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.

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.

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.

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%.

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.

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.

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.

Exposure Fusion is a high dynamic range imaging technique fusing a bracketed exposure sequence into a high quality image. In this paper, we provide a refined version resolving its out-of-range artifact and its low-frequency halo. It improves on the original Exposure Fusion by augmenting contrast in all image parts. Furthermore, we extend this algorithm to single exposure images, thereby turning it into a competitive contrast enhancement operator. To do so, bracketed images are first simulated from a single input image and then fused by the new version of Exposure Fusion. The resulting algorithm competes with state of the art image enhancement methods.