Search Results (18)

The rapid development of nanosatellite technologies, their low development cost, and their economical launching due to their small size have made them an excellent option for Earth Observation (EO) and remote sensing. Nanosatellites are widely used in generic applications, such as education, vegetation monitoring, natural disasters, oceanography, and specialized applications, such as disaster response, and they serve as an Internet of Things (IoT) communications platform. This paper presents a review of the latest public nanosatellite EO missions, their applications, and their propulsion systems. Furthermore, we discuss specialized applications of the nanosatellites and their use in remote sensing for EO. Likewise, we aim to present the limitations of the nanosatellites in remote sensing, a comprehensive taxonomy according to propulsion systems, and directions for future research. Full article

The Planet SuperDove sensors produce eight-band, three-meter resolution images covering the blue, green, red, red-edge, and NIR spectral bands. Variations in spectral response in the data used to perform atmospheric correction combined with low signal-to-noise over ocean waters can lead to visible striping artifacts in the downstream ocean-color products. It was determined that the striping artifacts could be removed from these products by filtering the top of the atmosphere radiance in the red and NIR bands prior to selecting the aerosol models, without sacrificing high-resolution features in the imagery. This paper examines an approach that applies this filtering to the respective bands as a preprocessing step. The outcome and performance of this filtering technique are examined to assess the success of removing the striping effect in atmospherically corrected Planet SuperDove data. Full article

Nanosatellites increasingly produce more data than can be downlinked within a reasonable time due to their limited bandwidth and power. Therefore, an on-board system is required to prioritize scientifically significant data for downlinking, as described by scientists. This paper determines whether natural language processing can be used to prioritize remote sensing images on CubeSats with more flexibility compared to existing methods. Two approaches implementing the same conceptual prioritization pipeline are compared. The first uses YOLOv8 and Llama2 to extract image features and compare them with text descriptions via cosine similarity. The second approach employs CLIP, fine-tuned on remote sensing data, to achieve the same. Both approaches are evaluated on real nanosatellite hardware, the VERTECS Camera Control Board. The CLIP approach, particularly the ResNet50-based model, shows the best performance in prioritizing and sequencing remote sensing images. This paper demonstrates that on-orbit prioritization using natural language descriptions is viable and allows for more flexibility than existing methods. Full article

Nanosatellites are proliferating as low-cost, dedicated remote sensing opportunities for small nations. However, nanosatellites’ performance as remote sensing platforms is impaired by low downlink speeds, which typically range from 1200 to 9600 bps. Additionally, an estimated 67% of downloaded data are unusable for further applications due to excess cloud cover. To alleviate this issue, we propose an image segmentation and prioritization algorithm to classify and segment the contents of captured images onboard the nanosatellite. This algorithm prioritizes images with clear captures of water bodies and vegetated areas with high downlink priority. This in-orbit organization of images will aid ground station operators with downlinking images suitable for further ground-based remote sensing analysis. The proposed algorithm uses Convolutional Neural Network (CNN) models to classify and segment captured image data. In this study, we compare various model architectures and backbone designs for segmentation and assess their performance. The models are trained on a dataset that simulates captured data from nanosatellites and transferred to the satellite hardware to conduct inferences. Ground testing for the satellite has achieved a peak Mean IoU of 75% and an F1 Score of 0.85 for multi-class segmentation. The proposed algorithm is expected to improve data budget downlink efficiency by up to 42% based on validation testing. Full article

This paper presents findings from a spaceborne Earth observation experiment utilizing a novel, ultra-compact hyperspectral imaging camera aboard a 3U CubeSat. Leveraging the Offner optical scheme, the camera’s hyperspectrometer captures hyperspectral images of terrestrial regions with a 200 m spatial resolution and 12 nanometer spectral resolution across a 400 to 1000 nanometer wavelength range, covering 150 channels in the visible and near-infrared spectrums. The hyperspectrometer is specifically designed for deployment on a 3U CubeSat nanosatellite platform, featuring a robust all-metal cylindrical body of the hyperspectrometer, and a coaxial arrangement of the optical elements ensures optimal compactness and vibration stability. The performance of the imaging hyperspectrometer was rigorously evaluated through numerical simulations prior to construction. Analysis of hyperspectral data acquired over a year-long orbital operation demonstrates the 3U CubeSat’s ability to produce various vegetation indices, including the normalized difference vegetation index (NDVI). A comparative study with the European Space Agency’s Sentinel-2 L2A data shows a strong agreement at critical points, confirming the 3U CubeSat’s suitability for hyperspectral imaging in the visible and near-infrared spectrums. Notably, the ISOI 3U CubeSat can generate unique index images beyond the reach of Sentinel-2 L2A, underscoring its potential for advancing remote sensing applications. Full article

The quality of inland waters has a significant influence on human life and the functioning of the environment. The disasters that result from water pollution may cause major financial losses and lead to irreversible changes in the ecosystem, such as the dying out of endemic species of plants and animals. Quick detection of pollution sources may minimise those negative effects and reduce the costs of their elimination. The study presents a constellation design that provides imagery in the optic range and that might supplement the point water quality measurements that are conducted in situ. The area of interest was the southern catchment of the Baltic Sea and the main rivers in the region. The requirements for the designed mission were defined in reference to the remote sensing needs concerning the monitoring of water quality, the characteristics of the analysed area, and weather conditions. Based on these requirements, the Simera Sense MultiScape100 CIS sensor and the M6P nanosatellite manufactured by NanoAvionics were selected. The authors proposed a process for selecting the optimum orbit, taking into account the area of interest, the possibilities of the satellite platform, and of the sensor’s optics. As a result of the analyses, four concepts of creating a constellation were presented. Each constellation consisted of four nanosatellites. The designs were then subjected to performance analysis, considering the lighting limitations. Among the proposed systems, the constellation designed by the authors was distinguished; it used four orbital planes and achieved the coverage and availability of imagery in the time that was best suited to monitoring the waters. Thanks to a small number of platforms, the costs of the mission are relatively low, and it might significantly improve awareness of the current state of surface waters in the southern catchment of the Baltic Sea. Full article

Here we present a machine-learning-based method for utilizing traditional ocean-viewing satellites to perform automated atmospheric correction of nanosatellite data. These sensor convolution techniques are required because nanosatellites do not usually possess the wavelength combinations required to atmospherically correct upwelling radiance data for oceanographic applications; however, nanosatellites do provide superior ground-viewing spatial resolution (~3 m). Coincident multispectral data from the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (Suomi NPP VIIRS; referred to herein as “VIIRS”) were used to remove atmospheric contamination at each of the nanosatellite’s visible wavelengths to yield an estimate of spectral water-leaving radiance [L_w(l)], which is the basis for surface ocean optical products. Machine learning (ML) algorithms (KNN, decision tree regressors) were applied to determine relationships between L_w and top-of-atmosphere (L_t)/Rayleigh (L_r) radiances within VIIRS training data, and then applied to test cases for (1) the Marine Optical Buoy (MOBY) in Hawaii and (2) the AErosol RObotic Network Ocean Color (AERONET-OC), Venice, Italy. For the test cases examined, ML-based methods appeared to improve statistical results when compared to alternative dark spectrum fitting (DSF) methods. The results suggest that ML-based sensor convolution techniques offer a viable path forward for the oceanographic application of nanosatellite data streams. Full article

The second United Nations Sustainable Development Goal (SDG2), zero hunger, aims to improve the productivity, food security, nutrition, and sustainability of small-scale farmers. The fall armyworm (FAW, Spodoptera frugiperda) has been devasting to smallholder farmer food security since it spread to sub-Saharan Africa in 2016, who have suffered massive crop losses, particularly maize, an important staple for basic sustenance. Since the FAW mainly devours green leaf biomass during the maize vegetative growth stage, the implementation of remote sensing technologies offers opportunities for monitoring the FAW. Here, we developed and tested a Sentinel 2 a+b satellite-based monitoring algorithm based on optimized first-derivative NDVI time series analysis using Google Earth Engine. For validation, we first employed the FAO Fall Armyworm Monitoring and Early Warning System (FAMEWS) mobile app data from Kenya, and then subsequently conducted field validation campaigns in Zimbabwe, Kenya, and Tanzania. Additionally, we directly observed loss of green biomass during maize vegetative growth stages caused by the FAW, confirming the observed signals of loss of the leaf area index (LAI) and the total green biomass (via the NDVI). Preliminary analyses suggested that satellite monitoring of small-scale farmer fields at the regional level may be possible with an NDVI first-derivative time series anomaly analysis using ESA Sentinel 2 a+b (R² = 0.81). Commercial nanosatellite constellations, such as PlanetScope, were also explored, which may offer benefits from greater spatial resolution and return interval frequency. Due to other confounding factors, such as clouds, intercropping, weeds, abiotic stresses, or even other biotic pests (e.g., locusts), validation results were mixed. Still, maize biomass anomaly detection for monitoring the FAW using satellite data could help confirm the presence of the FAW with the help of expanded field-based monitoring through the FAO FAMEWS app. Full article

This paper focuses on the design of a new optical cone and pendulum scanning imaging mode for micro-nanosatellites. This kind of satellite uses a high-resolution camera with a small imaging plane to achieve high-resolution and ultra-wide coverage imaging through the three-dimensional motion of the camera’s wobble, satellite spin, and satellite orbital motion. First, this paper designs a single-camera constant speed OCPSI (optical cone and pendulum scanning imaging) mode. On the premise of ensuring coverage, the motion parameters and imaging parameters are derived. Then, in order to improve the performance and imaging quality of the system, a dual-camera variable speed OCPSI mode is designed. In this method, in order to reduce the overlap ratio, the camera is oscillated at a variable speed. Turn on the cameras in turn at the same time to minimize the overlap. This paper details these working modes. The simulation experiment is carried out using the satellite orbit of 500 km, the focal length of 360 mm, the pixel size of 2.5 μm, the resolution of [5120 × 5120], the number of imaging frames in the pendulum scanning hoop of 10, and the initial camera inclination angle of 30°. The single-camera constant speed OCPSI mode has an effective swath of 1060 km at a ground sampling distance of 5.3 m. The dual-camera variable speed OCPSI mode has an effective width of 966 km under the same conditions. Finally, the ground experiment prototype of OCPSI imaging theory is designed. We choose a camera with a pixel size of 3.45 μm, a resolution of [1440 × 1080], and a focal length of 25 mm. The ground experiment was carried out at the initial camera inclination angle of 10°, the number of imaging frames in the pendulum scanning hoop of 3, and the orbit height of 11 m. The experimental result is that the effective width of OCPSI imaging mode reaches 10.8 m. Compared with the traditional push-broom mode using the same camera, the effective width of 1.64 m is increased by seven times, and the effective width of 3.83 m is increased by three times compared to the traditional whisk-broom imaging mode. This study innovatively integrates three-dimensional motion imaging into aerospace remote sensing and provides a reference for the research on the realization of high-resolution and ultra-wide coverage of micro-nano remote sensing satellites. Full article

With growth in data volume from space missions, interest in laser communications has increased, owing to their importance for high-speed data transfer in the commercial and defense fields, spaceborne remote sensing, and surveillance. Here, we propose a novel system for space-to-space laser communication, a very high-speed inter-satellite link system using an infrared optical terminal and nanosatellite (VISION), which is aimed at establishing and validating miniaturized laser crosslink systems and several space technologies using two 6U nanosatellites in formation flying. An optical link budget analysis is conducted to derive the signal-to-noise ratio requirements and allocate the system budget; the optical link margin should be greater than 10 dB to guarantee communication with practical limitations. The payload is a laser transceiver with a deployable space telescope to enhance the gain of the beam transmission and reception. Nanosatellites, including precise formation flying GNC systems, are designed and analyzed. The attitude control system ensures pointing and tracking errors within tens of arcsec, and they are equipped with a propulsion system that can change the inter-satellite distance rapidly and accurately. This novel concept of laser crosslink systems is expected to make a significant contribution to the future design and construction of high-speed space-to-space networks. Full article

CubeSats, the nanosatellites and microsatellites with a wet mass up to 60 kg, accompanied by the cost decrease of accessing the space, amplified the rapid development of the Earth Observation industry. Acquired image data serve as an essential source of information in various disciplines like environmental protection, geosciences, or the military. As the quantity of remote sensing data grows, the bandwidth resources for the data transmission (downlink) are exhausted. Therefore, new techniques that reduce the downlink utilization of the satellites must be investigated and developed. For that reason, we are presenting CloudSatNet-1: an FPGA-based hardware-accelerated quantized convolutional neural network (CNN) for satellite on-board cloud coverage classification. We aim to explore the effects of the quantization process on the proposed CNN architecture. Additionally, the performance of cloud coverage classification by biomes diversity is investigated, and the hardware architecture design space is explored to identify the optimal FPGA resource utilization. Results of this study showed that the weights and activations quantization adds a minor effect on the model performance. Nevertheless, the memory footprint reduction allows the model deployment on low-cost FPGA Xilinx Zynq-7020. Using the RGB bands only, up to 90% of accuracy was achieved, and when omitting the tiles with snow and ice, the performance increased up to 94.4% of accuracy with a low false-positive rate of 2.23% for the 4-bit width model. With the maximum parallelization settings, the hardware accelerator achieved 15 FPS with 2.5 W of average power consumption (0.2 W increase over the idle state). Full article

Launched in March 2021, the 3U CubeSat nanosatellite was the first ever to use an ultra-lightweight harmonic diffractive lens for Earth remote sensing. We describe the CubeSat platform we used; our 10 mm diameter and 70 mm focal length lens synthesis, design, and manufacturing; a custom 3D-printed camera housing built from a zero-thermal-expansion metal alloy; and the on-Earth image post-processing with a convolutional neural network resulting in images comparable in quality to classical refractive optics used for remote sensing before. Full article

Nanosatellites are being widely used in various missions, including remote sensing applications. However, the difficulty lies in mission operation due to downlink speed limitation in nanosatellites. Considering the global cloud fraction of 67%, retrieving clear images through the limited downlink capacity becomes a larger issue. In order to solve this problem, we propose an image prioritization method based on cloud coverage using CNN. The CNN is designed to be lightweight and to be able to prioritize RGB images for nanosatellite application. As previous CNNs are too heavy for onboard processing, new strategies are introduced to lighten the network. The input size is reduced, and patch decomposition is implemented for reduced memory usage. Replication padding is applied on the first block to suppress border ambiguity in the patches. The depth of the network is reduced for small input size adaptation, and the number of kernels is reduced to decrease the total number of parameters. Lastly, a multi-stream architecture is implemented to suppress the network from optimizing on color features. As a result, the number of parameters was reduced down to 0.4%, and the inference time was reduced down to 4.3% of the original network while maintaining approximately 70% precision. We expect that the proposed method will enhance the downlink capability of clear images in nanosatellites by 112%. Full article

The increasing demand for high-resolution hyperspectral images from nano and microsatellites conflicts with the strict bandwidth constraints for downlink transmission. A possible approach to mitigate this problem consists in reducing the amount of data to transmit to ground through on-board processing of hyperspectral images. In this paper, we propose a custom Convolutional Neural Network (CNN) deployed for a nanosatellite payload to select images eligible for transmission to ground, called CloudScout. The latter is installed on the Hyperscout-2, in the frame of the Phisat-1 ESA mission, which exploits a hyperspectral camera to classify cloud-covered images and clear ones. The images transmitted to ground are those that present less than 70% of cloudiness in a frame. We train and test the network against an extracted dataset from the Sentinel-2 mission, which was appropriately pre-processed to emulate the Hyperscout-2 hyperspectral sensor. On the test set we achieve 92% of accuracy with 1% of False Positives (FP). The Phisat-1 mission will start in 2020 and will operate for about 6 months. It represents the first in-orbit demonstration of Deep Neural Network (DNN) for data processing on the edge. The innovation aspect of our work concerns not only cloud detection but in general low power, low latency, and embedded applications. Our work should enable a new era of edge applications and enhance remote sensing applications directly on-board satellite. Full article

Nanosatellites are increasingly being used in space-related applications to demonstrate and test scientific capability and engineering ingenuity of space-borne instruments and for educational purposes due to their favourable low manufacturing costs, cheaper launch costs, and short development time. The use of CubeSat to demonstrate earth imaging capability has also grown in the last two decades. In 2017, a South African company known as Space Commercial Services launched a low-orbit nanosatellite named nSight-1. The demonstration nanosatellite has three payloads that include a modular designed SCS Gecko imaging payload, FIPEX atmospheric science instrument developed by the University of Dresden and a Radiation mitigation VHDL coding experiment supplied by Nelson Mandela University. The Gecko imager has a swath width of 64 km and captures 30 m spatial resolution images using the red, green, and blue (RGB) spectral bands. The objective of this study was to assess the interpretability of nSight-1 in the spatial dimension using Landsat 8 as a reference and to recommend potential earth observation applications for the mission. A blind image spatial quality evaluator known as Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) was used to compute the image quality for nSight-1 and Landsat 8 imagery in the spatial domain and the National Imagery Interpretability Rating Scale (NIIRS) method to quantify the interpretability of the images. A visual interpretation was used to propose some potential applications for the nSight1 images. The results indicate that Landsat 8 OLI images had significantly higher image quality scores and NIIRS results compared to nSight-1. Landsat 8 has a mean of 19.299 for the image quality score while nSight-1 achieved a mean of 25.873. Landsat 8 had NIIRS mean of 2.345 while nSight-1 had a mean of 1.622. The superior image quality and image interpretability of Landsat could be attributed for the mature optical design on the Landsat 8 satellite that is aimed for operational purposes. Landsat 8 has a GDS of 30-m compared to 32-m on nSight-1. The image degradation resulting from the lossy compression implemented on nSight-1 from 12-bit to 8-bit also has a negative impact on image visual quality and interpretability. Whereas it is evident that Landsat 8 has the better visual quality and NIIRS scores, the results also showed that nSight-1 are still very good if one considers that the categorical ratings consider that images to be of good to excellent quality and a NIIRS mean of 1.6 indicates that the images are interpretable. Our interpretation of the imagery shows that the data has considerable potential for use in geo-visualization and cartographic land use and land cover mapping applications. The image analysis also showed the capability of the nSight-1 sensor to capture features related to structural geology, geomorphology and topography quite prominently. Full article