Geoinformation Science Research Team

Ryuhei Hamaguchi, Specialist for Specific Subject of The Geoinformation Science Research Team, won the first prize in the Building Detection Challenge Category of DeepGlobe Satellite Image Understanding Challenges on CVPR 2018.

Animal Detection Using Thermal Images and Its Required Observation Conditions（PDF）

Yu Oishi, Hiroyuki Oguma, Ayako Tamura, Ryosuke Nakamura and Tsuneo Matsunaga　

<Abstract>
Information about changes in the population sizes of wild animals is extremely important for conservation and management. Wild animal populations have been estimated using statistical methods, but it is difficult to apply such methods to large areas. To address this problem, we have developed several support systems for the automated detection of wild animals in remote sensing images. In this study, we applied one of the developed algorithms, the computer-aided detection of moving wild animals (DWA) algorithm, to thermal remote sensing images. We also performed several analyses to confirm that the DWA algorithm is useful for thermal images and to clarify the optimal conditions for obtaining thermal images (during predawn hours and on overcast days). We developed a method based on the algorithm to extract moving wild animals from thermal remote sensing images. Then, accuracy was evaluated by applying the method to airborne thermal images in a wide area. We found that the producer's accuracy of the method was approximately 77.3% and the user's accuracy of the method was approximately 29.3%. This means that the proposed method can reduce the person-hours required to survey moving wild animals from large numbers of thermal remote sensing images. Furthermore, we confirmed the extracted sika deer candidates in a pair of images and found 24 moving objects that were not identified by visual inspection by an expert. Therefore, the proposed method can also reduce oversight when identifying moving wild animals. The detection accuracy is expected to increase by setting suitable observation conditions for surveying moving wild animals. Accordingly, we also discuss the required observation conditions. The discussions about the required observation conditions would be extremely useful for people monitoring animal population changes using thermal remote sensing images.

Extremely strong polarization of an active asteroid (3200) Phaethon

Takashi Ito, Masateru Ishiguro, Tomoko Arai, Masataka Imai, Tomohiko Sekiguchi, Yoonsoo P. Bach, Yuna G. Kwon, Masanori Kobayashi, Ryo Ishimaru, Hiroyuki Naito, Makoto Watanabe & Kiyoshi Kuramoto

<Abstract>
The near-Earth asteroid (3200) Phaethon is the parent body of the Geminid meteor stream. Phaethon is also an active asteroid with a very blue spectrum. We conducted polarimetric observations of this asteroid over a wide range of solar phase angles α during its close approach to the Earth in autumn 2016. Our observation revealed that Phaethon exhibits extremely large linear polarization: P = 50.0 ± 1.1% at α = 106.5°, and its maximum is even larger. The strong polarization implies that Phaethon's geometric albedo is lower than the current estimate obtained through radiometric observation. This possibility stems from the potential uncertainty in Phaethon's absolute magnitude. An alternative possibility is that relatively large grains (~300 μm in diameter, presumably due to extensive heating near its perihelion) dominate this asteroid's surface. In addition, the asteroid's surface porosity, if it is substantially large, can also be an effective cause of this polarization.

Hyperspectral Image Dataset for Benchmarking on Salient Object Detection

Nevrez Imamoglu , Yu Oishi, Xiaoqiang Zhang , Guanqun Ding, Yuming Fang, Toru Kouyama , Ryousuke Nakamura

<Abstract> Many works have been done on salient object detection using supervised or unsupervised approaches on color images. Recently, a few studies demonstrated that efficient salient object detection can also be implemented by using spectral features in visible spectrum of hyperspectral images from natural scenes. However, these models on hyperspectral salient object detection were tested with a very few number of data selected from various online public dataset, which are not specifically created for object detection purposes. Therefore, here, we aim to contribute to the field by releasing a hyperspectral salient object detection dataset with a collection of 60 hyperspectral images with their respective ground-truth binary images and representative rendered color images (sRGB). We took several aspects in consideration during the data collection such as variation in object size, number of objects, foreground-background contrast, object position on the image, and etc. Then, we prepared ground truth binary images for each hyperspectral data, where salient objects are labeled on the images. Finally, we did performance evaluation using Area Under Curve (AUC) metric on some existing hyperspectral saliency detection models in literature.