Texture Based Land Cover Classification Algorithm Using Gabor Wavelet and Anfis Classifier
نویسنده
چکیده
Texture features play a predominant role in land cover classification of remotely sensed images. In this study, for extracting texture features from data intensive remotely sensed image, Gabor wavelet has been used. Gabor wavelet transform filters frequency components of an image through decomposition and produces useful features. For classification of fuzzy land cover patterns in the remotely sensed image, Adaptive Neuro Fuzzy Inference System (ANFIS) has been used. The strength of ANFIS classifier is that it combines the merits of fuzzy logic and neural network. Hence in this article, land cover classification of remotely sensed image has been performed using Gabor wavelet and ANFIS classifier. The classification accuracy of the classified image obtained is found to be 92.8%.
منابع مشابه
Texture Classification of Diffused Liver Diseases Using Wavelet Transforms
Introduction: A major problem facing the patients with chronic liver diseases is the diagnostic procedure. The conventional diagnostic method depends mainly on needle biopsy which is an invasive method. There are some approaches to develop a reliable noninvasive method of evaluating histological changes in sonograms. The main characteristic used to distinguish between the normal...
متن کاملClassification of Endometrial Images for Aiding the Diagnosis of Hyperplasia Using Logarithmic Gabor Wavelet
Introduction: The process of discriminating among benign and malignant hyperplasia begun with subjective methods using light microscopy and is now being continued with computerized morphometrical analysis requiring some features. One of the main features called Volume Percentage of Stroma (VPS) is obtained by calculating the percentage of stroma texture. Currently, this feature is calculated ...
متن کاملSpectral-spatial classification of hyperspectral images by combining hierarchical and marker-based Minimum Spanning Forest algorithms
Many researches have demonstrated that the spatial information can play an important role in the classification of hyperspectral imagery. This study proposes a modified spectral–spatial classification approach for improving the spectral–spatial classification of hyperspectral images. In the proposed method ten spatial/texture features, using mean, standard deviation, contrast, homogeneity, corr...
متن کاملAn Efficient Texture Classification Algorithm using Gabor Wavelet
In this paper we have investigated the application of nonseparable Gabor wavelet transform for texture classification. We have compared the effect of applying the dyadic wavelet transform as a traditional method with Gabor wavelet for texture extraction. It is well known that Gabor wavelets attain maximum joint space-frequency resolution which is highly significant in the process of texture ext...
متن کاملUnsupervised Segmentation of Texture Images Using a Combination of Gabor and Wavelet Features
This paper presents an unsupervised method of texture classification by combining the two most commonly used multi-resolution, multi-channel filters: Gabor filters and wavelet transform. We used a set of 8 Gabor filters and 2 wavelet filters: Daubeschies and Haar, for our analysis. The parameters (viz. frequency, orientation and size) of the Gabor filter bank are obtained by trial and error met...
متن کامل