Data reduction is used to aggregate or amalgamate the large data sets into smaller and manageable information pieces in order to fast and accurate classification of different attributes. However, excessive spatial or spectral data reduction may result in losing or masking important radiometric information. Therefore, we conducted this research to evaluate the effectiveness of the different...

Semi-supervised multi-label classification has been applied to many real-world applications such as image classification, document classification and so on. In semi-supervised learning, unlabeled samples are added to the training set for enhancing the classification performance, however, noises are introduced simultaneously. In order to reduce this negative effect, the nearest neighbor data edi...

We investigated the speckle filtering effect in supervised classification of the C-band polarimetric Ground Based SAR image data. Wishart classification method was used for the supervised classification of the polarimetric GB-SAR image data and total of 6 kinds of speckle filters were applied before supervised classification, which are boxcar, Gaussian, Lopez, IDAN, the refined Lee, and the ref...

This paper describes a proposed automatic language accent identification system based on phoneme class trajectory models. Our focus is to preserve discriminant information of the spectral evolution that belong to each accent. Here, we describe two classification schemes based on stochastic trajectory models; supervised and unsupervised classification. For supervised classification, we assume te...

As the information carried in a high spatial resolution image is not represented by single pixels but by meaningful image objects, which include the association of multiple pixels and their mutual relations, the object based method has become one of the most commonly used strategies for the processing of high resolution imagery. This processing comprises two fundamental and critical steps towar...

• The global and local structure are jointly considered for deep graph learning networks. relationship of the structures mined by hierarchical progressive learning. different fusion dynamically encoded their interdependence. Graph (GL) can capture distribution (graph structure) data based on convolutional networks (GCN), quality directly influences GCN semi-supervised classification. Most exist...