Efficient rotation- and scale-invariant texture classification method based on Gabor wavelets

2008 ◽  
Vol 17 (4) ◽  
pp. 043026 ◽  
Author(s):  
Xudong Xie
Keyword(s):  
Texture Classification ◽  
Classification Method ◽  
Gabor Wavelets ◽  
Scale Invariant

Rotation invariant texture classification based on Gabor wavelets

2007 ◽  
Author(s):  
Xudong Xie ◽  
Jianhua Lu ◽  
Jie Gong ◽  
Ning Zhang
Keyword(s):  
Texture Classification ◽  
Gabor Wavelets ◽  
Rotation Invariant

scholarly journals ASSESSMENT OF MULTIBEAM BACKSCATTER TEXTURE ANALYSIS FOR SEAFLOOR SEDIMENT CLASSIFICATION

2017 ◽  
Vol XLII-4/W5 ◽  
pp. 177-183 ◽  
Author(s):  
S. A. Samsudin ◽  
R. C. Hasan
Keyword(s):  
Spatial Resolution ◽  
Clustering Algorithm ◽  
High Spatial Resolution ◽  
Texture Classification ◽  
Classification Method ◽  
Sediment Type ◽  
Range Analysis ◽  
Sediment Classification ◽  
Sediment Types ◽  
Seafloor Sediment

Recently, there have been many debates to analyse backscatter data from multibeam echosounder system (MBES) for seafloor classifications. Among them, two common methods have been used lately for seafloor classification; (1) signal-based classification method which using Angular Range Analysis (ARA) and Image-based texture classification method which based on derived Grey Level Co-occurrence Matrices (GLCMs). Although ARA method could predict sediment types, its low spatial resolution limits its use with high spatial resolution dataset. Texture layers from GLCM on the other hand does not predict sediment types, but its high spatial resolution can be useful for image analysis. The objectives of this study are; (1) to investigate the correlation between MBES derived backscatter mosaic textures with seafloor sediment type derived from ARA method, and (2) to identify which GLCM texture layers have high similarities with sediment classification map derived from signal-based classification method. The study area was located at Tawau, covers an area of 4.7&amp;thinsp;km<sup>2</sup>, situated off the channel in the Celebes Sea between Nunukan Island and Sebatik Island, East Malaysia. First, GLCM layers were derived from backscatter mosaic while sediment types (i.e. sediment map with classes) was also constructed using ARA method. Secondly, Principal Component Analysis (PCA) was used determine which GLCM layers contribute most to the variance (i.e. important layers). Finally, K-Means clustering algorithm was applied to the important GLCM layers and the results were compared with classes from ARA. From the results, PCA has identified that GLCM layers of Correlation, Entropy, Contrast and Mean contributed to the 98.77&amp;thinsp;% of total variance. Among these layers, GLCM Mean showed a good agreement with sediment classes from ARA sediment map. This study has demonstrated different texture layers have different characterisation factors for sediment classification and proper analysis is needed before using these layers with any classification technique.

Texture classification in aerial photographs using multiscale and multilayer complex networks

2021 ◽  
Author(s):  
M.N. Favorskaya ◽  
A.N. Zhukovskaya
Keyword(s):  
Complex Networks ◽  
Texture Analysis ◽  
Complex Network ◽  
Multilayer Structure ◽  
Statistical Approach ◽  
Texture Classification ◽  
Classification Method ◽  
Aerial Photographs ◽  
Functional Connections

Texture classification using oriented complex networks considers the functional connections between topological elements and simulates the complex textures more accurately. In contrast to the classical spatial texture analysis, we offer a novel function of weights in complex networks and a classification method that takes into account the scaling and color of textures. For this, three complex networks represented R, G and B components are built, which provide invariance of color aerial photographs obtained at different times. Comparison of the classification results using the proposed multiscale complex networks and conventional texture analysis based on a statistical approach is given. Also we extended this approach on color aerial photographs using multilayer structure of complex network.

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