New model for Multiband Texture Analysis

Safia, A. (2014) New model for multiband texture analysis. Philosophiae Doctor (Ph.D.) thesis in remote sensing. Department of Applied Geomatics. Sherbrooke University, Quebec, Canada, 173p. [1]

In multispectral images, texture is typically extracted independently in each band using existing grayscale texture methods. However, reducing texture of multispectral images into a set of independent grayscale texture ignores inter-band spatial interactions which can be a valuable source of information. The main obstacle for characterizing texture as intra- and inter-band spatial interactions is that the required calculations are cumbersome. In the first part of this PhD thesis, a new texture model named the Compact Texture Unit (C‑TU) model was proposed. The C‑TU model is a general solution for the texture spectrum model, in order to decrease its computational complexity. This simplification comes from the fact that the C‑TU model characterizes texture using only statistical information, while the texture spectrum model uses both statistical and structural information. The proposed model was evaluated using a new monoband C‑TU descriptor in the context of texture classification and image retrieval. Results showed that the monoband C‑TU descriptor that uses the proposed C‑TU model provides performances equivalent to those delivered by the texture spectrum model but with much more lower complexity.The calculation efficiency of the proposed C‑TU model is exploited in the second part of this thesis in order to propose a new descriptor for multiband texture characterization. This descriptor, named multiband C‑TU, extracts texture as a set of intra- and inter-band spatial interactions simultaneously. The multiband C‑TU descriptor is very simple to extract and computationally efficient. The proposed descriptor was compared with three strategies commonly adopted in remote sensing. The first is extracting texture using panchromatic data; the second is extracting texture separately from few new-bands obtained by principal components transform; and the third is extracting texture separately in each spectral band. These strategies were applied using cooccurrence matrix and monoband compact texture descriptors. For all experiments, the proposed descriptor provided the best results. In the last part of this thesis, a new color texture images database is developed, named Multiband Brodatz Texture database. Images from this database have two important characteristics. First, their chromatic content, even if it is rich, does not have discriminative value, yet it contributes to form texture. Second, their textural content is characterized by high intra- and inter-band variation. These two characteristics make this database ideal for multiband texture analysis without the influence of color information.



- Compact Texture Unit Codificator: This is a Matlab© implementation for texture codification system based on the concept of compact texture units introduced in [1][2][3].

- Multiband Compact Texture Unit Extraction: This a Matlab© implementation of the Multiband Compact Texture Unit (Multiband C-TU) descriptor proposed in [1][4]. Paper and code will be online soon.



[1] A. Safia, “New model for multiband texture analysis,” Sherbrooke University, 2014. fp
[2] A. Safia and D. He, “Improving the Texture Spectrum Model Using a Compact Texture Unit Descriptor,” J. Commun. Comput., vol. 10,
no. 2, pp. 1–18, 2013. 2p
[3] D.-C. He and L. Wang, “Simplified texture spectrum for texture analysis,” J. Commun. Comput., vol. 7, no. 8, pp. 44–53, 2010. 3p
[4] A. Safia and D.-C. He, “Multiband compact texture unit descriptor for intra-band and inter-band texture analysis,” accepted,
ISPRS J. Photogramm. Remote Sens.
, 2014. 4p

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