Jyh-Perng Fang

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In this paper, a novel technique is proposed for a supervised classification of multisource images for the purpose of landslide hazard assessment. The method, known as the generalized positive Boolean function (GPBF), is developed for land cover classification based on the fusion of remotely sensed images of the same scene collected from multiple sources.(More)
In recent years, dynamic voltage and frequency scaling (DVFS) has been considered as one of the most efficient techniques to decrease energy consumption, especially for battery-powered portable devices. However, many DVFS algorithms discuss the issue from the perspective of the processors only. Some researches have started to study the effects of memories(More)
Greedy modular eigenspaces (GME) has been developed for the band selection of hyperspectral images (HSI). GME attempts to greedily select uncorrelated feature sets from HSI. Unfortunately, GME is hard to find the optimal set by greedy operations except by exhaustive iterations. The long execution time has been the major drawback in practice. Accordingly,(More)
Band selection for hyperspectral images is an effective technique to mitigate the curse of dimensionality. A variety of band selection methods have been suggested in the past. This paper presents a novel band prioritization based on impurity function (IF) for the band selection of hyperspectral images. The proposed IF band selection (IFBS) is incorporated(More)
In this paper a novel technique based on nearest feature space (NFS), known as incenter-based nearest feature space (INFS), is proposed for supervised hyperspectral image classification. Due to the class separability and neighborhood structure, the traditional NFS can perform well for classification of remote sensing images. However, in some instances, the(More)
In this paper, we present a parallel computing technique, referred to as parallel positive Boolean function (PPBF), for supervised classification of multispectral images. The approach is based on the generalized positive Boolean function (GPBF) scheme, which has been successfully applied in multispectral image classification. The GPBF classifier is(More)
In this paper we present a parallel classification learning method, referred to as parallel minimum classification error (PMCE) learning, for supervised classification of multisource remote sensing images. The approach is based on the positive Boolean function (PBF) classifier scheme. The PBF implements the minimum classification error (MCE) as a criterion(More)
In this paper, we present a parallel computing technique for the feature extraction of hyperspectral images. The approach is based on the complete modular eigenspace (CME) scheme, which was designed to extract the simplest and most efficient feature modules by a newly defined multi-dimensional correlation matrix to optimize the modular eigenspace for high-(More)