Kotaro Matsushita

Artificial neural network ensemble-based land-cover classifiers using MODIS data

Terra and Aqua, two satellites launched by the NASA-centered International Earth Observing System project, house MODIS (moderate resolution imaging spectroradiometer) sensors. Moderate-resolution remote sensing allows the quantifying of land-surface type and extent, which can be used to monitor changes in land cover and land use for extended periods of time. In this article, we propose land-surface classification by applying an ensemble technique based on fault masking among individual classifiers in N-version programming. An N-version programming ensemble of artificial neural networks is created, in which the majority vote result is used to predict land-surface cover from MODIS data. It is shown by experiment that an N-version programming ensemble of neural networks greatly improves the classification error rate of land-cover type.

Development and evaluation of satellite image data analysis infrastructure

Tokyo University of Information Sciences (TUIS) receives moderate resolution imaging spectroradiometer (MODIS) data, and provides the processed data to universities and research institutes as part of the academic frontier project. One of the major fields of research using MODIS data is the analysis of changes in the environment. We are currently developing applications to analyze environmental changes. These applications run on our satellite image data analysis system, which is implemented in a parallel distributed system and a database server. When using satellite data, one common problem is the interference of clouds. In order to remove this interference, the standard solution is to create composite data of the same regions during a selected time span, and to patch together data which are not covered by clouds to create a clear image. We introduced a piece-processing algorithm which separates one set of satellite image data into many small pieces of image data, making it quicker and easier to analyze and process the time-series satellite data. In this research, we implemented the pieceprocessing and composite-processing algorithms in order to increase the speed of analysis within the satellite image database. We tested the proposed processing and verified its effectiveness for target applications.

Artificial neural networks paddy-field classifier using spatiotemporal remote sensing data

Monitoring changes in a paddy-field area is important since rice is a staple food and paddy agriculture is a major cropping system in Asia. For monitoring changes in land surface, various applications using different satellites have been researched in the field of remote sensing. However, monitoring a paddy-field area with remote sensing is difficult owing to the temporal changes in the land surface, and the differences in the spatiotemporal characteristics in countries and regions. In this article, we used an artificial neural network to classify paddy-field areas using moderate resolution sensor data that includes spatiotemporal information. Our aim is to automatically generate a paddy-field classifier in order to create localized classifiers for each country and region.

Ensemble of artificial neural network based land cover classifiers using satellite data

Terra and Aqua, 2 satellites launched by the NASA-centered international Earth Observing System project, house MODIS (Moderate Resolution Imaging Spectroradiometer) sensors. Moderate resolution remote sensing allows the quantifying of land surface type and extent, which can be used to monitor changes in land cover and land use for extended periods of time. In this paper, we propose applying an ensemble technique, based on fault masking among individual classifier for N-version programming. We create an N-version programming ensemble of artificial neural networks and use the majority voting result to predict land surface cover from MODIS data. We show that an N-version programming ensemble of neural networks greatly improves the classification error rate of land cover type.

Applying Brightness Information in Satellite Image Data Search using Distributed Genetic Algorithm

Tokyo University of Information Sciences maintains and distributes MODIS (Moderate Resolution Imaging Spectroradiometer) satellite data as part of the research output for Frontier project. An intelligent image search system is being developed as part of the project, in order to retrieve requested images such as matching images patterns or forest and field fires extraction. The intelligent image search system applies genetic algorithm (GA) in the search algorithm. When searching for a target image area within the MODIS image database, it is possible that the search algorithm cannot match the optimal location when the brightness of the search image data and MODIS data image are very different. In order to solve this problem, we extended the search algorithm by implementing brightness information in the GA chromosome, so that brightness is adjusted within the GA image search. Further, we implemented the image search as distributed genetic algorithm search over a PC cluster network, in order to increase the search speed within the satellite image database. We tested the proposed system and verified the effectiveness of distributed genetic algorithm for the distributed MODIS satellite database search process.

Development of game-based learning features in programming learning support system

In this research, a programming learning support system incorporating game-based learning is proposed. The aim of the game-based learning approach is to stimulate and sustain the motivation of the learner during programming training. In the developed system, a puzzle-solving interface to programming training and a competitive scoring system was implemented to incorporate game-based learning. The proposed system was applied to an actual college programming course to verify the effectiveness of the proposed system. Finally, future works based on the results are discussed.

Detection of environmental changes from network structure

We studied the networks of the temperature record in the atmosphere. They are made by the strength of the synchronization, including the delay between temperatures at locations on Earth. We consider these locations as nodes, and we consider a pair of locations as a link if the synchronization between them is stronger than a threshold. The network is scale-free, which is thought to contribute to the stability of the climate.

Development and evaluation of a personalized Kanji learning system using multiple example sentences

This paper describes the development and evaluation of a user adaptive kanji learning system for JFL (Japanese as a foreign language) learners. Learning kanji is a real challenge for learners of Japanese as a foreign language. Since the context is important in the process of language learning, multiple example sentences to provide various contexts where the target kanji is used were implemented. In order to enhance its usability, English navigation was introduced. The result of the pilot evaluation showed that the average score increased in terms of its usability, while it revealed some user unfriendliness in kanji display. Our future works include its improvement such as addition of kanji meaning links and keyboard control.

Satellite data visualization system for education

It is important for environment protection to monitor changes in the environment by natural and human causes. It is also important to educate the next generation on the importance of the global environment issues. Recently, it has become possible to continually monitor the global environment using various satellite sensor data. But these satellite data are used for highly specialized analysis by experts in such fields, and the data cannot easily be used by non-experts. In this paper, we propose a satellite data visualization system for educational use. In the proposed system, a gray-scale 2-dimensional image is created from the satellite data. Next, a pseudo-color image is created from the gray-scale image to assist the comprehension of the data. A 3-dimensional data representation of the image is also created, to assist the comparison of the individual data. The aim of the created image is for educational use, and the image is created with emphasis on comprehension of the data, rather than presentation of data details. The aim of the proposed system is presenting the satellite data visually so that non-experts can easily understand. The target of this research is to apply the proposed system for natural science education and to improve the awareness of global environmental issues.

Applying soft computing for remote sensing data composite algorithms

Remote sensing of the earth’s surface using satellite-mounted sensor data is one of the most important methods for global environmental monitoring today. However, when using satellite sensor data, clouds in the atmosphere can interfere with the remote sensing, and specific land points may not be correctly monitored on any given day. In order to overcome this problem, a common alternative is to use multiple day composite data. Multiple day composite data use several consecutive days’ remote sensing data, and choose the most accurate data within the temporal dataset for the same land point. This allows the creation of a more complete dataset by patching together data which have had no cloud interference during a specified time period in order to create a clearer, more usable dataset. In this article, we propose the application of soft computing, namely fuzzy logic, in order to select the clearest data in the temporal interval to use for the composite data. Moderate resolution remote sensing data of areas in Japan were used for the evaluation, and the results were compared with previous composite methods.