an increasingly important part of our world. Such images are used to forecast the weather, track green house gas emissions, monitor agricultural crop health, and many other applications. Such advances are possible thanks to the free availability of a large number of satellite images. Satellite imagery now plays a key role in many areas, including external security. In this context, it is necessary to question the reliability of this data. Can the authenticity of a satellite image be guaranteed? How can one protect oneself against an entity wishing to hide illegal military material or, conversely, to incite action against another entity by falsely suggesting that it possess such material? If the forensic analysis of photographs has attracted a great deal of academic interest in recent years, this is not yet the case for satellite imagery. In this paper, we propose a methodology to create a very simple but interesting dataset to test the performance of state-of-the-art forensic methods on pristine and manipulated satellite images. Despite the strong performance of such algorithms, satellite images require special attention due to the nature of the images themselves.

Since the commissioning phase of Sentinel-1A, several calibration studies have improved the geolocation and geometric modeling of the data. The implementation of the corrections presented in these studies is left to the user. The issues found might be confusing when working with bursts in the interferometric wide swath mode, because the geometric shifts present in the data are not usually the same at the burst boundaries. This might introduce small inconsistencies in a mosaic product if not properly handled, which is especially inconve-nient in high precision applications. This paper proposes a method to account for this effect by resampling the bursts before stitching. The method is validated with experiments on real Sentinel-1 data.

Shape from shading (SfS) enables 3D reconstruction of stockpiles from a single image. However, this method requires proper boundary conditions to work properly. Obtaining such Dirichlet and Neumann conditions is equivalent to a segmentation of the heaps. To get a fast and accurate 3D reconstruction, we propose a simple and interactive segmentation method. SfS is then applied on 0.5-meter resolution Synthetic Aperture Radar (SAR) images with more precise boundary conditions. The results show that prior segmentation is preferable to no segmentation for the stockpiles volume estimation problem. Furthermore, we show that the proposed interactive segmentation method reduces the annotation time needed for such a prior segmentation

In this study, we compare the different spectral centering methods (referred to as deramping) for the Sentinel-1 images acquired with the TOPSAR method, in the context of interferometry. The deramping is a necessary step prior to image interpolation. We show the analogy between two different approaches in the literature and propose our own improvements. The proposed deramping method approximately re-centers the spectrum, i.e. a small residual spectral shift remains. We validate our improvements with experiments on Sentinel-1 data, and show that interpolating the images containing this residual spectral shift should not induce considerable errors.

The Rational Polynomial Camera (RPC) model can be used to describe a variety of image acquisition systems in remote sensing, notably optical and Synthetic Aperture Radar (SAR) sensors. RPC functions relate 3D to 2D coordinates and vice versa, regardless of physical sensor specificities, which has made them an essential tool to harness satellite images in a generic way. This article describes a terrain-independent algorithm to accurately derive a RPC model from a set of 3D-2D point correspondences based on a regularized least squares fit. The performance of the method is assessed by varying the point correspondences and the size of the area that they cover. We test the algorithm on SAR and optical data, to derive RPCs from physical sensor models or from other RPC models after composition with corrective functions.

The recent proliferation of constellations of recurrent satellites enables the constitution of temporally dense times series of registered images. We therefore propose in this paper a more in depth detection and analysis of observable changes. This approach is intended to be generic and independent of the type of satellite used, whether band limited or multispectral. It is based on a global analysis of the sequence. The detection stage is based on the definition of a residual sequence calculated from the novelty filter. A statistical approach based on the NFA test is then employed to detect significant changes. We then use these detections to classify the changes according to their nature: unique or lasting. To establish the efficiency of the method, we created an open dataset of 28 sequences of 20 images acquired by Sentinel-2 in different regions of the world. We obtain satisfactory results which are consistent with the visual observations of experts.

Global water storage monitoring is often done using remote sensing to solve the problem of missing gauge station. Moreover, this information is very often not available to the public even when a measure station is available. The methods usually used are however often unpractical for small lakes, often require images that are costly or difficult to acquire (for example SAR images of sufficient resolution) or with a bad revisit time. In this paper we propose an automatic method for visible satellite images based on a precise tracking of shoreline. It uses bathymetry information of the lake when available but we also present different alternatives that still produce precise estimations. The method is applied to accurately track the water volume of lake of La Bultière in 2019 using only PlanetScope images.

Image registration is a fundamental tool of remote sensing. The recent proliferatio of earth observation satellites has opened the way to the analysis of long image time series with denser temporal repetition. Given this wealth of images, it is crucial to design automatic tools to process them. We thus propose a method for the global registration of satellite image time series, that leverages their redundancy to improve in precision and robustness. By computing the relative displacement for all possible pairs of images, we are able to discard outliers and minimize the number of misaligned images. Experiments on synthetic data show that longer image series are registered with a higher precision.

This paper proposes a system for automatic surface volume monitoring from time series of SkySat pushframe imagery. A specific challenge of building and comparing large 3D models from SkySat data is to correct inconsistencies between the camera models associated to the multiple views that are necessary to cover the area at a given time, where these camera models are represented as Rational Polynomial Cameras (RPCs). We address the problem by proposing a date-wise RPC refinement, able to handle dynamic areas covered by sets of partially overlapping views. The cameras are refined by means of a rotation that compensates for errors due to inaccurate knowledge of the satellite attitude. The refined RPCs are then used to reconstruct multiple consistent Digital Surface Models (DSMs) from different stereo pairs at each date. RPC refinement strengthens the consistency between the DSMs of each date, which is extremely beneficial to accurately measure volumes in the 3D surface models. The system is tested in a real case scenario, to monitor large coal stockpiles. Our volume estimates are validated with measurements collected on site in the same period of time.

Reducing methane emissions is essential to tackle climate change. Here, we address the problem of detecting large methane leaks by using hyperspectral data from the satellite Sentinel-5P. By sampling Sentinel-5P spectral data at fine scale, we detect methane absorption features in the shortwave infrared wavelength range (SWIR). Our method involves two separate steps: i) background subtraction and ii) detection of local maxima in the negative logarithmic spectrum of each pixel. In the first step, we remove the impact of the albedo using albedo maps and the impact of the atmosphere by using a principal component analysis (PCA) over a time series of past observations. In the second step, we count for each pixel the number of local maxima that correspond to a subset of local maxima in the methane absorption spectrum. This counting method allows us to set up a statistical a contrario test that controls the false alarm rate of our detections.

Cloud detection is a crucial step for automatic satellite image analysis. Some cloud detection methods exploit specially designed spectral bands, other base the detection on time series, or on the inter-band delay in push-broom satellites. Nevertheless many use cases occur where these methods do not apply. This paper describes a convolutional neural network for cloud detection in panchromatic and single-frame images. Only a per-image annotation is required, indicating which images contain clouds and which are cloud-free. Our experiments show that, in spite of using less information, the proposed method produces competitive results.

Using optical satellite images requires detecting accurately all clouds in any image. For many applications, automatic cloud detection methods are not accurate enough. We describe here a fast machine learning based annotation system and demonstrate on Sentinel-2 images its efficacy to reach in four clicks or less a more than 95% accurate cloud detector. To obtain these statistics, we constructed an eclectic database of partially cloudy images and its ground truth, and evaluated its accuracy to be larger than 98%. We then show that our fast supervised annotation is far more accurate than recent sophisticated cloud detectors.

New micro-satellite constellations enable unprecedented systematic monitoring applications thanks to their wide coverage and short revisit capabilities. However, the large volumes of images that they produce have uneven qualities, creating the need for automatic quality assessment methods. In this work, we quantify the sharpness of images from the PlanetScope constellation by estimating the blur kernel from each image. Once the kernel has been estimated, it is possible to compute an absolute measure of sharpness which allows to discard low quality images and deconvolve blurry images before any further processing. The method is fully blind and automatic, and since it does not require the knowledge of any satellite specifications it can be ported to other constellations.

The evolution of the antarctic ice cap is a subject of the utmost importance for the climate science. For this reason exploiting the historic archive of SPOT 5 HRS binocular stereo imagery over the Antarctica has gained interest. However, estimating surface models of Antarctica from optical satellite imagery is a challenging task. Different factors contribute to its difficulty: the reduced contrast of the snow, the abrupt changes in elevation, and the persistent cloud cover that is often indistinguishable from the snowy ground, or is translucent, or projects shadows on the ground; all of them hinder stereo matching. For these reasons the direct application of existing satellite stereo pipelines on these images often yields unsatisfactory results. In this study we explore strategies to address these problems and improve 3D modeling on these regions. We adapt the S2P [1] pipeline and incorporate a new multiscale strategy that allows to deal with incorrect geometry estimation due to clouds or lack of texture. In addition, integrating existing very low resolution DSM (1 km per pixel) of the Antarctica allows to further filter the model to produce a cleaner 3D model.

This article addresses the problem of estimating scene visibility in time series of satellite images. We are especially focused on satellites with few spectral bands and high revisit frequency. Our approach exploits the redundancy of information acquired during these revisits. It is based on an unsupervised algorithm that tracks local ground textures across time and detects ruptures caused by opaque clouds, haze, cirrus and shadows. Experiments have been carried out on 18 PlanetScope time series covering various locations. These time series come with hand-made labeled ground truth. We compare our results with those of the PlanetScope algorithm and demonstrate the effectiveness of the proposed method : success rates of 94% and 84% are reached for the visible and occulted regions classification.

These last years, earth observation imagery has significantly improved. Public satellites such as WorldView-3 can now produce images with a Ground Sample Distance of 31cm, reaching a resolution equivalent to aerial images. Perhaps more importantly, the revisit frequency has also been greatly enhanced: providers such as Planet can now acquire images of a given ground location on a daily basis. These major improvements are fueled by an increasing demand for frequent objects detection. An application generating a particular interest is vehicle detection. Indeed, vehicle detection can give to public and private actors valuable data such as traffic monitoring and parking occupancy rate estimations. Several datasets, such as DOTA or VehSat, already exist, allowing researchers to train machine learning algorithms to detect vehicles. However, these datasets focus on relatively high definition and expensive aerial and satellite images. In this paper, we will present a method for detecting vehicles on medium resolution satellite images, with a GSD comprised between 1 and 5 meters. This approach can notably be used on Planet images, allowing to monitor traffic of an area on a daily basis.

International audience

Image stereo pairs obtained from pinhole cameras can be stereo-rectified, thus permitting to test and use the many standard stereo matching algorithms of the literature. Yet, it is well-known that pushbroom Earth observation satellites produce image pairs that are not stereo-rectifiable. Nevertheless, we show that by a new and adequate use of the satellite calibration data, one can perform a precise local stereo-rectification of large Earth images. Based on this we built a fully automatic 3D reconstruction chain for the new Pléiades Earth observation satellite. It produces 1/10 pixel accurate Earth image stereo pairs at a high resolution. Examples will be made available online to the computer vision community.

Modern Earth observation satellites are calibrated in such a way that a point on the ground can be located with an error of just a few pixels in the image domain. For many applications this error can be ignored, but this is not the case for stereo reconstruction, that requires sub-pixel accuracy. In this article we propose a method to correct this error. The method works by estimating local corrections that compensate the error relative to a reference image. The proposed method does not rely on ground control points, but only on the relative consistency of the image contents. We validate our method with Pléiades and WorldView-1 images on a representative set of geographic sites.