Advanced Gaussian MRF Rotation-Invariant Texture Features for Classification of Remote Sensing Imagery
نویسندگان
چکیده
The features based on Markov random field (MRF) models are usually sensitive to the rotation of image textures. This paper develops an anisotropic circular Gaussian MRF (ACGMRF) model for modelling rotated image textures and retrieving rotation-invariant texture features. To overcome the singularity problem of the least squares estimate (LSE) method, an approximate least squares estimate (ALSE) method is proposed to estimate the parameters of the ACGMRF model. The rotation-invariant features can be obtained from the parameters of the ACGMRF model by the one-dimensional (1-D) discrete Fourier transform (DFT). Significantly improved accuracy can be achieved by applying the rotation-invariant features to classify SAR (synthetic aperture radar) sea ice and Brodatz imagery.
منابع مشابه
Fusion of Rotation-Invariant Texture Features for
Multichannel Gabor filters (MGFs) and Markov random fields (MRFs) are two common methods for texture classification. However, the two above methods make the implicit assumption that textures are acquired in the same viewpoint, which is unsuitable for rotation-invariant texture classification. In this paper, rotation-invariant (RI) texture features are developed based on MGF and MRF. A novel alg...
متن کاملTexture Characterization in Remote Sensing Imagery Using Binary Coding Techniques
In this paper rotation invariant Local Binary Patterns (LBP) texture based descriptors are evaluated experimentally in the context of land-use and land-cover object-based classification. The texture descriptors were employed in the classification of an Ikonos-2 and a Quickbird-2 image. The experiments have shown that texture characterization approaches perform well when combined with the graysc...
متن کاملRotation invariant texture descriptors based on Gaussian Markov random fields for classification
Local Parameter Histograms (LPH) based on Gaussian Markov random fields (GMRFs) have been successfully used in effective texture discrimination. LPH features represent the normalized histograms of locally estimated GMRF parameters via local linear regression. However, these features are not rotation invariant. In this paper two techniques to design rotation invariant LPH texture descriptors are...
متن کاملClassification of textures using Gaussian Markov random fields
The problem of texture classification arises in several disciplines such as remote sensing, computer vision, and image analysis. In this paper we present two feature extraction methods for the classification of textures using two-dimensional (2-D) Markov random field (MRF) models. It is assumed that the given M x M texture is generated by a Gaussian MRF model. In the first method, the least squ...
متن کاملTexture fusion and feature selection applied to SAR imagery
The discrimination ability of four different methods for texture computation in ERS SAR imagery is examined and compared. Feature selection methodology and discriminant analysis are applied to find the optimal combination of texture features. By combining features derived from different texture models, the classification accuracy increased significantly.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2003