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  • Open access
  • 142 Reads
Design of a Lebanese Cube Satellite

Nanosatellites have drawn significant attention in research communities and industrial sectors due to their small size, ease of deployment, and relatively short developmental period. CubeSat specifications have been suggested as an effort to standardize nanosatellite mission design. Standardization opens the door for inter-CubeSat communications that can be used to form a CubeSat Cloud and mimic regular large multifunctional satellites with wide range of features, measurements and sensing capabilities.

Lebanon and many Developing countries have not been involved in satellite/nanosatellite design and launch. This research paper will first present a Comprehensive CubeSat (CoCube) online database. CoCube database is created by collecting information from various resources including currently available databases, published research papers and disseminated information about various CubeSat missions.

Based on the lessons learned from comparing various CubeSat design alternatives and components’ structure and analyzing the best practices of CubeSat development, LibanSAT design is introduced. LibanSAT is a 1U CubeSat that serves two main objectives: (i) greenhouse gases observation and (ii) educational purposes. LibanSAT is designed based on existing Components Off-The-Shelf (COTS). The rapid growth in the production of ready to use COTS subsystem made missions’ design faster, simpler and more effective. We benchmarked subsystems from various suppliers including ISIS-innovative solutions in Space, Nano avionics, Clyde-space, Gomspace and Pumpkins CubeSat kit among others and chose the most suitable products for our target mission based on cost, size, weight and power consumption.

Lebanon signed the Paris Agreement of climate change in April 2016 where greenhouse gases are identified as the main critical factor causing global warming. LibanSAT mission falls under the greenhouse gases observing satellite category, also known as GoSat. The most abundant greenhouse gases in Earth’s atmosphere can be clearly observed in the near-infrared band. As a result, the proposed spectrometer to be used as a payload is the Argus 1000 infrared spectrometer that is currently being tested as a payload in Canx2 and AlbertaSat-1 missions.

LibanSAT project serves also an educational purpose to raise awareness about the importance of space research among Lebanese academic institutions (universities and research centers). Likewise, the development of this type of technology will ensure the progress of our country and can open the opportunity for other Lebanese universities to perform space science and exploration. Future plans include designing an educational prototype that serves as a classroom CubeSat where all subsystems can be introduced for university students.

Finally, we shed special focus on communication security subsystem during LibanSAT design process. Due to the limitation in CubeSat weight, volume and power, we propose the use of a gateway mission which can be also used to route data between CubeSat Cloud nodes. The gateway is used as a firewall where all communication between ground stations and the Cloud will be handled by this central threat management entity.

  • Open access
  • 48 Reads
The RADARSAT Constellation Mission in Support of Environmental Applications

The RADARSAT-1 (launched in 1995), RADARSAT-2 (launched in 2007) and the RADARSAT Constellation Mission (to be launched in late 2018) are three past, current, and future Synthetic Aperture Radar (SAR) space missions which consists the Canadian RADARSAT program. The RADARSAT Constellation Mission (RCM) is the evolution of the RADARSAT Program with the objective of ensuring data   continuity, improved operational use of SAR data and enhanced system reliability. Canada is developing the RCM using small satellites to further maximize the capability to carry out round-the-clock surveillance from space. The Canadian Space Agency (CSA), in collaboration with other government-of-Canada departments and Canadian industry, is leading the design, development and operation of the RCM to help addressing key priorities. The mission with its three identical satellites will provide average daily complete coverage of Canada’s land and oceans. The short revisit frequency of the mission (four day cycle) combined with accurate orbital control affords a range of applications that are based on regular collection of data and creation of composite images that highlight changes over time. The purpose of our presentation is to discuss the evolution of the RADARSAT program with an overview on the RCM and its characteristics and advancements over the previous SAR missions. However, emphasis will be given on the expected potential RCM will offer on various environmental applications, such as monitoring climate change, land use evolution, and human impacts on local environments. Examples include sea ice classification, wetland monitoring, oil spill detection, flood mapping, coastal erosion and others. Improvement addressing these applications is also expected given the advanced RCM SAR innovations, such as the availability of the compact polarization.

  • Open access
  • 46 Reads
Radiometric Calibration of RapidScat using GPM Microwave Imager

Due to it’s Non-Sun-synchornous orbit, RapidScat is the first scatterometer capable of measuring ocean vector winds over the full diurnal cycle istead of observing given location at the fixed time of day. Non-Sun-synchronous orbit enables also overlap with other satellite instruments that have been flying in Sun-synchronous orbits. Rapidscat covers range between ± 51.6  latitudes and was operated onboard the International Space Station between September 2014 and August 2016. This paper describes process that combines RapidScat’s active/passive mode, simultaneously measuring both the radar surface backscatter (active mode) and microwave emission from the system noise temperature (passive mode). This work presents the radiometric (passive mode) cross-calibration using the GPM Microwave Imager (GMI), to eliminate brightness temperature measurement biases between a pair of radiometer channels operating at slightly different frequencies and incidence angles. The GPM Microwave Imager (GMI) on the GPM Core satellite flies in a low inclination orbit, with conical-scanning dual-polarized beams. Since the RapidScat operates at 13.4 GHz and the closest GMI channel is 10.65 GHz. GMI Tb’s were normalized required before the calibration. The GMI brightness temperatures was translated using the radiative transfer model (RTM) to yield an equivalent Tb prior to direct comparison with RapidScat. Seasonal and systematic biases between two radiometers have been calculated for both polarizations as a function of geometry, atmospheric and ocean brightness temperature models. Calculated biases may be used for measurement correction and reprocessing.  

  • Open access
  • 82 Reads
Road Extraction from High Resolution Image with Deep Convolution Network – A Case Study of GF-2 Image
Published: 22 March 2018 by MDPI AG in 2nd International Electronic Conference on Remote Sensing session Big Data Handling

Recently, with the development of remote sensing and computer techniques, automatic extraction and update of road information is becoming true. Remote sensing images can not only provide the surface information in large areas, but also save the costs in human resources, materials and time. Additionally, the update cycle of road network is now much shorter than traditional method. Nowadays, accurate extraction of road information from satellite data has become one of the most popular topics in both remotes sensing and transportation fields. However, as there is usually huge information provided by remote sensing data, an efficient and accurate method to refine the data and extract the road is thus important in real applications, such as the road update and emergency rescue decision making. By combining the deep convolution network and image segmentation approach, this paper proposed a new solution for extraction road network from high resolution images. For doing this, a road class table was built according to the road design and construction specifications that made by transportation industry. Following that, the ownership probability of different road classes should be predicted through deep convolutional network at pixel-level and the corresponding task was then regarded as a specific image segmentation. In addition, to make the extracted road segments more realistic, a post-processing approach was also used to modify, connect and smooth the road segments. Experiments in this paper showed that, the proposed solution successfully distinct multi-type roads from complex situations. Furthermore, a more realistic road map could also be provided with a total accuracy of more than 80% in discriminable areas.

  • Open access
  • 55 Reads
Determining Relative Errors of Satellite Precipitation Data over The Netherlands
Published: 22 March 2018 by MDPI AG in 2nd International Electronic Conference on Remote Sensing session Big Data Handling

Satellite precipitation estimates data are widely used for a variety of studies, including the hydrologic and climate modeling, weather forecasting, and agriculture management or extreme events prediction. However, satellite precipitation estimation is inevitably followed with errors which are caused by different factors, therefore it is essential to evaluate the relative errors of satellite precipitation data. A realizable method which can be used to quantify the relative errors in large-scale datasets is triple collocation. This method can objectively obtain the relative errors for at least three or more independent products. But before estimation of relative errors, the bias of the products relative to each other should be reduced or removed. This study tests the cumulative distribution function (CDF) matching approach which aims to reduce the bias among three precipitation products over the Netherlands. Afterwards, the triple collocation technique is applied to determine the relative errors of these precipitation products. The three precipitation datasets are, the Climate Prediction Center morphing method (CMORPH), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) and the gridded rain gauge data interpolated from in situ rain gauge measurement data provided by the Royal Netherlands Meteorological Institute (KNMI).

  • Open access
  • 58 Reads

Remote sensing of near real-time heavy precipitation using observations from GPM and MFG over India and nearby oceanic regions

This study deals with the integration of ingmerging of highly accurate precipitation estimates from Global Precipitation Measurement (GPM) with sampling gap-free satellite observations from Meteosat 7 of Meteosat First Generation (MFG) to develop a regional rainfall monitoring algorithm for monitoring precipitation over India and nearby oceanic regions. For this purpose, we derived precipitation signatures from Meteosat observations to co-locate it against precipitation from GPM. A relationship is then established between rainfall and rainfall signature using observations from various rainy seasons. The relationship thus derived can be used to monitor precipitation over India and nearby oceanic regions. Performance of this technique was tested against rain gauges and global precipitation products including the Global Satellite Mapping of Precipitation (GSMaP), Climate Prediction Centre MORPHing (CMORPH), Precipitation Estimation from Remote Sensing Information using Artificial Neural Network (PERSIANN) and Integrated Multi-satellitE Retrievals for GPM (IMERG). A case study is presented here to examine the performance of the developed algorithm for monitoring heavy rainfall during flood event of Tamil Nadu in 2015. The present algorithm shows a bias of -2.72, a Root Mean Square Error of 10.82, a Correlation Coefficient of 0.76 and a skill score of 0.36 when compared with IMD gridded rainfall product at 0.250

  • Open access
  • 32 Reads
Classification of Sentinel-2 Images Utilizing Abundances Representation

This paper deals with (both supervised and unsupervised) classification in Sentinel-2 images, utilizing the abundances representation of the pixels of interest. The latter pixel representation uncovers the hidden structured regions which are not available in the reference maps. Additionally, it reduces the dimensionality of the original spectral band space, it encourages data distinctions and bolsters accuracy. The proposed methodology involves two main stages: (I) the determination of the pixels abundance representation and (II) the employment of a classification algorithm applied on the abundance representations. More specifically, stage (I) incorporates two key processes namely: (a) endmember extraction utilizing spectrally homogeneous regions of interest (ROIs) and, (b) spectral unmixing, which hinges upon the endmember selection. The adopted spectral unmixing process assumes the Linear Mixing Model (LMM), where each pixel is expressed as a linear combination of the endmembers. The pixel’s abundance vector is estimated via a variational Bayes algorithm that is based on a suitably defined hierarchical Bayesian model. The resulting abundance vectors are then fed to stage (II) where two off-the-shelf supervised classification approaches (namely nearest neighbor (NN) classification and support vector machines (SVM)) as well as an unsupervised classification process (namely online adaptive possibilistic c-means (OAPCM) clustering algorithm), are adopted. Experiments are performed on a Sentinel-2 image acquired for a specific region of the Northern Pindos National Park in the northwestern Greece containing water, vegetation and soil areas. The experimental results demonstrate that the ad-hoc classification approaches utilizing the abundance representations of the pixels outperform the ones utilizing the spectral signatures of the pixels, in terms of accuracy.

  • Open access
  • 30 Reads
Application of Spectral Unmixing on Hyperspectral data of the Historic volcanic products of Mt. Etna (Italy).

   In this study, we focus on the identification of various volcanic products of Mt. Etna (eastern Sicily, Italy). Mt. Etna is one of the most active basaltic composite stratovolcanoes with the ability to change its land field rapidly, vigorously and continuously. For this purpose, NASA EO-1/Hyperion hyperspectral data (HSI) are used for lava flow differentiation and mapping within the historic “1536” to “1669” era of the Torre del Filosofo formation. These volcanic products are selected, due to a) their distinct spatial distribution, b) spectral similarity and c) field segregation from surrounding younger lavas. Due to their high compositional variability, the corresponding HSI pixels are mixed thus the problem is tackled with Spectral Unmixing (SU) techniques. First, endmember extraction, for each lava type, is performed by averaging over a Gaussian distribution of pixel reflectances of its associated region of interest (ROI). Then, abundances are estimated by two unmixing models: Constraint Linear Least Squares Unmixing (LLSU) and Bilinear Unmixing (BLU), applied on both spectral signatures and transformed versions of them, i.e. in the frequency domain through a Fourier Transform (FT). Historic lava flow delineation results are presented creating abundance maps per method. Also, a qualitative evaluation is performed using the Etna geological map, while a quantitative assessment is performed through derivation of the Structural Similarity Index (SSIM) of each implemented method. Ultimately, we address the degree of lava flow separability with the intercomparison of all methods and give an estimation of the computationally most efficient method. The specific research shows that HSIs provide useful information for individual lava flow differentiation and mapping in a complex environment such as Mt. Etna, despite limitations such as the relatively coarse pixel size, noisy bands and the sparse number of bibliographic references on lava spectral measurements.

  • Open access
  • 61 Reads
Sentinel-1 data border noise removal and seamless SAR mosaic generation

Space-borne SAR is the primary data source for operational monitoring and mapping of sea and lake ice at the Canadian Ice Service (CIS). In addition to RADARSAT-2, recently available Sentinel-1 A and B have provided more capability with enhanced revisit frequency and extended spatial coverage. Considering that the three-satellite RADARSAT Constellation Mission (RCM) will be launched in 2018, the CIS will be receiving hundreds of SAR images daily with almost a complete coverage of the CIS’ seasonal areas of interest. In order to efficiently use and analyze such a large amount and a wide areal extent of data, short-term (i.e. within a day) high-quality mosaic products are of interest. We have developed such sample mosaic products using Sentinel-1 and RADARSAT-2 data. However, it has been noted that there is a border noise issue inherent to Sentinel-1 data at image edges. Such noise needs to be removed before generating a seamless mosaic. Complicating matters further, the level of border noise varies scene to scene and sometimes the noise is even higher than that of valid data, so a simple threshold masking approach is not feasible. A method using line-by-line scanning and filtering is proposed, which traces an extreme jump between two neighboring pixels along a scan line. The results show this method locates and allows us to remove the noise precisely while retaining the rest of the valid data. Mosaicking SAR image frames or swaths acquired at different times, look directions, and observation angles is a challenge due to the scene-to-scene signal and tonal variations. For visual display, analysis, and interpretation – such as that done at the CIS – a tone-balanced smooth mosaic is of interest and value to ice analysts in displaying overall ice distribution and in viewing and comparing cross-region ice conditions. To address this, a scene boundary balancing method is developed to generate a seamless tone-balanced mosaic product. These short-term mosaic products can also be used as baseline data for further processing where raw data with absolute values are not critical, such as animation of a time series and calculation of macroscopic ice drift.

  • Open access
  • 52 Reads
Satellite-based identification of Aquaculture Farming using Neural Network Method over Coastal Areas around Bhitarkanika, Odisha

Aquaculture is the farming of fish, crustaceans, molluscus, aquatic plants, algae, and other aquatic organisms. Aquaculture farming in coastal areas of India plays key role in economy which contributes 1.07 % of GDP. It is the second largest in aquaculture production, which gives employment to 14.5 million people and foreign exchange earnings of US$ 3.51 billion from fishes and fisheries products. In Odisha, aquaculture system exports 26% of its products to foreign countries. Artificial neural networks have a feature of pattern recognition which uses training dataset to identify pattern of any feature from images. The term pattern recognition considers a wide range of information processing problems of great practical significance. After identifying the patterns it can be used to identify similar patterns from other images.  This study have been carried over two districts namely, Bhadrak and Kendrapada in Odisha. Here, Landsat-8 satellite data (OLI sensor) has been used and training sites have been collected. In this study, pattern recognition feature of neural network have been used to extract aquaculture features from satellite image. Further, we have analyzed the area that have been converted from agriculture to aquaculture from 2002 to 2017 using neural network classification. The details and results will be presented at the conference.  

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