scholarly journals Seismic Damage Recognition Based on Watershed Segmentation of SAR Image Texture Features

2016 ◽  
Author(s):  
Qiang Li ◽  
Lixia Gong ◽  
Jingfa Zhang
Keyword(s):  
Change Detection ◽  
Texture Feature ◽  
Texture Features ◽  
Seismic Damage ◽  
Segmentation Algorithm ◽  
Image Texture ◽  
Sar Image ◽  
Watershed Segmentation ◽  
Sar Images ◽  
Feature Parameters

The information of seismic damage of buildings in SAR images of different time phase, especially in SAR images after earthquake, is easily disturbed by other factors, which affects the accuracy of information discrimination. In order to identify and evaluate the distribution information of the seismic damage accurately and make full use of the abundant texture features in the SAR image. The conventional method of change detection based on texture features usually takes the pixel as the calculating unit. In this paper, a method of texture feature change detection of SAR images based on watershed segmentation algorithm is proposed. Based on the optimization of texture feature parameters, the feature parameters are segmented by the watershed segmentation algorithm, and the feature object image is obtained. This method introduces the idea of object oriented, and carries out the calculation of the difference map at the object level, Finally, the classification threshold value of different types of seismic damage types is selected, and the recognition of building damage is achieved. Taking the ALOS data before and after the earthquake in Yushu as an example to verify the effectiveness of the method, the overall accuracy of the building extraction is 88.9%, Compared with pixel-based methods, it is proved that the proposed method is effective.

scholarly journals Research on college gymnastics teaching model based on multimedia image and image texture feature analysis

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Ying Wu ◽  
Jikun Liu
Keyword(s):  
Human Body ◽  
Rapid Development ◽  
Texture Feature ◽  
Texture Features ◽  
Dynamics Simulation ◽  
Image Texture ◽  
Whole Body ◽  
Database Modeling ◽  
Rigid Model ◽  
The Subject

AbstractWith the rapid development of gymnastics technology, novel movements are also emerging. Due to the emergence of various complicated new movements, higher requirements are put forward for college gymnastics teaching. Therefore, it is necessary to combine the multimedia simulation technology to construct the human body rigid model and combine the image texture features to display the simulation image in texture form. In the study, GeBOD morphological database modeling was used to provide the data needed for the modeling of the whole-body human body of the joint and used for dynamics simulation. Simultaneously, in order to analyze and summarize the technical essentials of the innovative action, this experiment compared and analyzed the hem stage of the cross-headstand movement of the subject and the hem stage of the 180° movement. Research shows that the method proposed in this paper has certain practical effects.

scholarly journals Delineation of Cocoa Agroforests Using Multiseason Sentinel-1 SAR Images: A Low Grey Level Range Reduces Uncertainties in GLCM Texture-Based Mapping.

2019 ◽  
Vol 8 (4) ◽  
pp. 179 ◽  
Author(s):  
Frederick N. Numbisi ◽  
Frieke M. B. Van Coillie ◽  
Robert De Wulf
Keyword(s):  
Optical Sensors ◽  
Spectral Characteristics ◽  
Texture Feature ◽  
Image Texture ◽  
Optical Data ◽  
Grey Level ◽  
Sar Images ◽  
Quantization Level ◽  
Class Prediction ◽  
Sar Imagery

Delineating the cropping area of cocoa agroforests is a major challenge in quantifying the contribution of land use expansion to tropical deforestation. Discriminating cocoa agroforests from tropical transition forests using multispectral optical images is difficult due to the similarity of the spectral characteristics of their canopies. Moreover, the frequent cloud cover in the tropics greatly impedes optical sensors. This study evaluated the potential of multiseason Sentinel-1 C-band synthetic aperture radar (SAR) imagery to discriminate cocoa agroforests from transition forests in a heterogeneous landscape in central Cameroon. We used an ensemble classifier, Random Forest (RF), to average the SAR image texture features of a grey level co-occurrence matrix (GLCM) across seasons. We then compared the classification performance with results from RapidEye optical data. Moreover, we assessed the performance of GLCM texture feature extraction at four different grey levels of quantization: 32 bits, 8 bits, 6 bits, and 4 bits. The classification’s overall accuracy (OA) from texture-based maps outperformed that from an optical image. The highest OA (88.8%) was recorded at the 6 bits grey level. This quantization level, in comparison to the initial 32 bits in the SAR images, reduced the class prediction error by 2.9%. The texture-based classification achieved an acceptable accuracy and revealed that cocoa agroforests have considerably fragmented the remnant transition forest patches. The Shannon entropy (H) or uncertainty provided a reliable validation of the class predictions and enabled inferences about discriminating inherently heterogeneous vegetation categories.

scholarly journals Two-Phase Object-Based Deep Learning for Multi-Temporal SAR Image Change Detection

2020 ◽  
Vol 12 (3) ◽  
pp. 548 ◽  
Author(s):  
Xinzheng Zhang ◽  
Guo Liu ◽  
Ce Zhang ◽  
Peter M. Atkinson ◽  
Xiaoheng Tan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Speckle Noise ◽  
Sar Image ◽  
Phase Object ◽  
Sar Images ◽  
Two Phase ◽  
Object Based ◽  
Multi Temporal ◽  
Image Change Detection

Change detection is one of the fundamental applications of synthetic aperture radar (SAR) images. However, speckle noise presented in SAR images has a negative effect on change detection, leading to frequent false alarms in the mapping products. In this research, a novel two-phase object-based deep learning approach is proposed for multi-temporal SAR image change detection. Compared with traditional methods, the proposed approach brings two main innovations. One is to classify all pixels into three categories rather than two categories: unchanged pixels, changed pixels caused by strong speckle (false changes), and changed pixels formed by real terrain variation (real changes). The other is to group neighbouring pixels into superpixel objects such as to exploit local spatial context. Two phases are designed in the methodology: (1) Generate objects based on the simple linear iterative clustering (SLIC) algorithm, and discriminate these objects into changed and unchanged classes using fuzzy c-means (FCM) clustering and a deep PCANet. The prediction of this Phase is the set of changed and unchanged superpixels. (2) Deep learning on the pixel sets over the changed superpixels only, obtained in the first phase, to discriminate real changes from false changes. SLIC is employed again to achieve new superpixels in the second phase. Low rank and sparse decomposition are applied to these new superpixels to suppress speckle noise significantly. A further clustering step is applied to these new superpixels via FCM. A new PCANet is then trained to classify two kinds of changed superpixels to achieve the final change maps. Numerical experiments demonstrate that, compared with benchmark methods, the proposed approach can distinguish real changes from false changes effectively with significantly reduced false alarm rates, and achieve up to 99.71% change detection accuracy using multi-temporal SAR imagery.

scholarly journals Cotton stubble detection based on wavelet decomposition and texture features

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Yukun Yang ◽  
Jing Nie ◽  
Za Kan ◽  
Shuo Yang ◽  
Hangxing Zhao ◽  
...  
Keyword(s):  
Navigation System ◽  
Wavelet Decomposition ◽  
Back Propagation ◽  
Texture Feature ◽  
Texture Features ◽  
Back Propagation Neural Network ◽  
Visual Navigation ◽  
Image Texture ◽  
Original Image ◽  
Residual Film

Abstract Background At present, the residual film pollution in cotton fields is crucial. The commonly used recycling method is the manual-driven recycling machine, which is heavy and time-consuming. The development of a visual navigation system for the recovery of residual film is conducive, in order to improve the work efficiency. The key technology in the visual navigation system is the cotton stubble detection. A successful cotton stubble detection can ensure the stability and reliability of the visual navigation system. Methods Firstly, it extracts the three types of texture features of GLCM, GLRLM and LBP, from the three types of images of stubbles, residual films and broken leaves between rows. It then builds three classifiers: Random Forest, Back Propagation Neural Network and Support Vector Machine in order to classify the sample images. Finally, the possibility of improving the classification accuracy using the texture features extracted from the wavelet decomposition coefficients, is discussed. Results The experiment proves that the GLCM texture feature of the original image has the best performance under the Back Propagation Neural Network classifier. As for the different wavelet bases, the vertical coefficient texture feature of coif3 wavelet decomposition, combined with the texture feature of the original image, is the feature having the best classification effect. Compared with the original image texture features, the classification accuracy is increased by 3.8%, the sensitivity is increased by 4.8%, and the specificity is increased by 1.2%. Conclusions The algorithm can complete the task of stubble detection in different locations, different periods and abnormal driving conditions, which shows that the wavelet coefficient texture feature combined with the original image texture feature is a useful fusion feature for detecting stubble and can provide a reference for different crop stubble detection.

An identification method of cashmere and wool by the two features fusion

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yaolin Zhu ◽  
Jiayi Huang ◽  
Tong Wu ◽  
Xueqin Ren
Keyword(s):  
Textile Industry ◽  
Texture Feature ◽  
Texture Features ◽  
Identification Accuracy ◽  
Impact Factors ◽  
Content Type ◽  
Identification Method ◽  
Feature Parameters ◽  
The Impact ◽  
Optimal Feature

PurposeThe purpose of this paper is to select the optimal feature parameters to further improve the identification accuracy of cashmere and wool.Design/methodology/approachTo increase the accuracy, the authors put forward a method selecting optimal parameters based on the fusion of morphological feature and texture feature. The first step is to acquire the fiber diameter measured by the central axis algorithm. The second step is to acquire the optimal texture feature parameters. This step is mainly achieved by using the variance of secondary statistics of these two texture features to get four statistics and then finding the impact factors of gray level co-occurrence matrix relying on the relationship between the secondary statistic values and the pixel pitch. Finally, the five-dimensional feature vectors extracted from the sample image are fed into the fisher classifier.FindingsThe improvement of identification accuracy can be achieved by determining the optimal feature parameters and fusing two texture features. The average identification accuracy is 96.713% in this paper, which is very helpful to improve the efficiency of detector in the textile industry.Originality/valueIn this paper, a novel identification method which extracts the optimal feature parameter is proposed.

TEXTURE ANALYSIS USING LOCAL TEXTURE PATTERNS: A FUZZY LOGIC APPROACH

2011 ◽  
Vol 25 (05) ◽  
pp. 741-762 ◽  
Author(s):  
E. M. SRINIVASAN ◽  
K. RAMAR ◽  
A. SURULIANDI
Keyword(s):  
Texture Analysis ◽  
Texture Classification ◽  
Texture Feature ◽  
Texture Features ◽  
Vital Role ◽  
Image Texture ◽  
Feature Extraction Method ◽  
Local Texture ◽  
Texture Model ◽  
Fuzzy Logic Approach

Texture analysis plays a vital role in image processing. The prospect of texture based image analysis depends on the texture features and the texture model. This paper presents a new texture feature extraction method 'Fuzzy Local Texture Patterns (FLTP)' and 'Fuzzy Pattern Spectrum (FPS)', suitable for texture analysis. The local image texture is described by FLTP and the global image texture is described by FPS. The proposed method is tested with texture classification, texture segmentation and texture edge detection. The results show that the proposed method provides a very good and robust performance for texture analysis.

scholarly journals Texture Analysis Based on Pi-Rads 4/5-Scored MRI Images Combined with Machine Learning to Distinguish Benign Lesions from Prostate Cancer

2021 ◽  
Author(s):  
Lu Ma ◽  
Qi Zhou ◽  
Huming Yin ◽  
Xiaojie Ang ◽  
Yu Li ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Texture Feature ◽  
Texture Features ◽  
Classification Model ◽  
Clinical Indicators ◽  
Model Based ◽  
Feature Parameters ◽  
Auc Value ◽  
Malignant Prostate

Abstract Background: To extract the texture features of Apparent Diffusion Coefficient (ADC) images in Mp-MRI and build a machine learning model based on radiomics texture analysis to determine its ability to distinguish benign from prostate cancer (PCa) lesions using PI-RADS 4/5 score.Materials and methods: First, use ImageJ software to obtain texture feature parameters based on ADC images; use R language to standardize texture feature parameters, and use Lasso regression to reduce the dimensionality of multiple feature parameters; then, use the feature parameters after dimensionality reduction to construct image-based groups. Learn R-Logistic, R-SVM, R-AdaBoost to identify the machine learning classification model of prostate benign and malignant nodules. Secondly, the clinical indicators of the patients were statistically analyzed, and the three clinical indicators with the largest AUC values were selected to establish a classification model based on clinical indicators of benign and malignant prostate nodules. Finally, compare the performance of the model based on radiomics texture features and clinical indicators to identify benign and malignant prostate nodules in PI-RADS 4/5.Results: The experimental results show that the AUC of the R-Logistic model test set is 0.838, which is higher than the R-SVM and R-AdaBoost classification models. At this time, the corresponding R-Logistic classification model formula is: Y_radiomics=9.396-7.464*median ADC-0.584 *kurtosis+0.627*skewness+0.576*MRI lesions volume; analysis of clinical indicators shows that the 3 indicators with the highest discrimination efficiency are PSA, Fib, LDL-C, and the corresponding C-Logistic classification model formula is: Y_clinical =-2.608 +0.324*PSA-3.045*Fib+4.147*LDL-C, the AUC value of the model training set is 0.860, which is smaller than the training set R-Logistic classification model AUC value of 0.936.Conclusion: The machine learning classifier model is established based on the texture features of radiomics. It has a good classification performance in identifying benign and malignant nodules of the prostate in PI-RADS 4/5. This has certain potential and clinical value for patients with prostate cancer to adopt different treatment methods and prognosis.

scholarly journals A Fusion Method of Optical Image and SAR Image Based on Dense-UGAN and Gram–Schmidt Transformation

2021 ◽  
Vol 13 (21) ◽  
pp. 4274
Author(s):  
Yingying Kong ◽  
Fang Hong ◽  
Henry Leung ◽  
Xiangyang Peng
Keyword(s):  
Loss Function ◽  
Texture Features ◽  
Speckle Noise ◽  
Source Image ◽  
Fusion Method ◽  
Sar Image ◽  
Sar Images ◽  
Spatial Features ◽  
Optical Images ◽  
Fusion Methods

To solve the problems such as obvious speckle noise and serious spectral distortion when existing fusion methods are applied to the fusion of optical and SAR images, this paper proposes a fusion method for optical and SAR images based on Dense-UGAN and Gram–Schmidt transformation. Firstly, dense connection with U-shaped network (Dense-UGAN) are used in GAN generator to deepen the network structure and obtain deeper source image information. Secondly, according to the particularity of SAR imaging mechanism, SGLCM loss for preserving SAR texture features and PSNR loss for reducing SAR speckle noise are introduced into the generator loss function. Meanwhile in order to keep more SAR image structure, SSIM loss is introduced to discriminator loss function to make the generated image retain more spatial features. In this way, the generated high-resolution image has both optical contour characteristics and SAR texture characteristics. Finally, the GS transformation of optical and generated image retains the necessary spectral properties. Experimental results show that the proposed method can well preserve the spectral information of optical images and texture information of SAR images, and also reduce the generation of speckle noise at the same time. The metrics are superior to other algorithms that currently perform well.

An Advanced Morphological Component Analysis, Steganography, and Deep Learning-Based System to Transmit Secure Textual Data

2021 ◽  
Vol 13 (2) ◽  
pp. 40-62
Author(s):  
Binay Kumar Pandey ◽  
Digvijay Pandey ◽  
Subodh Wairya ◽  
Gaurav Agarwal
Keyword(s):  
Signal To Noise Ratio ◽  
Texture Feature ◽  
Texture Features ◽  
Performance Measure ◽  
Previous Method ◽  
Image Texture ◽  
Least Significant Bit ◽  
Suggested Approach ◽  
Morphological Component Analysis ◽  
Morphological Component

A potential to extract detailed textual image texture features is a key characteristic of the suggested approach, instead of using a single spatial texture feature. For the generation of MCs, four textured characteristics (including horizontal and vertical) are assumed in this paper that are content, coarseness, contrast, and directionality. The morphological parts of a clandestine text-based image were further segmented and then usually inserted into the least significant bit in cover pixels utilising spatial steganography. This same reverse process for steganography and MCA is conducted on the recipient side after transmission. The results demonstrate that the proposed method based on fusion of MCA and steganography provides a higher performance measure, for instance peak signal-to-noise ratio, SSIM, than the previous method.

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