Adaptive Type-2 Fuzzy System Research Articles

Fused LISS IV Image Classification using Deep Convolution Neural Networks

These days, earth observation frameworks give a large number of heterogeneous remote sensing information. The most effective method to oversee such fulsomeness in utilizing its reciprocity is a vital test in current remote sensing investigation. Considering optical Very High Spatial Resolution (VHSR) images, satellites acquire both Multi Spectral (MS) and panchromatic (PAN) images at various spatial goals. Information fusion procedures manage this by proposing a technique to consolidate reciprocity among the various information sensors. Classification of remote sensing image by Deep learning techniques using Convolutional Neural Networks (CNN) is increasing a solid decent footing because of promising outcomes. The most significant attribute of CNN-based strategies is that earlier element extraction is not required which prompts great speculation capacities. In this article, we are proposing a novel Deep learning based SMDTR-CNN (Same Model with Different Training Round with Convolution Neural Network) approach for classifying fused (LISS IV + PAN) image next to image fusion. The fusion of remote sensing images from CARTOSAT-1 (PAN image) and IRS P6 (LISS IV image) sensor is obtained by Quantization Index Modulation with Discrete Contourlet Transform (QIM-DCT). For enhancing the image fusion execution, we remove specific commotions utilizing Bayesian channel by Adaptive Type-2 Fuzzy System. The outcomes of the proposed procedures are evaluated with respect to precision, classification accuracy and kappa coefficient. The results revealed that SMDTR-CNN with Deep Learning got the best all-around precision and kappa coefficient. Likewise, the accuracy of each class of fused images in LISS IV + PAN dataset is improved by 2% and 5%, respectively.

A novel QIM-DCT based fusion approach for classification of remote sensing images via PSO and SVM models

Fusion of panchromatic and multispectral images has become a research interest for the classification of remote sensing images. The spectral and spatial resolutions of different images give better information with the aid of image classification. However, fusing pixels for various satellite images is difficult due to the nature of original image consists of complex information. Similarly, most of the existing fusion algorithms implement a unified processing over the whole part of the image, thereby leaving certain important needs out of consideration. The main aim of our proposed approach is to fuse the images by gathering all important information from multiple images with minimum errors. In this paper, we propose a novel quantization index modulation with discrete contourlet transform-based fusion approach for classification of remote sensing images (LISS IV sensor). In order to improve the image fusion performance, we eliminate certain noises (salt, pepper, and Gaussian) using Bayesian filter with Adaptive Type-2 Fuzzy System. After image fusion, we make image classification by two steps of processes including deep multi-feature extraction and feature selection. Multiple features such as spectral, shape, global and local features are extracted using Affine Transformation (0??, 90??, 180??, and 270??), and then the best set of features are chosen by mutual information and maximal information coefficients. Finally, the image is classified into seven classes using PSO and SVM namely Urban, Vegetation, Wetland, Tank, Water Area, Bare Land, and Roadways. MATLAB R2017b has been used for evaluation of the LISS IV images. Experimental results revealed that our proposed approach is very effective in terms of their classification accuracy.

Design of Adaptive Interval Type-2 Fuzzy Control System and Its Stability Analysis

Due to the requirement of dealing with the uncertainty and random disturbance in the real-time applications, a framework of adaptive type-2 fuzzy control system is designed. Since most of existed stability analysis results can not be used for the proposed system, by transferring the interval membership degrees into the constrained typical membership degrees, the closed-loop stability of proposed control system is analyzed. The necessary stability conditions are deduced. All stability analysis results are summarized into a Theorem. Comparing with other stability analysis results, the proposed method need less constrains and is easy to be used for controller design issue.