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

2021 ◽  
Author(s):  
mohsen
Keyword(s):  
Image Classification ◽  
Gabor Filter ◽  
Three Dimensional ◽  
Gabor Filters ◽  
Feature Reduction ◽  
Synthetic Aperture ◽  
Optimal Result ◽  
Polarimetric Synthetic Aperture Radar ◽  
New Space ◽  
Filter Approach

Abstract 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.

scholarly journals LOCAL BINARY GRAPH FEATURE REDUCTION FOR THREE-DIMENSIONAL GABOR FILTER BASED HYPERSPECTRAL IMAGE CLASSIFICATION

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 285-291 ◽  
Author(s):  
M. Darvishnezhad ◽  
H. Ghassemian ◽  
M. Imani
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Gabor Filter ◽  
Three Dimensional ◽  
Gabor Filters ◽  
Feature Reduction ◽  
Hyperspectral Image Classification ◽  
Data Set ◽  
Spatial Features ◽  
Novel Method

Abstract. One of the challenges of the hyperspectral image classification is the fusing spectral and spatial features. There are several methods for fusing features in hyperspectral image classification. Three-Dimensional Gabor Filters are the best method to extract spectral and spatial features simultaneously. However, one of the problems with using the 3D Gabor filter is the high number of extracted features. In this paper, to reducing extracted features from 3D-Gabor filters and increasing the classification accuracy in hyperspectral images, a novel method named Local Binary Graph (LBG) is used. The LBG method uses a local graph to solve the optimization problem, which maps each pixel to the reduced dimension image and improves the McNemar test result in comparison with the existing methods. Finally, the result of the proposed method achieved 96.2% and 92.6% overall accuracy for Pavia University and Indian Pines data set, respectively.

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

2021 ◽  
Author(s):  
Nezamoddin Nezamoddini-Kachouie
Keyword(s):  
Land Use ◽  
Texture Analysis ◽  
Satellite Imagery ◽  
Filter Bank ◽  
Gabor Filter ◽  
Gabor Filters ◽  
Texture Segmentation ◽  
Feature Reduction ◽  
Gabor Function ◽  
Gabor Filter Bank

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.

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

2021 ◽  
Author(s):  
Nezamoddin Nezamoddini-Kachouie
Keyword(s):  
Land Use ◽  
Texture Analysis ◽  
Satellite Imagery ◽  
Filter Bank ◽  
Gabor Filter ◽  
Gabor Filters ◽  
Texture Segmentation ◽  
Feature Reduction ◽  
Gabor Function ◽  
Gabor Filter Bank

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.

scholarly journals PolSAR Image Classification Using a Superpixel-Based Composite Kernel and Elastic Net

2021 ◽  
Vol 13 (3) ◽  
pp. 380
Author(s):  
Yice Cao ◽  
Yan Wu ◽  
Ming Li ◽  
Wenkai Liang ◽  
Peng Zhang
Keyword(s):  
Image Classification ◽  
Feature Fusion ◽  
Classification Performance ◽  
Elastic Net ◽  
Synthetic Aperture ◽  
Polarimetric Synthetic Aperture Radar ◽  
Training Samples ◽  
Composite Kernel ◽  
Limited Training Samples ◽  
State Of Art

The presence of speckles and the absence of discriminative features make it difficult for the pixel-level polarimetric synthetic aperture radar (PolSAR) image classification to achieve more accurate and coherent interpretation results, especially in the case of limited available training samples. To this end, this paper presents a composite kernel-based elastic net classifier (CK-ENC) for better PolSAR image classification. First, based on superpixel segmentation of different scales, three types of features are extracted to consider more discriminative information, thereby effectively suppressing the interference of speckles and achieving better target contour preservation. Then, a composite kernel (CK) is constructed to map these features and effectively implement feature fusion under the kernel framework. The CK exploits the correlation and diversity between different features to improve the representation and discrimination capabilities of features. Finally, an ENC integrated with CK (CK-ENC) is proposed to achieve better PolSAR image classification performance with limited training samples. Experimental results on airborne and spaceborne PolSAR datasets demonstrate that the proposed CK-ENC can achieve better visual coherence and yield higher classification accuracies than other state-of-art methods, especially in the case of limited training samples.

scholarly journals Geometry-Aware Discriminative Dictionary Learning for PolSAR Image Classification

2021 ◽  
Vol 13 (6) ◽  
pp. 1218
Author(s):  
Yachao Zhang ◽  
Xuan Lai ◽  
Yuan Xie ◽  
Yanyun Qu ◽  
Cuihua Li
Keyword(s):  
Image Classification ◽  
San Francisco ◽  
Riemannian Manifolds ◽  
Dictionary Learning ◽  
Synthetic Aperture ◽  
Polarimetric Synthetic Aperture Radar ◽  
Alternating Direction ◽  
Euclidian Space ◽  
Public Datasets ◽  
Discriminative Dictionary Learning

In this paper, we propose a new discriminative dictionary learning method based on Riemann geometric perception for polarimetric synthetic aperture radar (PolSAR) image classification. We made an optimization model for geometry-aware discrimination dictionary learning in which the dictionary learning (GADDL) is generalized from Euclidian space to Riemannian manifolds, and dictionary atoms are composed of manifold data. An efficient optimization algorithm based on an alternating direction multiplier method was developed to solve the model. Experiments were implemented on three public datasets: Flevoland-1989, San Francisco and Flevoland-1991. The experimental results show that the proposed method learned a discriminative dictionary with accuracies better those of comparative methods. The convergence of the model and the robustness of the initial dictionary were also verified through experiments.

scholarly journals A Novel Deep Fully Convolutional Network for PolSAR Image Classification

2018 ◽  
Vol 10 (12) ◽  
pp. 1984 ◽  
Author(s):  
Yangyang Li ◽  
Yanqiao Chen ◽  
Guangyuan Liu ◽  
Licheng Jiao
Keyword(s):  
Image Classification ◽  
Sparse Coding ◽  
State Of The Art ◽  
Sliding Window ◽  
Synthetic Aperture ◽  
Prediction Problem ◽  
Convolutional Network ◽  
Polarimetric Synthetic Aperture Radar ◽  
Fully Convolutional Network ◽  
Image Integrity

Polarimetric synthetic aperture radar (PolSAR) image classification has become more and more popular in recent years. As we all know, PolSAR image classification is actually a dense prediction problem. Fortunately, the recently proposed fully convolutional network (FCN) model can be used to solve the dense prediction problem, which means that FCN has great potential in PolSAR image classification. However, there are some problems to be solved in PolSAR image classification by FCN. Therefore, we propose sliding window fully convolutional network and sparse coding (SFCN-SC) for PolSAR image classification. The merit of our method is twofold: (1) Compared with convolutional neural network (CNN), SFCN-SC can avoid repeated calculation and memory occupation; (2) Sparse coding is used to reduce the computation burden and memory occupation, and meanwhile the image integrity can be maintained in the maximum extent. We use three PolSAR images to test the performance of SFCN-SC. Compared with several state-of-the-art methods, SFCN-SC achieves promising results in PolSAR image classification.

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