Super-resolution restoration of spaceborne HD videos using the UCL MAGiGAN system

We developed a novel SRR system, called Multi-Angle Gotcha image restoration with Generative Adversarial Network (MAGiGAN), to produce resolution enhancement of 3-5 times from multi-pass EO images. The MAGiGAN SRR system uses a combination of photogrammetric and machine vision approaches including image segmentation and shadow labelling, feature matching and densification, estimation of an image degradation model, and deep learning approaches, to retrieve image information from distorted features and training networks. We have tested the MAGiGAN SRR using the NVIDIA® Jetson TX-2 GPU card for onboard processing within a smart-satellite capturing high definition satellite videos, which will enable many innovative remote-sensing applications to be implemented in the future. In this paper, we show SRR processing results from a Planet® SkySat HD 70cm spaceborne video using a GPU version of the MAGiGAN system. Image quality and effective resolution enhancement are measured and discussed

Time series analysis of very slow landslides in the three Gorges region through small baseline SAR offset tracking

Sub-pixel offset tracking has been used in various applications, including measurements of glacier movement, earthquakes, landslides, etc., as a complementary method to time series InSAR. In this work, we explore the use of a small baseline subset (SBAS) Offset Tracking approach to monitor very slow landslides with centimetre-level annual displacement rate, and in challenging areas characterized by high humidity, dense vegetation cover, and steep slopes. This approach, herein referred to as SBAS Offset Tracking, is used to minimize temporal and spatial de -correlation in offset pairs, in order to achieve high density of reliable measurements. This approach is applied to a case study of the Tanjiahe landslide in the Three Gorges Region. Using the TerraSAR-X Staring Spotlight (TSX-ST) data, with sufficient density of observations, we estimate the precision of the SBAS offset tracking approach to be 2-3 cm on average. The results demonstrated accord well with corresponding GPS measurements

Automated stereo retrieval of smoke plume injection heights and retrieval of smoke plume masks from AATSR and assessment with CALIPSO and MISR.

The longevity and dispersion of smoke and asso- ciated chemical constituents released from wildfire events are dependent on several factors, crucially including the height at which the smoke is injected into the atmosphere. The aim here is to provide improved emission data for the initialization of chemical transport models in order to better predict aerosol and trace gas dispersion following injection into the free atmosphere. A new stereo-matching algorithm, named M6, which can effec- tively resolve smoke plume injection heights (SPIH), is presented here. M6 is extensively validated against two alternative space- borne earth observation SPIH data sources and demonstrates good agreement. Further, due to the spectral and dual-view configuration of the Advanced Along-Track Scanning Radiometer imaging system, it is possible to automatically differentiate smoke from other atmospheric features effectively—a feat, which currently no other algorithm can achieve. Additionally, as the M6 algorithm shares a heritage with the other M-series matchers, it is here compared against one of its predecessors, M4, which, for the determination of SPIH, M6 is shown to substantially outperform

Comparison between active sensor and radiosonde cloud boundaries over the ARM Southern Great Plains site

In order to test the strengths and limitations of cloud boundary retrievals from radiosonde profiles, 4 years of radar, lidar, and ceilometer data collected at the Atmospheric Radiation Measurements Southern Great Plains site from November 1996 through October 2000 are used to assess the retrievals of Wang and Rossow [1995] and Chernykh and Eskridge [1996]. The lidar and ceilometer data yield lowest-level cloud base heights that are, on average, within approximately 125 m of each other when both systems detect a cloud. These quantities are used to assess the accuracy of coincident cloud base heights obtained from radar and the two radiosonde-based methods applied to 200 m resolution profiles obtained at the same site. The lidar/ceilometer and radar cloud base heights agree by 0.156 ± 0.423 km for 85.27% of the observations, while the agreement between the lidar/ceilometer and radiosonde-derived heights is at best −0.044 ± 0.559 km for 74.60% of all cases. Agreement between radar- and radiosonde-derived cloud boundaries is better for cloud base height than for cloud top height, being at best 0.018 ± 0.641 km for 70.91% of the cloud base heights and 0.348 ± 0.729 km for 68.27% of the cloud top heights. The disagreements between radar- and radiosonde-derived boundaries are mainly caused by broken cloud situations when it is difficult to verify that drifting radiosondes and fixed active sensors are observing the same clouds. In the case of the radar the presence of clutter (e.g., vegetal particles or insects) can affect the measurements from the surface up to approximately 3–5 km, preventing comparisons with radiosonde-derived boundaries. Overall, Wang and Rossow [1995] tend to classify moist layers that are not clouds as clouds and both radiosonde techniques report high cloud top heights that are higher than the corresponding heights from radar

A search for polycyclic aromatic hydrocarbons over the Martian South Polar Residual Cap

We present our research on compositional mapping of the Martian South Polar Residual Cap (SPRC), especially the detection of organic signatures within the dust content of the ice, based on hyperspectral data analysis. The SPRC is the main region of interest for this investigation, because of the unique CO 2 ice sublimation features that cover the surface. These flat floored, circular depressions are highly dynamic, and we infer frequently expose dust particle s previously trapped within the ice during the wintertime. Here we identify suitable regions for potential dust exposure on the SPRC, and utilise data from the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) on board NASA's Mars Reconnaissance Orbiter (MRO) satellite to examine infrared spectra of dark regions assumed to be composed mainly of dust particles to establish their mineral composition, to eliminate the effects of ices on sub-pixel dusty features, and to look for signatures indicative of Polycyclic Aromatic Hydrocarbons (PAHs). Spectral mapping has identified compositional differences between depression rims and the majority of the SPRC and CRISM spectra have been corrected to minimise the influence of CO 2 ice. Whilst no conclusive evidence for PAHs has been found within the detectability limits of the CRISM instrument, depression rims are shown to have higher water content than regions of featureless ice, and there are possible indications of magnesium carbonate within the dark, dusty regions

CloudFCN: Accurate and robust cloud detection for satellite imagery with deep learning

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

3D stereo reconstruction: High resolution satellite video

Precise high-resolution Digital Elevation Models (DEMs) are essential for creation of terrain relief and associated terrain hazard area maps, urban land development, smart cities and in other applications. The 3D modelling system entitled the UCL Co-registration Ames Stereo Pipeline (ASP) Gotcha Optimised (CASP-GO) was demonstrated on stereo data of Mars to generate 3D models for around 20% of Martian surface using cloud computers which was reported in 2018. CASP-GO is an automated DEM/DTM processing chain for NASA Mars, lunar and Earth Observation data including Mars 6m Context Camera (CTX) and High Resolution Imaging Science Experiment (HiRISE) 25cm stereo-data as well as ASTER 18m stereo data acquired on the NASA EOS Terra platform. CASP-GO uses tie-point based multi- resolution image co-registration, combined with sub-pixel refinement and densification. It is based on a combination of the NASA ASP and an adaptive least squares cor- relation and region growing matcher called Gotcha (Gruen-Otto-Chau). CASP-GO was successfully applied to produce more than 5300 DTMs of Mars (http://www.i- Mars.eu/web-GIS). This work employs CASP-GO to obtain DEMs from high resolution Earth Observation (EO) satellite video system SSTL Carbonite-2. CASP- GO was modified to work with multi-view point-and-stare video data including subpixel fusion of point clouds. Multi-view stereo video data are distinguished from still image data by a richer amount of information and noisier water areas

A Method of Retrieving 10-m Spectral Surface Albedo Products from Sentinel-2 and MODIS data

This study proposed a new method of retrieving 10-m 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 top-of-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