Statistical Class Feature in Texture Analysis of Remote Sensing Imagery

Teng, Xin; Song, Shun Lin; Zhan, Yong Zhao

doi:10.4028/www.scientific.net/amr.518-523.5749

Cited by 4 publications

(3 citation statements)

References 8 publications

(14 reference statements)

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“…Rao et al [ 18 ] extracted two-order statistical parameters from the GLCM of the liquid crystal texture, including contrast, energy, uniformity and correlation, and identified the phase transition temperature of the crystal. Teng et al [ 19 ] selected five typical texture class samples from Quick Bird data and used GLCM to quantitatively calculate six statistical texture features that were obtained by computing the average values in four directions and one pixel of pair-wise distance. The paper discussed which parameters were suitable for the specific texture classification.…”

Section: Introductionmentioning

confidence: 99%

A Study for Texture Feature Extraction of High-Resolution Satellite Images Based on a Direction Measure and Gray Level Co-Occurrence Matrix Fusion Algorithm

Xin

Cui

Wang

et al. 2017

Sensors

177

101

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To address the problem of image texture feature extraction, a direction measure statistic that is based on the directionality of image texture is constructed, and a new method of texture feature extraction, which is based on the direction measure and a gray level co-occurrence matrix (GLCM) fusion algorithm, is proposed in this paper. This method applies the GLCM to extract the texture feature value of an image and integrates the weight factor that is introduced by the direction measure to obtain the final texture feature of an image. A set of classification experiments for the high-resolution remote sensing images were performed by using support vector machine (SVM) classifier with the direction measure and gray level co-occurrence matrix fusion algorithm. Both qualitative and quantitative approaches were applied to assess the classification results. The experimental results demonstrated that texture feature extraction based on the fusion algorithm achieved a better image recognition, and the accuracy of classification based on this method has been significantly improved.

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Section: Introductionmentioning

confidence: 99%

A Study for Texture Feature Extraction of High-Resolution Satellite Images Based on a Direction Measure and Gray Level Co-Occurrence Matrix Fusion Algorithm

Xin

Cui

Wang

et al. 2017

Sensors

177

101

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show abstract

“…GLCM describes the texture by measuring the spatial correlation features of the spectrum on the image [32]. The standard deviation is one of its statistics, which is different from the simple standard deviation of the grayscale in the image.…”

Section: ) Texture Featuresmentioning

confidence: 99%

Identification and Evaluation of Urban Construction Waste with VHR Remote Sensing Using Multi-Feature Analysis and a Hierarchical Segmentation Method

et al. 2021

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With rapid urbanization, the disposal and management of urban construction waste have become the main concerns of urban management. The distribution of urban construction waste is characterized by its wide range, irregularity, and ease of confusion with the surrounding ground objects, such as bare soil, buildings, and vegetation. Therefore, it is difficult to extract and identify information related to urban construction waste by using the traditional single spectral feature analysis method due to the problem of spectral confusion between construction waste and the surrounding ground objects, especially in the context of very-high-resolution (VHR) remote sensing images. Considering the multi-feature analysis method for VHR remote sensing images, we propose an optimal method that combines morphological indexing and hierarchical segmentation to extract the information on urban construction waste in VHR images. By comparing the differences between construction waste and the surrounding ground objects in terms of the spectrum, geometry, texture, and other features, we selected an optimal feature subset to improve the separability of the construction waste and other objects; then, we established a classification model of knowledge rules to achieve the rapid and accurate extraction of construction waste information. We also chose two experimental areas of Beijing to validate our algorithm. By using construction waste separability quality evaluation indexes, the identification accuracy of construction waste in the two study areas was determined to be 96.6% and 96.2%, the separability indexes of the construction waste and buildings reached 1.000, and the separability indexes of the construction waste and vegetation reached 1.000 and 0.818. The experimental results show that our method can accurately identify the exposed construction waste and construction waste covered with a dust screen, and it can effectively solve the problem of spectral confusion between the construction waste and the bare soil, buildings, and vegetation.

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“…Rao et al [18] extricated two-request factual boundaries from the GLCM of the fluid precious stone surface, including contrast, energy, consistency and relationship, and recognized the stage change temperature of the gem. Teng et al [19] chose five run of the mill surface class tests from Quick Bird information and utilized GLCM to quantitatively ascertain six factual surface highlights that were gotten by figuring the normal values in four ways and one pixel of pair-wise distance. The paper talked about which boundaries were reasonable for the particular surface arrangement.…”

Section: Related Workmentioning

confidence: 99%

Classification of Leaf Diseases in Paddy Plant Based on Combined Approach of Texture and Colour Feature Extraction and Optimized Feature Selection

Nithiya¹,

Annapurani²

2021

Proceedings of the First International Conference on Computing, Communication and Control System, I3CAC 2021, 7-8 June 2021, Bh

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This paper proposes a methodology for the classification of leaf diseases by using the different characterization of shape, and colour properties. Paddy plant diseases are discussed in this research work. Bacterial light, Brown Spot, Leaf smut diseases is identified in paddy crops. A plant leaf is pre-processed at first utilizing the improved BAHE (brightness adapted histogram equalization). Leaf image is segmented using K mean clustering algorithm. The feature extraction is improved by combination of texture feature and colour features. The extracted feature is given as an input for feature selection using optimized firefly algorithm's after that it is used for the classification of diseases in paddy plant. The optimization result is compared with PSO and Genetic algorithm..

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Statistical Class Feature in Texture Analysis of Remote Sensing Imagery

Cited by 4 publications

References 8 publications

A Study for Texture Feature Extraction of High-Resolution Satellite Images Based on a Direction Measure and Gray Level Co-Occurrence Matrix Fusion Algorithm

A Study for Texture Feature Extraction of High-Resolution Satellite Images Based on a Direction Measure and Gray Level Co-Occurrence Matrix Fusion Algorithm

Identification and Evaluation of Urban Construction Waste with VHR Remote Sensing Using Multi-Feature Analysis and a Hierarchical Segmentation Method

Classification of Leaf Diseases in Paddy Plant Based on Combined Approach of Texture and Colour Feature Extraction and Optimized Feature Selection

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