scholarly journals Evaluating the Effects of Image Texture Analysis on Plastic Greenhouse Segments via Recognition of the OSI-USI-ETA-CEI Pattern

2019 ◽  
Vol 11 (3) ◽  
pp. 231 ◽  
Author(s):  
Yao Yao ◽  
Shixin Wang
Keyword(s):  
Texture Analysis ◽  
Visual Analysis ◽  
Scale Parameter ◽  
Image Texture ◽  
Quantization Level ◽  
Image Texture Analysis ◽  
Segmentation Evaluation ◽  
Object Based ◽  
Geographic Object ◽  
Error Index

Compared to multispectral or panchromatic bands, fusion imagery contains both the spectral content of the former and the spatial resolution of the latter. Even though the Estimation of Scale Parameter (ESP), the ESP 2 tool, and some segmentation evaluation methods have been introduced to simplify the choice of scale parameter (SP), shape, and compactness, many challenges remain, including obtaining the natural border of plastic greenhouses (PGs) from a GaoFen-2 (GF-2) fusion imagery, accelerating the progress of follow-up texture analysis, and accurately evaluating over-segmentation and under-segmentation of PG segments in geographic object-based image analysis. Considering the features of high-resolution images, the heterogeneity of fusion imagery was compressed using texture analysis before calculating the optimal scale parameter in ESP 2 in this study. As a result, we quantified the effects of image texture analysis, including increasing averaging operator size (AOS) and decreasing greyscale quantization level (GQL) on PG segments via recognition of a proposed Over-Segmentation Index (OSI)-Under-Segmentation Index (USI)-Error Index of Total Area (ETA)-Composite Error Index (CEI) pattern. The proposed pattern can be used to reasonably evaluate the quality of PG segments obtained from GF-2 fusion imagery and its derivative images, showing that appropriate texture analysis can effectively change the heterogeneity of a fusion image for better segmentation. The optimum setup of GQL and AOS are determined by comparing CEI and visual analysis.

Image Texture Analysis based on Color Level Methods using Neural Network

2020 ELEKTRO ◽  
2020 ◽  
Author(s):  
Miroslav Benco ◽  
Patrik Kamencay ◽  
Robert Hudec ◽  
Martina Radilova ◽  
Peter Sykora
Keyword(s):  
Neural Network ◽  
Texture Analysis ◽  
Image Texture ◽  
Image Texture Analysis

Characterization of micrographs and fractographs of Cu-strengthened HSLA steel using image texture analysis

Measurement ◽  
2014 ◽  
Vol 47 ◽  
pp. 130-144 ◽  
Author(s):  
Samik Dutta ◽  
Kaustav Barat ◽  
Arpan Das ◽  
Swapan Kumar Das ◽  
A.K. Shukla ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Hsla Steel ◽  
Image Texture ◽  
Image Texture Analysis

scholarly journals Exploration of Semantic Geo-Object Recognition Based on the Scale Parameter Optimization Method for Remote Sensing Images

2021 ◽  
Vol 10 (6) ◽  
pp. 420
Author(s):  
Jun Wang ◽  
Lili Jiang ◽  
Qingwen Qi ◽  
Yongji Wang
Keyword(s):  
Parameter Optimization ◽  
Scale Parameter ◽  
Human Perception ◽  
Optimization Methods ◽  
Optimization Method ◽  
Initiation Point ◽  
Scale Parameters ◽  
Object Based ◽  
Geographic Object ◽  
Multiresolution Segmentation

Image segmentation is of significance because it can provide objects that are the minimum analysis units for geographic object-based image analysis (GEOBIA). Most segmentation methods usually set parameters to identify geo-objects, and different parameter settings lead to different segmentation results; thus, parameter optimization is critical to obtain satisfactory segmentation results. Currently, many parameter optimization methods have been developed and successfully applied to the identification of single geo-objects. However, few studies have focused on the recognition of the union of different types of geo-objects (semantic geo-objects), such as a park. The recognition of semantic geo-objects is likely more crucial than that of single geo-objects because the former type of recognition is more correlated with the human perception. This paper proposes an approach to recognize semantic geo-objects. The key concept is that a single geo-object is the smallest component unit of a semantic geo-object, and semantic geo-objects are recognized by iteratively merging single geo-objects. Thus, the optimal scale of the semantic geo-objects is determined by iteratively recognizing the optimal scales of single geo-objects and using them as the initiation point of the reset scale parameter optimization interval. In this paper, we adopt the multiresolution segmentation (MRS) method to segment Gaofen-1 images and tested three scale parameter optimization methods to validate the proposed approach. The results show that the proposed approach can determine the scale parameters, which can produce semantic geo-objects.

scholarly journals NIMG-21. IMPACT OF GENETIC ALTERATIONS ON TUMOR LOCATIONS AND LESION HETEROGENEITY FOR WHO GRADE 2 AND 3 GLIOMAS: A VOXEL-BASED LESION MAPPING AND IMAGE TEXTURE ANALYSIS OF 201 GLIOMAS

2016 ◽  
Vol 18 (suppl_6) ◽  
pp. vi128-vi128
Author(s):  
Manabu Kinoshita ◽  
Hideyuki Arita ◽  
Toshiki Yoshimine ◽  
Masamichi Takahashi ◽  
Yoshitaka Narita ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Genetic Alterations ◽  
Image Texture ◽  
Who Grade ◽  
Image Texture Analysis ◽  
Lesion Mapping

An efficient method for image texture analysis

2004 ◽  
Author(s):  
Umasankar Kandaswamy ◽  
Donald A. Adjeroh ◽  
M. C. Lee
Keyword(s):  
Texture Analysis ◽  
Efficient Method ◽  
Image Texture ◽  
Image Texture Analysis

Aging feature extraction of oil-impregnated insulating paper using image texture analysis

2017 ◽  
Vol 24 (3) ◽  
pp. 1636-1645 ◽  
Author(s):  
Shuaibing Li ◽  
Guoqiang Gao ◽  
Guangcai Hu ◽  
Bo Gao ◽  
Tianshan Gao ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Texture Analysis ◽  
Image Texture ◽  
Image Texture Analysis ◽  
Insulating Paper

scholarly journals Optimal Segmentation Scale Parameter, Feature Subset and Classification Algorithm for Geographic Object-Based Crop Recognition Using Multisource Satellite Imagery

2019 ◽  
Vol 11 (5) ◽  
pp. 514 ◽  
Author(s):  
Lingbo Yang ◽  
Lamin Mansaray ◽  
Jingfeng Huang ◽  
Limin Wang
Keyword(s):  
Satellite Imagery ◽  
Scale Parameter ◽  
Selection Method ◽  
Classification Algorithm ◽  
Feature Subset Selection ◽  
Computational Time ◽  
Feature Subset ◽  
Object Based ◽  
Geographic Object ◽  
Crop Classification

Geographic object-based image analysis (GEOBIA) has been widely used in the remote sensing of agricultural crops. However, issues related to image segmentation, data redundancy and performance of different classification algorithms with GEOBIA have not been properly addressed in previous studies, thereby compromising the accuracy of subsequent thematic products. It is in this regard that the current study investigates the optimal scale parameter (SP) in multi-resolution segmentation, feature subset, and classification algorithm for use in GEOBIA based on multisource satellite imagery. For this purpose, a novel supervised optimal SP selection method was proposed based on information gain ratio, and was then compared with a preexisting unsupervised optimal SP selection method. Additionally, the recursive feature elimination (RFE) and enhanced RFE (EnRFE) algorithms were modified to generate an improved EnRFE (iEnRFE) algorithm, which was then compared with its precursors in the selection of optimal classification features. Based on the above, random forest (RF), gradient boosting decision tree (GBDT) and support vector machine (SVM) were applied to segmented objects for crop classification. The results indicated that the supervised optimal SP selection method is more suitable for application in heterogeneous land cover, whereas the unsupervised method proved more efficient as it does not require reference segmentation objects. The proposed iEnRFE method outperformed the preexisting EnRFE and RFE methods in optimal feature subset selection as it recorded the highest accuracy and less processing time. The RF, GBDT, and SVM algorithms achieved overall classification accuracies of 91.8%, 92.4%, and 90.5%, respectively. GBDT and RF recorded higher classification accuracies and utilized much less computational time than SVM and are, therefore, considered more suitable for crop classification requiring large numbers of image features. These results have shown that the proposed object-based crop classification scheme could provide a valuable reference for relevant applications of GEOBIA in crop recognition using multisource satellite imagery.

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