要約のみTohoku University吉田和哉課

Banatao Julie Ann Masirag

Tohoku University博士（工学）博士学位論文 (Thesis(doctor))要約のみthesi

Banatao, Julie Ann Masirag

Tohoku University Repository (TOUR) / 東北大学機関リポジトリ

Autonomous Image Processing and TargetPointing for an Earth ObservationMicrosatellite著者 Banatao Julie Ann Masirag学位授与機関 Tohoku University学位授与番号 11301甲第19494号URL http://hdl.handle.net/10097/00134260          ジュリーアン マシラグ   バナタオ 氏 名           JULIE ANN MASIRAG BANATAO 研究科，専攻の名称 東北大学大学院工学研究科（博士課程）航空宇宙工学専攻 学 位 論 文 題 目          Autonomous Image Processing and Target Pointing for an Earth                    Observation Microsatellite 論 文 審 査 委 員          主査  東北大学教授 吉田 和哉，                            東北大学教授 槙原 幹十朗，                           東北大学教授 岡谷 貴之，                           東北大学特任准教授（研究） 坂本 祐二 論文内容要約   This thesis discusses a method for improving the quality of images captured through an earth observation satellite by implementing an automated target selection and attitude maneuver onboard the satellite. The automated target selection is carried out by first capturing an image of a desired target area before maximum elevation. This real-time image will be processed onboard the satellite to decide the optimal target for scientific data image acquisition. The criteria for choosing a target is affected by cloud cover, proximity to original target specified by satellite operator, and the attitude maneuver required to point the satellite to the said target. The algorithm proposed is tested using actual satellite images and attitude maneuvers captured and executed from the microsatellites Diwata-1 and Diwata-2, but the proposed framework is general and applicable to any earth observation satellite with similar cameras onboard. In recent years, the use of small satellites for earth observation is becoming more popular as these are low cost but still have good spatial and spectral resolution. Technological advancement has enabled miniaturization of onboard camera payloads and bus electronics, thus enabling microsatellites and Cubesats to capture images with good spatial and spectral resolution. However, as more and more satellites for earth observation are launched, the amount of image data sent to ground stations are also increasing proportionately. Processing of all these image data is time consuming and labor intensive if done manually, thus automation of image processing at the ground stations have received a lot of focus in research. This thesis aims to take this further and shift the automated image processing to start not at the ground stations but onboard the satellite itself. Chapter 1 provides an introduction of small satellite technology for earth observation. This subsequently provides an overview of the Philippine's efforts towards building the country's very own microsatellites in collaboration with Tohoku University and Hokkaido University. The result of this collaboration is the development of two microsatellites, Diwata-1 and Diwata-2, where the data of this research is based on.   Chapter 2 presents a case study of Diwata-1 images. A detailed analysis of Diwata-1 images is conducted to identify the major points of improvement to efficiently operate the same class of microsatellites. Diwata-1 was launched to low earth orbit and was operational from May 2016 to April 2020, a period of four years. Given the limited operation time, it is crucial to maximize the use of the satellite to successfully achieve the mission requirements identified during the conceptualization phase of the microsatellite. In this case, as Diwata-1 is an earth observation microsatellite mainly for monitoring areas of the Philippines. From the case study, it was found out that around 70% of images captured by Diwata-1 are mostly clouds, essentially rendering these images not useable for scientific data extraction. Chapter 3 examines the use of an image processing algorithm using classical image processing techniques for support in ground operations. Diwata-1 has four different camera payloads greatly vary in ground sampling distance and spectral components. To take these into account, different techniques are also applied for each payload. MFC images which have red, green, and blue (RGB) components are processed via K-Mean clustering and edge detection. Images from the High Precision Telescope (HPT) and Space-borne Multispectral Imager (SMI) are separated into the different spectral components. Additional processing are therefore implemented for feature detection and matching. Results of the automated image classification for Diwata-1 images into useable or not useable show that accuracy of 90.7%, 83.8%, and 8.1% and recall of 95.0%, 91.2% and 100% can be achieved for HPT, SMI, and MFC images respectively Chapter 4 discusses the use of machine learning for the processing of Diwata-1 images. Rather than classifying images into useable or unuseable, semantic segmentation is implemented to classify per pixel of the images. Instead of building a totally new neural network architecture, base models of popular architectures such as VGG and UNet are used. Training data is created from Diwata-1 images, and test data are created from Diwata-2 images. Both training data and test data are captured by the payload MFC although from a slightly different altitude and orbit as Diwata-1 is deployed at a 400-km circular orbit while Diwata-2 is in a 600-km sun-synchronous orbit. Results show that even if the neural network is optimized for Diwata-1 images, it still works when used with images of a different satellite as long as it is captured from the same payload in low earth orbit. Chapter 5 details the autonomous target pointing algorithm and how it can automatically select a target for image capture by utilizing onboard cameras and attitude control system. Experiments in actual satellite Diwata-2 are executed to verify that the autonomous target selection is effective in improving the quality of images compared to that of traditional nadir pointing or operator-based selection of targets. Cloud cover is one of the major factors in the selection of a target so real-time cloud detection is performed by utilizing the CNN for MFC image segmentation from Chapter 4. Attitude maneuvers are then performed based on the results of the cloud cover detection. In-orbit validation from Diwata-2 attitude data verified the feasibility of the algorithm to be used in an actual satellite. Chapter 6 summarizes the findings of this research and the ideas for future work to build upon the achievements of presented here. Finally, author intends this thesis to lay the groundwork for a future microsatellite payload that can be integrated to an actual microsatellite flight model. The algorithms discussed are designed and analyzed to validate for use into operations of a future microsatellite. 

Autonomous Image Processing and Target Pointing for an Earth Observation Microsatellite

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超小型地球観測衛星のための自律画像処理とターゲット・ポインティング

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