Imagery classification for autonomous ground vehicle mobility in cold weather environments

Autonomous ground vehicle (AGV) research for military applications is important for developing ways to remove soldiers from harm’s way. Current AGV research tends toward operations in warm climates and this leaves the vehicle at risk of failing in cold climates. To ensure AGVs can fulfill a military vehicle’s role of being able to operate on- or off-road in all conditions, consideration needs to be given to terrain of all types to inform the on-board machine learning algorithms. This research aims to correlate real-time vehicle performance data with snow and ice surfaces derived from multispectral imagery with the goal of aiding in the development of a truly all-terrain AGV. Using the image data that correlated most closely to vehicle performance the images were classified into terrain units of most interest to mobility. The best image classification results were obtained when using Short Wave InfraRed (SWIR) band values and a supervised classification scheme, resulting in over 95% accuracy.

Solar Contamination on HIRAS Cold Calibration View and the Corrected Radiance Assessment

The deep-space (DS) view spectra are used as a cold reference to calibrate the Hyperspectral Infrared Atmospheric Sounder (HIRAS) Earth scene (ES) observations. The DS spectra stability in the moving average window is crucial to the calibration accuracy of ES radiances. While in the winter and spring seasons, the HIRAS detector-3 DS view is susceptible to solar stray light intrusion when the satellite flies towards the tail of every descending orbit, and as a result, the measured DS spectra are contaminated by the stray light pseudo spectra, especially in the short-wave infrared (SWIR) band. The solar light intrusion issue was addressed on 13 December 2019 when the DS view angle of the scene selection mirror (SSM) was adjusted from −77.4° to −87°. As for the historic contaminated data, a correction method is applied to detect the anomalous data by checking the continuity of the DS spectra and then replace them with the proximate normal ones. The historic ES observations are recalibrated after the contaminated DS spectra correction. The effect of the correction is assessed by comparing the recalibrated HIRAS radiances with those measured by the Cross-track Infrared Sounder onboard the Suomi National Polar-orbiting Partnership Satellite (SNPP/CrIS) via the extended simultaneous nadir overpasses (SNOx) technique and by checking the consistency among the radiance data from different HIRAS detectors. The results show that the large biases of the radiance brightness temperature (BT) caused by the contamination are ameliorated greatly to the levels observed in the normal conditions.

A new glacier inventory for Svalbard from Sentinel-2 and Landsat 8 for improved calculation of climate change impacts

<p><span>Svalbard is famous for its numerous surge-type glaciers as well as for the harsh weather conditions of a highly maritime Arctic island, making regular observations of its glaciers challenging. However, the rapid changes of glacier geometry require a frequent update of their extent to perform accurate glacier-specific calculations such as their mass balance or contribution to sea level. The last inventory for Svalbard has been compiled by Nuth et al. (2013) from about 40 satellite scenes acquired by three different sensors (ASTER, Landsat, SPOT) on 30 unique days over a period of 10 years. Accordingly, any change assessment or other time dependent calculations are difficult to perform and a temporarily more consistent dataset is urgently required.</span></p><p><span>In this study we present the results of a new glacier inventory for Svalbard that has been derived from two Sentinel-2 swaths acquired for the main island within 3 days of 2017 and on 1 day in 2016 from Landsat 8 for Nordaustlandet. The images had overall very good snow conditions but in some regions late seasonal snow was hiding glaciers. Glacier mapping under local clouds in the very north and south could be performed by using further scenes from 2017 processed with GEE. We applied a simple red/SWIR band ratio to map clean ice and corrected wrong classifications (sea ice, lakes) or missing parts (debris cover) manually. New drainage divides and topographic parameters were derived from the ArcticDEM. </span></p><p>The new inventory counts 3136 glaciers >0.01 km<sup>2</sup> covering an area of 32,948 km<sup>2</sup>. Of these, glaciers < 1 km<sup>2</sup> cover 1.3% of the area but nearly 44% of the number whereas glac-iers >10 km<sup>2</sup> cover 91% of the area and 10% by number. Compared to the previous inventory we have 1468 glaciers more and 2.5% area less. However, when excluding the 2025 glaciers <1 km<sup>2</sup>, we only identified 1111 glaciers, i.e. 557 less than in the previous inventory. The differences are mostly due to newly considered entities, different drainage divides, glacier retreat and advance/surging. By excluding surge-type glaciers, a more meaningful determination of climate-related area changes can be performed. The presentation will discuss the differences of the new inventory to the RGI dataset, the specific glacier mapping challenges and our approach to solve them.</p>

Road Feature Extraction from LANDSAT-8 and ResourceSat-2 Images

This paper presents a methodology of road feature extraction from the different resolutions of Remote Sensing images of Landsat-8 Operational Lander Image (OLI) and ResourceSat-2 of Linear Imaging Self Sensor-3 (LISS-3) and LISS-4 sensors with the spatial resolutions of 15 m, 24 m, and 5 m. In the methodology of road extraction, an index is proposed based on the spectral profile of Roads, also involving Morphological transform (Top-Hat or Bot-Hat) and Markov Random Fields (MRF). In the proposed index, Short Wave Infrared (SWIR) band has a significant role in the detection of roads from sensors, and it is named Normalized Difference Road Index (NDRI). To enhancement of features from the index, Bot-Hat transforms used. To segment the road features from this image, MRF used. The methodology is performed on the OLI, LISS-3 and LISS-4 images, and presented with results.

A high-performance short-wave infrared phototransistor based on a 2D tellurium/MoS2 van der Waals heterojunction

A gate-tunable Te/MoS2 vdW heterostructure is fabricated, exhibiting favourable photodetection properties with a response spectrum covering the whole SWIR band.

Sharpening of Worldview-3 Satellite Images by Generating Optimal High-Spatial-Resolution Images

Compared to using images in the visible and near-infrared (VNIR) wavelength range only, remotely sensed satellite imagery from the spectral wavelengths of both VNIR and shortwave infrared (SWIR), such as Sentinel-2A and Worldview-3, is more effective for analyzing various types of information for tasks such as land cover mapping, environmental monitoring and land use change detection. In this manuscript, a new sharpening technique to enhance the spatial resolution of Worldview-3 satellite imagery with various spatial and spectral resolutions is proposed. Selected and synthesized band schemes were used to produce optimal panchromatic images; then, sharpened images were generated by applying the Gram-Schmidt adaptive (GSA) and Gram-Schmidt 2 (GS2) techniques, which are component substitution (CS)- and multiresolution analysis (MRA)-based algorithms, respectively. In addition, to minimize the spectral distortion of the initial sharpened image, a postprocessing methodology for spectral distortion reduction was developed. Qualitative and quantitative evaluation of the sharpened images showed that the pansharpening performance using the GS2 technique based on the selected band scheme and spectral distortion reduction was the best. To confirm the usability of the SWIR band, supervised classification based on machine learning was performed on the pansharpened images obtained by applying the technique proposed in this study and on the pansharpened images obtained by the VNIR bands only. The classification accuracy of the results using SWIR bands was higher than that of VNIR bands only. In particular, it was confirmed that the accuracy of the classification of artificial facilities known to be effective for SWIR bands was greatly improved.

In situ measurement of CO₂ and CH₄ from aircraft over northeast China and comparison with OCO-2 data

Several satellites have been launched to monitor the increasing concentrations of greenhouse gases, especially CO2 and CH4 in the atmosphere, through backscattered hyperspectral radiance in the shortwave infrared (SWIR) band. The vertical profiles of greenhouse gases and aerosols could strongly affect the results from these instruments. To investigate the effects of the vertical distribution of CO2 on the uncertainty of SWIR satellite retrieval results, we conducted observations of the vertical profiles of CO2, CH4 and aerosol particles at 0.6–7 km above sea level using a Beechcraft King Air 350ER in Jiansanjiang (46.77∘ N, 131.99∘ E), Heilongjiang Province, northeast China, on 7–12 August 2018. The profiles from this aircraft captured a decrease in CO2 from 2 km to the minimum altitude due to the absorption of vegetation at the surface in summer. CH4 measurements showed about a 0.2 ppm increase from 2.0 to 0.6 km on 10 August, which may result from emissions from the large area of paddy fields below, and a constant mole fraction between 1.951 and 1.976 ppm was recorded at 2 km and above. Comparison of CO2 profiles from a new version of the carbon cycle data assimilation system Tan-Tracker (v1), retrievals from OCO-2 and aircraft measurements was conducted. The results from OCO-2 and the assimilation model system Tan-Tracker captured the vertical structure of CO2 above 3 km, whereas below 3 km the values from OCO-2 and the Tan-Tracker model were lower than those from in situ measurements. Column-averaged CO2 volume mole fractions calculated from in situ measurements showed biases of -4.68±0.44 ppm (-1.18±0.11%) compared to OCO-2 retrievals.

Fire Blight Disease Detection for Apple Trees: Hyperspectral Analysis of Healthy, Infected and Dry Leaves

The effective and rapid detection of Fire Blight, an important bacterial disease caused by the quarantine pest E.amylovora, is crucial for today’s horticulture. This study explored the application of non-invasive proximal hyperspectral remote sensing (RS) in order to differentiate the healthy (H), infected (I) and dry (D) leaves of apple trees. Analysis of variance was employed in order to determine which hyperspectral narrow spectral bands exhibited the most significant differences. Spectral signatures for the range of 400–2500 nm were acquired with Thermo Scientific Evolution 220 and iS50NIR spectrometers. The selected spectral bands were then used to evaluate several RS indices, including ARI (Anthocyanin Reflectance Index), RDVI (Renormalized Difference Vegetation Index), MSR (Modified Simple Ratio) and NRI (Nitrogen Reflectance Index), for Fire Blight detection in apple tree leaves. Furthermore, a new index was proposed, namely QFI. The spectral indices were tested on apple trees infected by Fire Blight in a quarantine greenhouse. Results indicated that the short-wavelength infrared (SWIR) band located at 1450 nm was able to distinguish (I) and (H) leaves, while the SWIR band at 1900 nm differentiated all three leaf types. Moreover, tests using the Pearson correlation indicated that ARI, MSR and QFI exhibited the highest correlations with the infection progress. Our results prove that our hyperspectral remote sensing technique is able to differentiate (H), (I) and (D) leaves of apple trees for the reliable and precise detection of Fire Blight.

Sentinel 5 Precursor: Status of TROPOMI and the Operational Data Products

<p>The Copernicus Sentinel 5 Precursor (S5P) is the first of the Sentinel satellites dedicated to the observation of the atmospheric composition, for climate, air quality and ozone monitoring applications. The payload of S5P is TROPOMI (TROPOspheric Monitoring Instrument), a spectrometer covering spectral bands in ultraviolet, visible, near infrared and shortwave infrared, which was developed by The Netherlands in cooperation with the European Space Agency (ESA). TROPOMI has a wide swath of 2600 km, enabling daily global coverage, in combination with a high spatial resolution of about 3.5 x 5.5 km<sup>2</sup> (7 x 5.5 km<sup>2</sup> for the SWIR band).</p><p>S5P was successfully launched on 13 October 2017 and following a six-month commissioning phase, the operational data stream started at the end of April 2018. All of the TROPOMI operational data products have been released, with the exception of the ozone profile, which is planned to become available with the next major release[AR1]  of the Level 1B data. In addition to the operational data products, new research products are also being developed.</p><p>In this contribution, the status of TROPOMI and its data products will be presented. Results for observations of recent events will be provided, along with an outlook on the next release of the data products.</p><div> <div> <div> </div> </div> </div>

Modelling and impact of the turbulence effect on flash and cumulative 2D active imaging system

In the topic of 2D active imaging systems, two technologies exist for image acquisition. The flash mode consists of a very short and intense laser shot, associated with a short time integration of the sensor (range of hundreds of nanoseconds). The second is the cumulative mode which consists of the integration of many low level energy laser pulses over a long-time integration of the sensor (range of tens of milliseconds). Cumulative mode systems have existed for a long time in the near infrared band. But for the past few years, new sensors are available in the short wave infrared (SWIR) band. Cumulative mode in the SWIR band can provide 2D active imaging systems with very low risk considering the eye safety aspects. Moreover, with a similar design, cumulative systems can overcome the range of flash systems, thanks to their ability to average turbulence effects over the sensor integration time. So, in this paper we have proposed a scintillation noise comparison for each mode. First, we exposed the two types of available models, a numerical model, used for image generation. Second one, an analytical model, used for a quick evaluation of the design of a 2D active imaging system. Both models were compared, especially in their area of validity. Then for a specific scenario, we estimated the gain in term of range performance between a cumulative and a flash system.