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RSS FeedsRemote Sensing, Vol. 11, Pages 1417: Convolutional Neural Networks for On-Board Cloud Screening (Remote Sensing)

 
 

15 june 2019 00:00:57

 
Remote Sensing, Vol. 11, Pages 1417: Convolutional Neural Networks for On-Board Cloud Screening (Remote Sensing)
 




A cloud screening unit on a satellite platform for Earth observation can play an important role in optimizing communication resources by selecting images with interesting content while skipping those that are highly contaminated by clouds. In this study, we address the cloud screening problem by investigating an encoder–decoder convolutional neural network (CNN). CNNs usually employ millions of parameters to provide high accuracy; on the other hand, the satellite platform imposes hardware constraints on the processing unit. Hence, to allow an onboard implementation, we investigate experimentally several solutions to reduce the resource consumption by CNN while preserving its classification accuracy. We experimentally explore approaches such as halving the computation precision, using fewer spectral bands, reducing the input size, decreasing the number of network filters and also making use of shallower networks, with the constraint that the resulting CNN must have sufficiently small memory footprint to fit the memory of a low-power accelerator for embedded systems. The trade-off between the network performance and resource consumption has been studied over the publicly available SPARCS dataset. Finally, we show that the proposed network can be implemented on the satellite board while performing with reasonably high accuracy compared with the state-of-the-art.


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8 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 11, Pages 1418: Feedback Unilateral Grid-Based Clustering Feature Matching for Remote Sensing Image Registration (Remote Sensing)
Remote Sensing, Vol. 11, Pages 1416: Comparison of Pixel- and Object-Based Classification Methods of Unmanned Aerial Vehicle Data Applied to Coastal Dune Vegetation Communities: Casal Borsetti Case Study (Remote Sensing)
 
 
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