Cloud masking is of central importance to the Earth Observation community. This paper deals with the problem of detecting clouds in visible and multispectral imagery from high-resolution satellite cameras. Recently, Machine Learning has offered promising solutions to the problem of cloud masking, allowing for more flexibility than traditional thresholding techniques, which are restricted to instruments with the requisite spectral bands. However, few studies use multi-scale features (as in, a combination of pixel-level and spatial) whilst also offering compelling experimental evidence for real-world performance. Therefore, we introduce CloudFCN, based on a Fully Convolutional Network architecture, known as U-net, which has become a standard Deep Learning approach to image segmentation. It fuses the shallowest and deepest layers of the network, thus routing low-level visible content to its deepest layers. We offer an extensive range of experiments on this, including data from two high-resolution sensors—Carbonite-2 and Landsat 8—and several complementary tests. Owing to a variety of performance-enhancing design choices and training techniques, it exhibits state-of-the-art performance where comparable to other methods, high speed, and robustness to many different terrains and sensor types.
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We introduce a novel neural network architecture-Spectral ENcoder for SEnsor Independence (SEnSeI)-by which several multispectral instruments, each with different combinations of spectral bands, can be used to train a generalised deep learning model. We focus on the problem of cloud masking, using several pre-existing datasets, and a new, freely available dataset for Sentinel-2. Our model is shown to achieve state-of-the-art performance on the satellites it was trained on (Sentinel-2 and Landsat 8), and is able to extrapolate to sensors it has not seen during training such as Landsat 7, Per úSat-1, and Sentinel-3 SLSTR. Model performance is shown to improve when multiple satellites are used in training, approaching or surpassing the performance of specialised, single-sensor models. This work is motivated by the fact that the remote sensing community has access to data taken with a hugely variety of sensors. This has inevitably led to labelling efforts being undertaken separately for different sensors, which limits the performance of deep learning models, given their need for huge training sets to perform optimally. Sensor independence can enable deep learning models to utilise multiple datasets for training simultaneously, boosting performance and making them much more widely applicable. This may lead to deep learning approaches being used more frequently for on-board applications and in ground segment data processing, which generally require models to be ready at launch or soon afterwards.
We present here a dataset of nearly 5000 small craters across roughly 1700 km2 of the Martian surface, in the MC-11 East quadrangle. The dataset covers twelve 2000-by-2000 pixel Context Camera images, each of which is comprehensively labelled by six annotators, whose results are combined using agglomerative clustering. Crater size-frequency distributions are centrally important to the estimation of planetary surface ages, in lieu of in-situ sampling. Older surfaces are exposed to meteoritic impactors for longer and, thus, are more densely cratered. However, whilst populations of larger craters are well understood, the processes governing the production and erosion of small (sub-km) craters are more poorly constrained. We argue that, by surveying larger numbers of small craters, the planetary science community can reduce some of the current uncertainties regarding their production and erosion rates. To this end, many have sought to use state-of-the-art object detection techniques utilising Deep Learning, which—although powerful—require very large amounts of labelled training data to perform optimally. This survey gives researchers a large dataset to analyse small crater statistics over MC-11 East, and allows them to better train and validate their crater detection algorithms. The collection of these data also demonstrates a multi-annotator method for the labelling of many small objects, which produces an estimated confidence score for each annotation and annotator.
<div class="page" title="Page 2"> <div class="section"> <div class="layoutArea"> <div class="column"> <p>The Sentinel-3A and Sentinel-3B satellites, launched in February 2016 and April 2018 respectively, build on the legacy of CryoSat-2 by providing high-resolution radar altimetry data over the polar regions up to 81 degrees North. The combination of synthetic aperture radar (SAR) mode altimetry from Sentinel-3A and Sentinel-3B, and the Ocean and Land Colour Instrument (OLCI) imaging spectrometer, results in the creation of the first satellite platform that&#160;offers coincident optical imagery and SAR radar altimetry. We utilise these datasets to validate existing surface classification algorithms, in addition to investigating novel applications of deep learning to classify sea-ice from leads. This is important for estimating sea-ice thickness and to predict future changes in the Arctic and Antarctic regions. In particular, we propose the use of Vision Transformers (ViT) for this task and demonstrate their effectiveness, with accuracy reaching above 92%. We compare our automated results with human classification using the software IRIS.&#160;</p> </div> </div> </div> </div>
<p>A key uncertainty in our understanding of crater populations is the number and spatial distribution of secondary craters, which can contaminate and artificially increase the number of observed impacts, especially at sub-km diameter ranges [1]. Constraints to this unknown have been derived from two branches of work, or in combination. One is physical modelling, in which simulations and our understanding of impact process dynamics provide estimates of the size, number and trajectory of secondary impactors produced by the primary [2]. The other uses visuospatial studies, wherein experts attempt to identify and count craters thought to be secondaries, and use these to produce bounds on the number of secondaries produced by a specific primary crater [3], [4]. In this work, we propose a third approach which we hope to use to further quantify the extent of the secondary crater population on Mars, in a way that is as independent as possible of previous estimates.</p> <p>We propose to use spatial statistics and stochastic modelling across a large region of Mars in order to decouple the primary and secondary populations, using some simple assumptions about the nature of the two overlapping distributions. First, the primary distribution is---although locally random---constant over the region of interest. Second, the secondary crater population can be modelled as a 2-dimensional normal distribution emanating from each primary crater, with a standard deviation much smaller than the dimensions of the region of interest. With these assumptions, we can then model the total distribution with only a few parameters: The primary distribution, the number of secondaries produced by a primary of a given size, and the standard deviation of the secondary craters' distance from their parent.</p> <p>Given the importance of a uniform primary population for our method to work, we have selected Isidis Planitia as our study site. This large, flat plain is remarkable in its geological consistency, and is thought to be the basin of a large impact in the Noachian period. The surface we see today, excluding overlaid impact sites, is a single geological unit, with an estimated age of 2.8 Gy [5]. In total, it covers roughly 6 million km<sup>2</sup>.</p> <p>The huge quantity of craters required for a method such as this to be successful has necessitated the use of automated detection. We have refined the Mask R-CNN architecture [6]---widely used for instance segmentation tasks---in order to detect craters. Trained on a variety of different Martian crater datasets, we have used this model to produce the most numerous crater survey to date over the Isidis Planitia region of Mars (see Figure 1). With these data, we will attempt to use statistical analysis to empirically estimate the number of expected secondaries, and their spatial distribution, for a primary of a given size.</p> <p>We hope that our results will further constrain the processes governing the production of secondary craters. In the future, this method could be straightforwardly applied to other regions of Mars, as well as the Moon, to see how the atmosphere and a surface's geology affects secondary craters.</p> <p>&#160;</p> <p><img src="data:image/png;base64, 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
<p>In Song et al. (2021) [1] a framework for the retrieval of 10 m and 20 m spectral and 20 m broadband surface albedo products was described. This framework consists of four modules: 1) a machine learning based cloud detection method, Spectral ENcoder for SEnsor Independence (SEnSeI) [2]. 2) an advanced atmospheric correction model Sensor Invariant Atmospheric Correction (SIAC) [3]. 3) an endmember-based class extraction method, which enables the retrieval of 10 m/20 m albedos based on a regression between the MODIS Bidirectional Reflectance Distribution Function (BRDF) derived surface albedo and Sentinel-2 surface reflectance resampled to MODIS resolution. 4) a novel method of using the MODIS BRDF prior developed within the QA4ECV programme (http://www.qa4ecv.eu/) to fill in the gaps in a time series caused by cloud obscuration. We describe how ~1100 scenes were processed over 22 Sentinel-2 tiles at the STFC JASMIN facility. These tiles spanned different 4 month time periods for different users with a maximum of 22 dates per tile. These tiles cover Italy, Germany, South Africa, South Sudan, Ukraine and UK for 6 different users. For the Italian site, a detailed analysis was performed of the impact of this hr-albedo on the fAPAR and LAI derived using TIP [5] whilst a second user employed a method described in [6] to compare MODIS and Sentinel-2 and a third user looked at the impact on agricultural yield forecasting. Lessons learnt from these different applications will be described including both the opportunities and areas where further work is required to improve the data quality.</p><p>&#160;</p><p>We thank ESA for their support through ESA-HR-AlbedoMap: Contract CO 4000130413 and the STFC JASMIN facility and in particular Victoria Bennett for their assistance.</p><p>[1] Song, R., Muller, J.-P., Francis, A., A Method of Retrieving 10-m Spectral Surface Albedo Products from Sentinel-2 and MODIS data," 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, pp. 2381-2384, doi: 10.1109/IGARSS47720.2021.9554356</p><p>[2] Francis, A., Mrziglod, J., Sidiropoulos, P. &#160;and J.-P. Muller, "SEnSeI: A Deep Learning Module for Creating Sensor Independent Cloud Masks," in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2021.3128280.</p><p>[3] Feng et al. (2019) A Sensor Invariant Atmospheric Correction: Sentinel-2/MSI AND Landsat 8/OLI https://doi.org/10.31223/osf.io/ps957.</p><p>[4] Song, R.; Muller, J.-P.; Kharbouche, S.; Yin, F.; Woodgate, W.; Kitchen, M.; Roland, M.; Arriga, N.; Meyer, W.; Koerber, G.; Bonal, D.; Burban, B.; Knohl, A.; Siebicke, L.; Buysse, P.; Loubet, B.; Leonardo, M.; Lerebourg, C.; Gobron, N. Validation of Space-Based Albedo Products from Upscaled Tower-Based Measurements Over Heterogeneous and Homogeneous Landscapes. Remote Sensing 2020, 12, 1&#8211;23.doi: 10.3390/rs12050833</p><p>[5] Gobron, N.; Marioni, M.; Muller, J.-P.; Song, R.; Francis, A. M.; Feng, Y.; Lewis, P. <em>ESA Sentinel-2 Albedo Case Study: FAPAR and LAI downstream products.</em>; 2021; pp. 1&#8211;30. JRC TR (in press)</p><p>[6] Peng, J.; Kharbouche, S.; Muller, J.-P.; Danne, O.; Blessing, S.; Giering, R.; Gobron, N.; Ludwig, R.; Mueller, B.; Leng, G.; Lees, T.; Dadson, S. Influences of leaf area index and albedo on estimating energy fluxes with HOLAPS framework. <em>J Hydrol</em> <strong>2020</strong>, <em>580</em>, 124245.</p>
This study proposed a new method of retrieving 10m spectral surface albedo products. Three crucial components are incorporated into this high-resolution surface albedo generation system. Firstly, a deep learning system, CloudFCN based on the U-net paradigm has been developed. This produces the best available cloud detection of any algorithm published to date. Secondly, an advanced atmospheric correction model, the Sensor Invariant Atmospheric Correction (SIAC) is employed. The SIAC method considers the surface BRDF effects as these are usually ignored, because the atmosphere correction is a large signal and the largest uncertainty in converting topof-atmosphere reflectance to top-of-canopy surface reflectance. Thirdly, an endmember-based new technology will be used to retrieve high-resolution albedo from high-resolution reflectance by combining downscaled MODIS BRDF. These methods are further described alongside results shown of the different stages and the final high resolution albedo.
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