Graph-Based Feature Reduction for Three-Dimensional Gabor Filter in PolSAR Image Classification

Polarimetric Synthetic Aperture Radar (PolSAR) image classification is one of the most important applications in remote sensing. In this paper, the goal is PolSAR image classification and also introduce a method to obtain the best result for PolSAR image classification and recognition. In this article, we present the 3D-Gabor filters as a way in order to feature extraction of PolSAR images and get the best result with high accuracy for PolSAR image classification. Also, we prove that the 3D-Gabor filter approach can get higher accuracy than traditional methods for PolSAR images classification, but one of the most important challenges of 3D-Gabor filters is the number of features that are extracted from them. Therefore, by using 3D-Gabor filter we can't reach the optimal result because of the curse of dimensionality. So, to achieve the best results we propose a method to reduce the features that are extracted from 3D-Gabor filters. By using our proposed method, the features will be mapped to a new space with smaller dimensions. In the end, the experimental results indicate the superiority of the proposed method.

Automatic Detection of Subsidence Troughs in SAR Interferograms Using Mathematical Morphology

In this paper, an automatic algorithm for the detection of subsidence areas in SAR interferograms is proposed. It is based on the analysis of spatial distribution of the interferogram phase, and its coherence and entropy. The developed method was tested for differential interferograms generated on the basis of Sentinel-1 SAR images covering mining areas in South Poland. The obtained results were compared with those achieved using a method based on circular Gabor filters. Performed analysis revealed that the detection rate for the proposed method varied from 34% to 83%. It is an improved method based on Gabor filters that achieved a detection rate from 30% to 53%.

Gabor Filter Selection and Computational Processing for Emotion Recognition

CrowdEmotion produce software to measure a person's emotions based on analysis of microfacial expressions using a machine learning algorithm to recognize which features correspond with which emotions. The features are derived by applying a bank of Gabor filters to a set of frames. CrowdEmotion needed to improve the accuracy, processing speed and cost-efficiency of the tool. In particular they wanted to know if a subset of the bank of Gabor filters was sufficient, and whether the image filtering stage could be implemented on a GPU. A framework for choosing the optimum set of Gabor filters was established and ways of reducing the dimensionality of this were interrogated. Taking a subset of Local Binary Patterns was found to be fully justified. Meanwhile choosing a gridding pattern is open to interpretation; some suggestions were made about how this choice might be improved.

Texture-Based Feature Extraction Using Gabor Filters to Detect Diseases of Tomato Leaves

The disease in tomato plants, especially on tomato leaves will have an impact on the quality and quantity of tomatoes produced. Handling disease on tomato leaves that must be done is to detect the type of disease as early as possible, then determine the treatment that must be done. Detection of its types of tomato plant diseases requires sufficient knowledge and experience. The problem is that many beginner farmers in growing tomatoes do not have much knowledge, so they have failed in growing tomatoes. Based on these cases, this study proposes a model for the early detection of disease in tomato leaves based on image processing. The research method used is divided into 5 stages, namely preprocessing, segmentation, feature extraction, classification, and performance evaluation. The feature extraction stage used is texture-based with Gabor filters and color-based filters. The final decision is determined by the Support Vector Machine (SVM) classification algorithm with the Radial Basis Function (RBF) kernel. The test results of the tomato leaf disease detection system produced an average performance parameter of 98.83% specificity, 90.37% sensitivity, 90.34% F1-score, 90.37% accuracy, and 94.60% area under the curve (AUC). Referring to the resulting of the AUC performance, the tomato leaf disease detection system is in the very good category.

A Hybrid Multiresolution Texture Segmentation and its Application to Ikonos Satellite Imagery

In this thesis a method for segmenting textured images using Gabor filters is presented. One of the most recent approaches for texture segmentation and analysis is multi-channel filtering. There are several applicable choices as filter banks which are used for textured images. Gaussian filters modulated by exponential or by sinusoidal filters, known as Gabor filters, have been proven to be very usefyl for texture analysis for the images containing specific frequency and orientation characteristics. Resembling the human visual cortical cells, Gabor function is a popular sub-band filter for multi-channel decompositon. Optimum joint spatial/spatial frequency uncertainty principle and its ability to recognize and pass specific frequencies and orientations are attributes of Gabor filter that make it more attractive. Gabor function with these attributes could simulate the task of simple visual cells in the cortex. Gabor function has several parameters that determine the sub-band Gabor filter and must be determined accurately to extract the features precisely for texture discrimination. A wide selection range for each parameter exists and many combinations of these parameters are possible. Accurate selection and combination of values for the parameters are of crucial importance. Hence a difficult goal is minimizing the number of filters. On the other hand a variety of approaches of texture analysis and recognition have been presented in remote sensing applications, including land cover/land use classification and urban scene segmentation. With the avaiability of very high-resolution commercial satellite imagery such as IKONOS, it is possible to obtain detailed information on urban land use and change detection that are of particular interest to urban and regional planners. In this thesis considering the attributes of human visual system, a hybrid algorithm is implemented using multi-channel decomposition by Gabor filter bank for feature extraction in conjunction with Artificial Neural Networks for both feature reduction and texture segmentation. Three approaches are implemented to optimize Gabor filter bank for image segmentation. Eventually the proposed method is successfully applied for segmentation of IKONOS satellite images.

A Hybrid Multiresolution Texture Segmentation and its Application to Ikonos Satellite Imagery

In this thesis a method for segmenting textured images using Gabor filters is presented. One of the most recent approaches for texture segmentation and analysis is multi-channel filtering. There are several applicable choices as filter banks which are used for textured images. Gaussian filters modulated by exponential or by sinusoidal filters, known as Gabor filters, have been proven to be very usefyl for texture analysis for the images containing specific frequency and orientation characteristics. Resembling the human visual cortical cells, Gabor function is a popular sub-band filter for multi-channel decompositon. Optimum joint spatial/spatial frequency uncertainty principle and its ability to recognize and pass specific frequencies and orientations are attributes of Gabor filter that make it more attractive. Gabor function with these attributes could simulate the task of simple visual cells in the cortex. Gabor function has several parameters that determine the sub-band Gabor filter and must be determined accurately to extract the features precisely for texture discrimination. A wide selection range for each parameter exists and many combinations of these parameters are possible. Accurate selection and combination of values for the parameters are of crucial importance. Hence a difficult goal is minimizing the number of filters. On the other hand a variety of approaches of texture analysis and recognition have been presented in remote sensing applications, including land cover/land use classification and urban scene segmentation. With the avaiability of very high-resolution commercial satellite imagery such as IKONOS, it is possible to obtain detailed information on urban land use and change detection that are of particular interest to urban and regional planners. In this thesis considering the attributes of human visual system, a hybrid algorithm is implemented using multi-channel decomposition by Gabor filter bank for feature extraction in conjunction with Artificial Neural Networks for both feature reduction and texture segmentation. Three approaches are implemented to optimize Gabor filter bank for image segmentation. Eventually the proposed method is successfully applied for segmentation of IKONOS satellite images.