On-board satellite data processing to achieve smart information collection

Schwartz, Christofer; Sander, Ingo; Jordão, Rodolfo; Bruhn, Fredrik; Persson, Mathias; Ekblad, Joakim; Fuglesang, C.

doi:10.1117/12.2620955

Cited by 3 publications

(8 citation statements)

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“…This paper makes use of the threshold-based algorithm described in [ 12 ] to perform the detection of the clouds. More precisely, the algorithm makes use of the RGB bands as input signals to explore the brightness content, for which the threshold value may vary by season, sun elevation, and other factors, to avoid false alarms.…”

Section: Methodsmentioning

confidence: 99%

“…The threshold-based algorithm proposed in [ 12 ] was used in this study. The algorithm makes use of the constant false alarm rate (CFAR) normalization described in [ 22 ] as a part of the change detection algorithm for VHF UWB SAR images.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Preliminary results illustrating achievable gains in terms of information saved are described in [ 12 ]. In [ 12 ], information is discarded that is considered not useful content: (1) images covered by clouds and (2) water images without vessels. Thus, the methodology described in [ 12 ] is composed of a cloud detection algorithm and a vessel detection algorithm, and was evaluated using four satellite images.…”

Section: Introductionmentioning

confidence: 99%

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Satellite Image Compression Guided by Regions of Interest

Schwartz

Sander

Bruhn

et al. 2023

Sensors

Self Cite

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Small satellites empower different applications for an affordable price. By dealing with a limited capacity for using instruments with high power consumption or high data-rate requirements, small satellite missions usually focus on specific monitoring and observation tasks. Considering that multispectral and hyperspectral sensors generate a significant amount of data subjected to communication channel impairments, bandwidth constraint is an important challenge in data transmission. That issue is addressed mainly by source and channel coding techniques aiming at an effective transmission. This paper targets a significant further bandwidth reduction by proposing an on-the-fly analysis on the satellite to decide which information is effectively useful before coding and transmitting. The images are tiled and classified using a set of detection algorithms after defining the least relevant content for general remote sensing applications. The methodology makes use of the red-band, green-band, blue-band, and near-infrared-band measurements to perform the classification of the content by managing a cloud detection algorithm, a change detection algorithm, and a vessel detection algorithm. Experiments for a set of typical scenarios of summer and winter days in Stockholm, Sweden, were conducted, and the results show that non-important content can be identified and discarded without compromising the predefined useful information for water and dry-land regions. For the evaluated images, only 22.3% of the information would need to be transmitted to the ground station to ensure the acquisition of all the important content, which illustrates the merits of the proposed method. Furthermore, the embedded platform’s constraints regarding processing time were analyzed by running the detection algorithms on Unibap’s iX10-100 space cloud platform.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Satellite Image Compression Guided by Regions of Interest

Schwartz

Sander

Bruhn

et al. 2023

Sensors

Self Cite

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“…Generally, remote-sensing satellites can be equipped with a palette of sensors providing information in various bands. From the simplest one (RGB imageries) followed by multispectral imageries (usually a combination of RGB and near-infrared band (NIR)) to the hyperspectral imageries providing a complex spectrum of the sensed area [25,26].…”

Section: Introductionmentioning

confidence: 99%

CloudSatNet-1: FPGA-Based Hardware-Accelerated Quantized CNN for Satellite On-Board Cloud Coverage Classification

Pitonak¹,

Mucha

Dobis³

et al. 2022

Remote Sensing

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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).

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Dynamic Targeting of Satellite Observations Incorporating Slewing Costs and Complex Observation Utility ^*

Kangaslahti,

Candela,

Swope

et al. 2024

2024 IEEE International Conference on Robotics and Automation (ICRA)

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On-board satellite data processing to achieve smart information collection

Cited by 3 publications

References 0 publications

Satellite Image Compression Guided by Regions of Interest

Satellite Image Compression Guided by Regions of Interest

CloudSatNet-1: FPGA-Based Hardware-Accelerated Quantized CNN for Satellite On-Board Cloud Coverage Classification

Dynamic Targeting of Satellite Observations Incorporating Slewing Costs and Complex Observation Utility ^*

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On-board satellite data processing to achieve smart information collection

Cited by 3 publications

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Satellite Image Compression Guided by Regions of Interest

Satellite Image Compression Guided by Regions of Interest

CloudSatNet-1: FPGA-Based Hardware-Accelerated Quantized CNN for Satellite On-Board Cloud Coverage Classification

Dynamic Targeting of Satellite Observations Incorporating Slewing Costs and Complex Observation Utility *

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Dynamic Targeting of Satellite Observations Incorporating Slewing Costs and Complex Observation Utility ^*