Ground Processing Software Research Articles

HIERARCHICAL AERIAL TRIANGULATION OF OBLIQUE IMAGE DATA

Abstract. Oblique imaging sensor systems are becoming a standard for photogrammetric applications such as city modeling, with increasing need for frequently updated geospatial data in urban areas. The ground processing software has to consider and support this develop-ment, providing a highly performant workflow for projects that can consist of tens or hundreds of thousands of images in different view angles: nadir (vertical), left, right, backward, forward. This is a challenge also for the oblique aerial triangulation (AT), both the large number of images to process and the high amount of overlap in-between them, where more than 50 image measurements for a given ground point are common.At the same time, the large overlap even in a single view generally provides stable sub-blocks for each viewing direction and, therefore, allows for separating the oblique AT by view and performing a hierarchical approach: The nadir block is triangulated first, updating the exterior orientation parameters of all nadir images and providing adjusted ground points. This nadir reference can be used to tie the oblique views’ sub-blocks in subsequent AT runs to eventually provide adjusted exterior orientations for all images, with comparable accuracy to an integrated AT – but providing a significant performance gain. The hierarchical AT is a three-step approach: 1. adjust-ment of the nadir imagery, 2. adjustment of each oblique views independently by cardinal direction (East, West, North, South) while tying it to the nadir reference, and 3. computation of combined statistics and report.The paper details our AT approach and evaluates its parameterization for different oblique data sets, comparing results and performance to a fully integrated AT.

Microwave Radiometer RFI Detection Using Deep Learning

Radio frequency interference (RFI) is a risk for microwave radiometers due to their requirement of very high sensitivity. The Soil Moisture Active Passive (SMAP) mission has an aggressive approach to RFI detection and filtering using dedicated spaceflight hardware and ground processing software. As more sensors push to observe at larger bandwidths in unprotected or shared spectrum, RFI detection continues to be essential. This article presents a deep learning approach to RFI detection using SMAP spectrogram data as input images. The study utilizes the benefits of transfer learning to evaluate the viability of this method for RFI detection in microwave radiometers. The well-known pretrained convolutional neural networks, AlexNet, GoogleNet, and ResNet-101 were investigated. ResNet-101 provided the highest accuracy with respect to validation data (99%), while AlexNet exhibited the highest agreement with SMAP detection (92%).

Suomi NPP CrIS measurements, sensor data record algorithm, calibration and validation activities, and record data quality

AbstractThe Cross‐Track Infrared Sounder (CrIS) is a Fourier Transform Michelson interferometer instrument launched on board the Suomi National Polar‐Orbiting Partnership (Suomi NPP) satellite on 28 October 2011. CrIS provides measurements of Earth view interferograms in three infrared spectral bands at 30 cross‐track positions, each with a 3 × 3 array of field of views. The CrIS ground processing software transforms the measured interferograms into calibrated and geolocated spectra in the form of Sensor Data Records (SDRs) that cover spectral bands from 650 to 1095 cm−1, 1210 to 1750 cm−1, and 2155 to 2550 cm−1 with spectral resolutions of 0.625 cm−1, 1.25 cm−1, and 2.5 cm−1, respectively. During the time since launch a team of subject matter experts from government, academia, and industry has been engaged in postlaunch CrIS calibration and validation activities. The CrIS SDR product is defined by three validation stages: Beta, Provisional, and Validated. The product reached Beta and Provisional validation stages on 19 April 2012 and 31 January 2013, respectively. For Beta and Provisional SDR data, the estimated absolute spectral calibration uncertainty is less than 3 ppm in the long‐wave and midwave bands, and the estimated 3 sigma radiometric uncertainty for all Earth scenes is less than 0.3 K in the long‐wave band and less than 0.2 K in the midwave and short‐wave bands. The geolocation uncertainty for near nadir pixels is less than 0.4 km in the cross‐track and in‐track directions.

Modelling the spectral response of the Swift-XRT CCD camera: experience learnt from in-flight calibration

Context. Since its launch in November 2004, Swift has revolutionised our understanding of gamma-ray bursts. The X-ray telescope (XRT), one of the three instruments on board Swift, has played a key role in providing essential positions, timing, and spectroscopy of more than 300 GRB afterglows to date. Although Swift was designed to observe GRB afterglows with power-law spectra, Swift is spending an increasing fraction of its time observing more traditional X-ray sources, which have more complex spectra. Aims. The aim of this paper is a detailed description of the CCD response model used to compute the XRT RMFs (redistribution matrix files), the changes implemented to it based on measurements of celestial and on-board calibration sources, and current caveats in the RMFs for the spectral analysis of XRT data. Methods. The RMFs are computed via Monte-Carlo simulations based on a physical model describing the interaction of photons within the silicon bulk of the CCD detector. Results. We show that the XRT spectral response calibration was complicated by various energy offsets in photon counting (PC) and windowed timing (WT) modes related to the way the CCD is operated in orbit (variation in temperature during observations, contamination by optical light from the sunlit Earth and increase in charge transfer inefficiency). We describe how these effects can be corrected for in the ground processing software. We show that the low-energy response, the redistribution in spectra of absorbed sources, and the modelling of the line profile have been significantly improved since launch by introducing empirical corrections in our code when it was not possible to use a physical description. We note that the increase in CTI became noticeable in June 2006 (i.e. 14 months after launch), but the evidence of a more serious degradation in spectroscopic performance (line broadening and change in the low-energy response) due to large charge traps (i.e. faults in the Si crystal) became more significant after March 2007. We describe efforts to handle such changes in the spectral response. Finally, we show that the commanded increase in the substrate voltage from 0 to 6 V on 2007 August 30 reduced the dark current, enabling the collection of useful science data at higher CCD temperature (up to −50 ◦ C). We also briefly describe the plan to recalibrate the XRT response files at this new voltage. Conclusions. We show that the XRT spectral response is described well by the public response files for line and continuum spectra in the 0.3−10 keV band in both PC and WT modes.

Geolocation Error Analysis of the Special Sensor Microwave Imager/Sounder

Geolocation errors in excess of 20-30 km have been observed in the special sensor microwave imager/sounder (F-16 SSMIS) radiometer observations when compared with accurate global shoreline databases. Potential error sources include angular misalignment of the sensor spin axis with the spacecraft zenith, sample timing offsets, nonuniform spin rate, antenna deployment offsets, spacecraft ephemeris, and approximations of the geolocation algorithm in the Ground Data Processing Software. An analysis methodology is presented to automate the process of quantifying the geolocation errors rapidly in terms of partial derivatives of the radiometer data in the along-scan and along-track directions and is applied to the SSMIS data. Angular and time offsets are derived for SSMIS that reveal the root cause(s) of the geolocation errors, while yet unresolved, are systematic, correctable in the ground processing software, and may be reduced to less than 4-5 km (1-sigma).

Design and Evaluation of the First Special Sensor Microwave Imager/Sounder

The first Special Sensor Microwave Imager/Sounder (SSMIS) was launched in October 2003 aboard the Air Force Defense Meteorological Satellite Program (DMSP) F-16 Spacecraft. As originally conceived, the SSMIS integrates the imaging capabilities of the heritage DMSP conically scanning Special Sensor Microwave/Imager sensor with the cross-track microwave sounders Special Sensor Microwave Temperature and Special Sensor Microwave Humidity Sounder, SSM/T-2 into a single conically scanning 24-channel instrument with extended sounding capability to profile the mesosphere. As such, the SSMIS represents the most complex operational satellite passive microwave imager/sounding sensor flown while, at the same time, offering new and challenging capabilities associated with radiometer channels having common fields of view, uniform polarizations, and fixed spatial resolutions across the active scene scan sector. A comprehensive end-to-end calibration/validation (cal/val) of the first SSMIS initiated shortly after launch was conducted under joint sponsorship by the DMSP and the Navy Space and Warfare Systems Command. Herein, we provide an overview of the SSMIS instrument design, performance characteristics, and major cal/val results. Overall, the first SSMIS instrument exhibits remarkably stable radiometer sensitivities, meeting requirements with considerable margin while providing high-quality imagery for all channels. Two unanticipated radiometer calibration anomalies uncovered during the cal/val-sun intrusion into the warm-load calibration target and antenna reflector emissions-required significant attention during the cal/val program. In particular, the tasks of diagnosing the root cause(s) of these anomalies as well as the development of ground processing software algorithms to mitigate their impact on F-16 SSMIS and hardware fixes on future instruments necessitated the construction of extensive analysis and simulation tools. The lessons learned from the SSMIS cal/val and the associated analysis tools are expected to play an important role in the design and performance evaluation of future passive microwave imaging and sounding instruments as well as guiding the planning and development of future cal/val programs.

Radar signal simulation: Surface modeling with the Facet Method

A major finding in exploration of Mars is the indication that a large quantity of water has been present on Mars. To discover the distribution of this quantity of water, the Mars Express spacecraft is carrying a spaceborne radar sounder, called Mars Advance Radar for Subsurface and Ionospheric Sounding (MARSIS), in order to map Mars subsurface dielectric characteristics. The returned radar echoes from Mars will contain both surface and subsurface reflection components, but the part of the signal we are interested in is the reflected signal from subsurface layers. To retrieve these weak deep echoes from the radar signal, a signal processing algorithm needs to be developed. In this paper, we present a computationally efficient radar signal simulation based on the use of the Facet Method as a surface modeling scheme. This simulator will be used to validate the MARSIS ground processing software and to support the interpretation of MARSIS data. The first step in the simulation algorithm definition is to model the Mars surface. Many surface modeling methods have been developed and can be found in the literature, but considering the fact that Mars surface is very smooth, the modeling algorithm we have developed uses the Facet Method. In this paper, we show that the Facet Method is an efficient scheme for modeling a relatively smooth surface, such as the Mars' surface. The instrument simulation we define makes strict reference to the MARSIS radar parameters; however, it may be used to model any radar sounder.