Solar Power Plant Detection on Multi-Spectral Satellite Imagery using Weakly-Supervised CNN with Feedback Features and m-PCNN Fusion

Most of the traditional convolutional neural networks (CNNs) implements bottom-up approach (feed-forward) for image classifications. However, many scientific studies demonstrate that visual perception in primates rely on both bottom-up and top-down connections. Therefore, in this work, we propose a CNN network with feedback structure for Solar power plant detection on middle-resolution satellite images. To express the strength of the top-down connections, we introduce feedback CNN network (FB-Net) to a baseline CNN model used for solar power plant classification on multi-spectral satellite data. Moreover, we introduce a method to improve class activation mapping (CAM) to our FB-Net, which takes advantage of multi-channel pulse coupled neural network (m-PCNN) for weakly-supervised localization of the solar power plants from the features of proposed FB-Net. For the proposed FB-Net CAM with m-PCNN, experimental results demonstrated promising results on both solar-power plant image classification and detection task.Comment: 9 pages, 9 figures, 4 table

Automated classification of heat sources detected using SWIR remote sensing

Abstract The potential of shortwave infrared (SWIR) remote sensing to detect hotspots has been investigated using satellite data for decades. The hotspots detected by satellite SWIR sensors include very high-temperature heat sources such as wildfires, volcanoes, industrial activity, or open burning. This study proposes an automated classification method of heat source detected utilizing Landsat 8 and Sentinel-2 data. We created training data of heat sources via visual inspection of hotspots detected by Landsat 8. A scheme to classify heat sources for daytime data was developed by combining classification methods based on a Convolutional Neural Network (CNN) algorithm utilizing spatial features and a decision tree algorithm based on thematic land-cover information and our time series detection record. Validation work using 10,959 classification results corresponding to hotspots acquired from May 2017 to July 2019 indicated that the two classification results were in 79.7% agreement. For hotspots where the two classification schemes agreed, the classification was 97.9% accurate. Even when the results of the two classification schemes conflicted, either was correct in 73% of the samples. To improve the accuracy, the heat source category was re-allocated to the most probable category corresponding to the combination of the results from the two methods. Integrating the two approaches achieved an overall accuracy of 92.8%. In contrast, the overall accuracy for heat source classification during nighttime reached 79.3% because only the decision tree-based classification was applicable to limited available data. Comparison with the Visible Infrared Imaging Radiometer Suite (VIIRS) fire product revealed that, despite the limited data acquisition frequency of Landsat 8, regional tendencies in hotspot occurrence were qualitatively appropriate for an annual period on a global scale

Investigation of organic matter in the Allende meteorite using scanning transmission X-ray microscope at photon factory

第6回極域科学シンポジウム[OA] 南極隕石11月16日（月）　国立極地研究所１階交流アトリウ

6U CubeSat for Ultraviolet Time-Domain Astronomy

A wide-field ultraviolet observatory for time-domain astronomy utilizing 6U CubeSat is presented. Ultraviolet waveband is one of the unexplored fields in astronomy. Potential targets are short duration transient sources in UV-band: early-phase emission from gravitational wave sources, supernovae shock-breakouts, tidal disruption events around super massive blackholes, etc. The telescope was designed for covering the large error circle of GW detectors, FoV~100 deg2. Thanks to the high quantum efficiency of “delta-doping” detector, the detection limit achieves 20 mag (AB) for 1800 s exposure in NUV band, which is sufficient to detect UV emission from a binary neutron star merger within 200 Mpc from the earth. The satellite has a high-performance on-board computer for on-orbit analysis to detect transient sources and measure the magnitude and the accurate position of the target. The obtained information is required to be transferred to the ground within 30 min from the detection to start multi-messenger follow-up observations utilizing ground-based observatories and astronomical satellites. In this presentation we show the mission overview and conceptual design of the satellite system