Unsupervised Classification of Fully Polarimetric SAR Image Based on Polarimetric Features and Spatial Features

Xia, Xue; Di, Liping; Guo, Liying; Lin, Li

doi:10.12928/telkomnika.v14i3a.4403

Cited by 4 publications

(1 citation statement)

References 12 publications

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“…This approach mainly classifies the texture image into two groups only and each group consists of 4 different types of texture images. Ratna Bhargavi et al [20] proposed an approach for detection of Lesion using texture features and Xiaorong Xue et.al [21] proposed an approach for Classification of Fully Polari metric SAR Images based on Polari metric Features and Spatial Features.…”

Section: A Novel Approach Based On Decreased Dimension and Reduced Grmentioning

confidence: 99%

A Novel Approach Based on Decreased Dimension and Reduced Gray Level Range Matrix Features for Stone Texture Classification

Murthy¹,

Rao.²,

Veerraju³

2017

IJECE

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The human eye can easily identify the type of textures in flooring of the houses and in the digital images visually. In this work, the stone textures are grouped into four categories. They are bricks, marble, granite and mosaic. A novel approach is developed for decreasing the dimension of stone image and for reducing the gray level range of the image without any loss of significant feature information. This model is named as "Decreased Dimension and Reduced Gray level Range Matrix (DDRGRM)" model. The DDRGRM model consists of 3 stages. In stage 1, each 5×5 sub dimension of the stone image is reduced into 2×2 sub dimension without losing any important qualities, primitives, and any other local stuff. In stage 2, the gray level of the image is reduced from 0-255 to 0-4 by using fuzzy concepts. In stage 3, Cooccurrence Matrix (CM) features are derived from the DDRGRM model of the stone image for stone texture classification. Based on the feature set values, a user defined algorithm is developed to classify the stone texture image into one of the 4 categories i.e. Marble, Brick, Granite and Mosaic. The proposed method is tested by using the K-Nearest Neighbor Classification algorithm with the derived texture features. To prove the efficiency of the proposed method, it is tested on different stone texture image databases. The proposed method resulted in high classification rate when compared with the other existing methods.

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Section: A Novel Approach Based On Decreased Dimension and Reduced Grmentioning

confidence: 99%

A Novel Approach Based on Decreased Dimension and Reduced Gray Level Range Matrix Features for Stone Texture Classification

Murthy¹,

Rao.²,

Veerraju³

2017

IJECE

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show abstract

Developing a Semantic Irrigation Ontology to Support WaterSmart System: A Demonstration of Reducing Water and Energy Consumption in Nebraska

Lin¹,

Di²,

Guo³

et al. 2020

Preprint

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Developing a Semantic Irrigation Ontology to Support WaterSmart System: A Demonstration of Reducing Water and Energy Consumption in Nebraska

Lin

Guo

et al. 2021

Preprint

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Unsupervised Classification of Fully Polarimetric SAR Image Based on Polarimetric Features and Spatial Features

Abstract: Polarimetric SAR (PolSAR)

Cited by 4 publications

References 12 publications

A Novel Approach Based on Decreased Dimension and Reduced Gray Level Range Matrix Features for Stone Texture Classification

A Novel Approach Based on Decreased Dimension and Reduced Gray Level Range Matrix Features for Stone Texture Classification

Developing a Semantic Irrigation Ontology to Support WaterSmart System: A Demonstration of Reducing Water and Energy Consumption in Nebraska

Developing a Semantic Irrigation Ontology to Support WaterSmart System: A Demonstration of Reducing Water and Energy Consumption in Nebraska

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