Geoinformation Science Research Team

Team Outline

Geoinformation science research team develops intelligent and effective analysis engines to handle rapidly growing geoinformation, such as satellite imagery and aerial photo, to meet scientific and social demand.

Our Research

Workshop

Mini Workshop for Global Wildfire Prevention

Ryosuke Nakamura,
Team Leader

Information

2018.10.12

MUltiband Satellite Imagery for object Classification (MUSIC) for HotArea dataset

We created MUltiband Satellite Imagery for object Classification (MUSIC) for HotArea (HA) dataset. The HA is a system to automatically detect hotspots (e.g., fires and volcanoes) in the world in mid-resolution satellite data and displays the results on a web-based GIS system. Currently Hotarea utilizes Landsat 8 and Sentinel-2 data in global scale and in some selected regions, respectively.

2018.08.23

MUltiband Satellite Imagery for object Classification (MUSIC) to detect Golf Course

We have created MUltiband Satellite Imagery for object Classification (MUSIC) to detect Golf Course (GC). There are more than 30,000 golf courses all over the world. They are good targets for research of automatic object detection due to their specific shape, world-wide distribution and common size. MUSIC for GC is the dataset to support the global survey of golf courses based on satellite imagery.

2018.07.04

We have created Hyper Spectral Salient Object Detection Dataset (HS-SOD)

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List of Publications

2019.02.28

Salient object detection on hyperspectral images using features learned from unsupervised segmentation task

Nevrez Imamoglu, Guanqun Ding, Yuming Fang, Asako Kanezaki, Toru Kouyama, Ryosuke Nakamura

<Abstract> Various saliency detection algorithms from color images have been proposed to mimic eye fixation or attentive object detection response of human observers for the same scenes. However, developments on hyperspectral imaging systems enable us to obtain redundant spectral information of the observed scenes from the reflected light source from objects. A few studies using low-level features on hyperspectral images demonstrated that salient object detection can be achieved. In this work, we proposed a salient object detection model on hyperspectral images by applying manifold ranking (MR) on self-supervised Convolutional Neural Network (CNN) features (high-level features) from unsupervised image segmentation task. Self-supervision of CNN continues until clustering loss or saliency maps converges to a defined error between each iteration. Finally, saliency estimations is done as the saliency map at last iteration when the self-supervision procedure terminates with convergence. Experimental evaluations demonstrated that proposed saliency detection algorithm on hyperspectral images is outperforming state-of-the-arts hyperspectral saliency models including the original MR based saliency model.

arxiv link: https://arxiv.org/abs/1902.10993

arxiv pdf link: https://arxiv.org/pdf/1902.10993.pdf

2018.12.07

Investigation on Perceptron Learning for Water Region Estimation Using Large-Scale Multispectral Images

Poliyapram Vinayaraj,Nevrez Imamoglu,Ryosuke Nakamura, Atsushi Oda

<Abstract>
Land cover classification and investigation of temporal changes are considered to be common applications of remote sensing. Water/non-water region estimation is one of the most fundamental classification tasks, analyzing the occurrence of water on the Earth's surface. However, common remote sensing practices such as thresholding, spectral analysis, and statistical approaches are not sufficient to produce a globally adaptable water classification. The aim of this study is to develop a formula with automatically derived tuning parameters using perceptron neural networks for water/non-water region estimation, which we call the Perceptron-Derived Water Formula (PDWF), using Landsat-8 images. Water/non-water region estimates derived from PDWF were compared with three different approaches--Modified Normalized Difference Water Index (MNDWI), Automatic Water Extraction Index (AWEI), and Deep Convolutional Neural Network--using various case studies. Our proposed method outperforms all three approaches, showing a significant improvement in water/non-water region estimation. PDWF performance is consistently better even in cases of challenging conditions such as low reflectance due to hill shadows, building-shadows, and dark soils. Moreover, our study implemented a sunglint correction to adapt water/non-water region estimation over sunglint-affected pixels.

2018.10.23

Exploring Recurrent and Feedback CNNs for Multi-Spectral Satellite Image Classification

Nevrez Imamoglu, Pascual Martínez-Gómez, Ryuhei Hamaguchi, Ken Sakurada, Ryosuke Nakamura

<Abstract> The emergence of deep learning applications such as convolutional neural networks (CNNs) have resulted in huge improvements on computer vision applications in a wide variety of fields. However, several works demonstrated that low-quality or noisy data (even including perceptually not visible noises) may have a huge impact on the accuracy of CNN models. Therefore, as inspired by biological perception systems, some recent works proposed the use of recurrent and feedback features in CNNs as an improvement to the existing feed-forward CNNs. These recent works on the integration of recurrence and/or feedback to CNNs mostly tested deep networks on natural scenes with relatively perceptually good resolution color images. In this work, we explored the effectiveness of CNNs with recurrent and feedback features for the solar-power plant classification task on mid-resolution (1 pixel - 30×30 square meters per pixel) multi-spectral satellite images. Experiments show promising results when using top-down signals (especially recurrent and feedback features together) on CNNs for multi-spectral image classification tasks, outperforming the baseline CNN model without any recurrent and feedback structure and other approaches in the literature including deep models.

Access link: https://www.sciencedirect.com/science/article/pii/S1877050918319987

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Researcher Profile

Name and role	Field of Expertise	E-mail address　HP
Team Leader Ryosuke Nakamura	Planetary Science,@Satellite remote sensing	r.nakamura[at]aist.go.jp
Chief Senior Researcher Toshiaki Iwata	Space technology	totty.iwata[at]aist.go.jp
Reseacher Toru Kouyama	Remote sensing, Planetary Meteorology	t.kouyama[at]aist.go.jp
AIST Postdoctoral Researcher Soushi Kato	Remote Sensing	kato.soush[at]iaist.go.jp
Fixed-Term Researcher Nevrez IMAMOGLU	Computer vision, Assistive robotics, Intelligent systems	nevrez.imamoglu[at]aist.go.jp https://sites.google.com/site/imamoglunevrez/