Masanori Ohshiro

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.

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.

Land cover classification from MODIS satellite data using probabilistically optimal ensemble of artificial neural networks

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 a probabilistically optimal ensemble technique, based on fault masking among individual classifier for N-version programming. We create an optimal ensemble of artificial neural networks and use the majority voting result to predict land surface cover from MODIS data. We show that an optimal ensemble of neural networks greatly improves the classification error rate of land cover type.

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.

Self-adjusting programming training support system using genetic algorithm

Computer aided training has become an important method for improving computer education. For this research, we propose a programming training support system which targets understanding program structures which satisfy required program specifications. In our proposed training system, a given source code is broken up into separate puzzle pieces, and the user must layout the pieces in the correct order to reconstruct the program. The proposed system applies genetic algorithm (GA) and allows the system to self-adjust the difficulty of the programming problems matching the trainees competency. We created a prototype system and applied it in a 1st year university programming course.