Unsupervised Segmentation of Remote Sensing Images using FD Based Texture Analysis Model and ISODATA

Abstract

In this paper, an unsupervised segmentation methodology is proposed for remotely sensed images by using Fractional Differential (FD) based texture analysis model and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA). Essentially, image segmentation is used to assign unique class labels to different regions of an image. In this work, it is transformed into texture segmentation by signifying each class label as a unique texture class. The FD based texture analysis model is suggested for texture feature extraction from images and ISODATA is used for segmentation. The proposed methodology was first implemented on artificial target images and then on remote sensing images from Google Earth. The results of the proposed methodology are compared with those of the other texture analysis methods such as LBP (Local Binary Pattern) and NBP (Neighbors based Binary Pattern) by visual inspection as well as using classification measures derived from confusion matrix. It is justified that the proposed methodology outperforms LBP and NBP methods.

Refined Color Texture Classification Using CNN and Local Binary Pattern

“…e second phase tends to recognize the texture from the extracted features. Texture recognition is increasingly set as a critical issue in computer vision that has many applications such as biomedical image processing [1][2][3][4][5], object detection [6][7][8], and remote sensing [9][10][11] application fields.…”

Evolutionary Machine Learning Powered by Genetics Algorithm for IoT-Specific Health Monitoring of Agriculture Vehicles

“…Potentially, optimization can improve the efficiency of the systems in a wide range of industrial applications like compressive sensing [6][7][8][9][10][11][12][13][14], optical instruments [15], power quality [16][17][18], sentiment mining [19], image adaptation [20,21], sentiment mining [19], data mining [22][23][24], power line communications [25], featuring big data [26], location-based service [27], telecommunications [28][29][30][31][32][33], analysis of texture [34], planning power system [35], public transportation systems [36], faulty condition diagnosis in agriculture machinery [37][38][39], interaction of human and robot [40], medical images [34,41,42], study of human motion [43], noise removal [44,45], smart ambient [46], electrocardiogram processing [47][48]…”

Using Fuzzy Approach in Determining Critical Parameters for Optimum Safety Functions in Mega Projects (Case Study: Iran’s Construction Industry)

“…Optimization techniques are in use in a variety of technical fields from power quality measurement [20][21][22] to medical engineering [23]. Additionally, it is getting to be deployed in sentiment mining [24], agriculture machines diagnostics [25][26][27], services based on location [28], extraction of features from big data [29], telecommunications [30][31][32][33][34][35], software-intensive systems [36], power line communications [37], processing medical images [38][39][40], public systems for transportation [41], power system planning [42,43], texture analysis [44], adaptation of image adaptation [45], human-robot interaction [46], data mining [47][48][49], noise cancellation [50,51], analysis of human motion [52], smart environment [53], electrocardiogram processing [54][55][56], etc. Meta-heuristic techniques cover a wide range of optimization methodologies [57][58][59].…”