scholarly journals EXTRACTION OF BUILT-UP AREA BY COMBINING TEXTURAL FEATURES AND SPECTRAL INDICES FROM LANDSAT-8 MULTISPECTRAL IMAGE

2018 ◽  
Vol XLII-5 ◽  
pp. 727-733 ◽  
Author(s):  
V. S. Bramhe ◽  
S. K. Ghosh ◽  
P. K. Garg
Keyword(s):  
Correlation Energy ◽  
Feature Space ◽  
Cost Effective ◽  
Misclassification Error ◽  
Support Vector ◽  
Svm Classifier ◽  
Landsat 8 ◽  
Textural Features ◽  
Spectral Indices ◽  
Spectral Channels

<p><strong>Abstract.</strong> Remote sensing techniques provide efficient and cost-effective approach to monitor the expansion of built-up area, in comparison to other traditional approaches. For extracting built-up class, one of the common approaches is to use spectral and spatial features such as, Normalized Difference Built- up index (NDBI), GLCM texture, Gabor filters etc. However, it is observed that classes such as river soil and fallow land usually mix up with built-up class due to their close spectral similarity. Intermixing of classes have been observed in the classified image when using spectral channels. In this paper, an approach has been proposed which uses urban based spectral indices and textural features to extract built-up areas. Three well known spectral indices i.e. NDBI, Built-up Area Extraction Index (BAEI) and Normalized Difference Bareness Index (NDBai) have been used in this work. Along with spectral indices, local spatial dependency of neighborhood regions is captured using eight GLCM based textural feature, such as, Contrast, Correlation, Energy and Homogeneity etc. for each image band. All textural and spectral indices bands are combined and used for extracting built-up areas using Support Vector Machine (SVM) classifier. Results suggest 4.91% increase in overall accuracy when using texture and spectral indices in comparison with 84.38% overall accuracy achieved when using spectral data only. It is observed that built-up class are more separable in the projected spectral-spatial feature space in comparison to spectral channels. Incorporation of textural features with spectral features reduces the misclassification error and provides results with less salt and pepper noise.</p>

scholarly journals Breast Thermograms Asymmetry Analysis using Gabor filters

2021 ◽  
Vol 309 ◽  
pp. 01109
Author(s):  
Priyanka Yadlapalli ◽  
Madhavi K Reddy ◽  
Sunitha Gurram ◽  
J Avanija ◽  
K Meenakshi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Early Stage ◽  
Cost Effective ◽  
Gabor Filters ◽  
Support Vector ◽  
Svm Classifier ◽  
Infrared Thermal Imaging ◽  
Younger Women ◽  
Early Diagnosis Of Cancer ◽  
Diagnosis Of Cancer

Women are far more likely than males to acquire breast cancer, and current research indicates that this is entirely avoidable. It is also to blame for higher death rates among younger women compared to older women in nearly all developing nations. Medical imaging modalities are continuously in need of development. A variety of medical techniques have been employed to detect breast cancer in women. The most recent studies support mammography for breast cancer screening, although its sensitivity and specificity remain suboptimal, particularly in individuals with thick breast tissue, such as young women. As a result, alternative modalities, such as thermography, are required. Digital Infrared Thermal Imaging (DITI), as it is known, detects and records temperature changes on the skin’s surface. Thermography is well-known for its non-invasive, painless, cost-effective, and high recovery rates, as well as its potential to identify breast cancer at an early stage. Gabor filters are used to extract the textural characteristics of the left and right breasts. Using a support vector machine, the thermograms are then classified as normal or malignant based on textural asymmetry between the breasts (SVM). The accuracy achieved by combining Gabor features with an SVM classifier is around 84.5 percent. The early diagnosis of cancer with thermography enhances the patient’s chances of survival significantly since it may detect the disease in its early stages.

scholarly journals AUTOMATIC FEATURE EXTRACTION FOR BREAST DENSITY SEGMENTATION AND CLASSIFICATION

2017 ◽  
Vol 10 (12) ◽  
pp. 111 ◽  
Author(s):  
Aswathy K Cherian ◽  
Poovammal E ◽  
Malathy C
Keyword(s):  
Human Life ◽  
Region Of Interest ◽  
Support Vector ◽  
Svm Classifier ◽  
Textural Features ◽  
Automatic Feature Extraction ◽  
Wide Range ◽  
Risk Of Cancer ◽  
Cancerous Cells ◽  
Aided Diagnosis

Objective: Cancer is the uncontrollable multiplication of cells in human body. The expansion of cancerous cells in the breast area of the women is identified as breast cancer. It is mostly identified among women aged above 40. With the current advancement in the medical field, various automatic tests are available for the identification of cancerous tissues. The cancerous cells are spotted by taking the photo imprint in the form of X-ray comprising the breast area of the woman. Such images are called mammograms. Segmentation of mammograms is the primary step toward diagnosis. It involves the pre-processing of the image to identify the region of interest (ROI). Later, features are extracted from the image which involves the learned features that may be statistical and textural features [7]. When these features are used as input to the simple classifier, it helps us to predict the risk of cancer. The support vector machine (SVM) classifier was proved to produce a better accuracy percentage with the features extracted.Methods: The mammograms are subjected to a pre-processing stage, where the images are processed to identify the ROI. Next, the features are extracted from these images to identify the statistical [9] and textural features. Finally, these features are used as input to the simple classifier, it helps us to predict the risk of cancer.Results: The SVM classifier was proved to produce the maximum accuracy of about 88.67% considering 13 features including both statistical and textural features. The features taken for the study are mean, inverse difference moment, energy, entropy, root mean square, correlation, homogeneity, variance, skewness, range, contrast, kurtosis, and smoothness.Conclusion: Computer-aided diagnosis is one of the most common methods of detection of cancer with mammograms, and it involves minor human intervention. The dataset of mammograms was analyzed and found that SVM provided the highest accuracy of 88.67%. A wide range of the study is progressing in the field of cancer as this disease causes a high threat of human life in this era.

scholarly journals Staging Melanocytic Skin Neoplasms Using High-Level Pixel-Based Features

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1443
Author(s):  
Mai Ramadan Ibraheem ◽  
Shaker El-Sappagh ◽  
Tamer Abuhmed ◽  
Mohammed Elmogy
Keyword(s):  
Area Under The Curve ◽  
Malignant Neoplasm ◽  
Texture Features ◽  
Feature Space ◽  
Skin Neoplasms ◽  
Support Vector ◽  
Svm Classifier ◽  
Abnormal Growth ◽  
Malignant Skin ◽  
High Level

The formation of malignant neoplasm can be seen as deterioration of a pre-malignant skin neoplasm in its functionality and structure. Distinguishing melanocytic skin neoplasms is a challenging task due to their high visual similarity with different types of lesions and the intra-structural variants of melanocytic neoplasms. Besides, there is a high visual likeliness level between different lesion types with inhomogeneous features and fuzzy boundaries. The abnormal growth of melanocytic neoplasms takes various forms from uniform typical pigment network to irregular atypical shape, which can be described by border irregularity of melanocyte lesion image. This work proposes analytical reasoning for the human-observable phenomenon as a high-level feature to determine the neoplasm growth phase using a novel pixel-based feature space. The pixel-based feature space, which is comprised of high-level features and other color and texture features, are fed into the classifier to classify different melanocyte neoplasm phases. The proposed system was evaluated on the PH2 dermoscopic images benchmark dataset. It achieved an average accuracy of 95.1% using a support vector machine (SVM) classifier with the radial basis function (RBF) kernel. Furthermore, it reached an average Disc similarity coefficient (DSC) of 95.1%, an area under the curve (AUC) of 96.9%, and a sensitivity of 99%. The results of the proposed system outperform the results of other state-of-the-art multiclass techniques.

scholarly journals Evaluating the Spectral Indices Efficiency to Quantify Daytime Surface Anthropogenic Heat Island Intensity: An Intercontinental Methodology

2020 ◽  
Vol 12 (17) ◽  
pp. 2854 ◽  
Author(s):  
Mohammad Karimi Firozjaei ◽  
Solmaz Fathololoumi ◽  
Naeim Mijani ◽  
Majid Kiavarz ◽  
Salman Qureshi ◽  
...  
Keyword(s):  
Los Angeles ◽  
Surface Temperature ◽  
Feature Space ◽  
Least Square ◽  
Anthropogenic Heat ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Heat Island ◽  
Spectral Indices ◽  
Spatial Coverage

The surface anthropogenic heat island (SAHI) phenomenon is one of the most important environmental concerns in urban areas. SAHIs play a significant role in quality of urban life. Hence, the quantification of SAHI intensity (SAHII) is of great importance. The impervious surface cover (ISC) can well reflect the degree and extent of anthropogenic activities in an area. Various actual ISC (AISC) datasets are available for different regions of the world. However, the temporal and spatial coverage of available and accessible AISC datasets is limited. This study was aimed to evaluate the spectral indices efficiency to daytime SAHII (DSAHII) quantification. Consequently, 14 cities including Budapest, Bucharest, Ciechanow, Hamburg, Lyon, Madrid, Porto, and Rome in Europe and Dallas, Seattle, Minneapolis, Los Angeles, Chicago, and Phoenix in the USA, were selected. A set of 91 Landsat 8 images, the Landsat provisional surface temperature product, the High Resolution Imperviousness Layer (HRIL), and the National Land Cover Database (NLCD) imperviousness data were used as the AISC datasets for the selected cities. The spectral index-based ISC (SIISC) and land surface temperature (LST) were modelled from the Landsat 8 images. Then, a linear least square model (LLSM) obtained from the LST-AISC feature space was applied to quantify the actual SAHII of the selected cities. Finally, the SAHII of the selected cities was modelled based on the LST-SIISC feature space-derived LLSM. Finally, the values of the coefficient of determination (R2) and the root mean square error (RMSE) between the actual and modelled SAHII were calculated to evaluate and compare the performance of different spectral indices in SAHII quantification. The performance of the spectral indices used in the built LST-SIISC feature space for SAHII quantification differed. The index-based built-up index (IBI) (R2 = 0.98, RMSE = 0.34 °C) and albedo (0.76, 1.39 °C) performed the best and worst performance in SAHII quantification, respectively. Our results indicate that the LST-SIISC feature space is very useful and effective for SAHII quantification. The advantages of the spectral indices used in SAHII quantification include (1) synchronization with the recording of thermal data, (2) simplicity, (3) low cost, (4) accessibility under different spatial and temporal conditions, and (5) scalability.

scholarly journals Automatic Classification of Major Urban Land Covers Based on Novel Spectral Indices

2018 ◽  
Vol 7 (12) ◽  
pp. 453 ◽  
Author(s):  
Mst Ilme Faridatul ◽  
Bo Wu
Keyword(s):  
Land Cover ◽  
Land Cover Classification ◽  
Urban Land ◽  
Support Vector ◽  
Landsat 8 ◽  
Spectral Indices ◽  
Urban Land Cover ◽  
Svm Algorithm ◽  
Bare Land ◽  
Land Covers

Urban land cover classification and mapping is an important and ongoing research field in monitoring and managing urban sprawl and terrestrial ecosystems. The changes in land cover largely affect the terrestrial ecosystem, thus information on land cover is important for understanding the ecological environment. Quantification of land cover in urban areas is challenging due to their diversified activities and large spatial and temporal variations. To improve urban land cover classification and mapping, this study presents three new spectral indices and an automated approach to classifying four major urban land types: impervious, bare land, vegetation, and water. A modified normalized difference bare-land index (MNDBI) is proposed to enhance the separation of impervious and bare land. A tasseled cap water and vegetation index (TCWVI) is proposed to enhance the detection of vegetation and water areas. A shadow index (ShDI) is proposed to further improve water detection by separating water from shadows. An approach for optimizing the thresholds of the new indices is also developed. Finally, the optimized thresholds are used to classify land covers using a decision tree algorithm. Using Landsat-8 Operational Land Imager (OLI) data from two study sites (Hong Kong and Dhaka City, Bangladesh) with different urban characteristics, the proposed approach is systematically evaluated. Spectral separability analysis of the new indices is performed and compared with other common indices. The urban land cover classifications achieved by the proposed approach are compared with those of the classic support vector machine (SVM) algorithm. The proposed approach achieves an overall classification accuracy of 94-96%, which is superior to the accuracy of the SVM algorithm.

scholarly journals DETERMINATION OF OPTIMUM CLASSIFICATION SYSTEM FOR HYPERSPECTRAL IMAGERY AND LIDAR DATA BASED ON BEES ALGORITHM

2015 ◽  
Vol XL-1-W5 ◽  
pp. 651-656
Author(s):  
F. Samadzadega ◽  
H. Hasani
Keyword(s):  
Urban Area ◽  
Hyperspectral Imagery ◽  
Feature Space ◽  
Classification Performance ◽  
Feature Subset Selection ◽  
Bees Algorithm ◽  
Support Vector ◽  
Svm Classifier ◽  
Lidar Data ◽  
Feature Subset

Hyperspectral imagery is a rich source of spectral information and plays very important role in discrimination of similar land-cover classes. In the past, several efforts have been investigated for improvement of hyperspectral imagery classification. Recently the interest in the joint use of LiDAR data and hyperspectral imagery has been remarkably increased. Because LiDAR can provide structural information of scene while hyperspectral imagery provide spectral and spatial information. The complementary information of LiDAR and hyperspectral data may greatly improve the classification performance especially in the complex urban area. In this paper feature level fusion of hyperspectral and LiDAR data is proposed where spectral and structural features are extract from both dataset, then hybrid feature space is generated by feature stacking. Support Vector Machine (SVM) classifier is applied on hybrid feature space to classify the urban area. In order to optimize the classification performance, two issues should be considered: SVM parameters values determination and feature subset selection. Bees Algorithm (BA) is powerful meta-heuristic optimization algorithm which is applied to determine the optimum SVM parameters and select the optimum feature subset simultaneously. The obtained results show the proposed method can improve the classification accuracy in addition to reducing significantly the dimension of feature space.

A Wearable Real-time Non-Contact Electrocardiogram System for Arrhythmia Detection and Classification (Preprint)

2020 ◽  
Author(s):  
Hongqiang Li ◽  
Sai Zhang ◽  
Shasha Zuo ◽  
Zhen Zhang ◽  
Binhua Wang ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Real Time ◽  
Predictive Value ◽  
Heart Diseases ◽  
Back Propagation ◽  
Feature Space ◽  
Primary Diagnosis ◽  
Support Vector ◽  
Svm Classifier ◽  
Heart Health

BACKGROUND Driven by the increasing demand for potential patients to monitor their own heart health, wearable technology is increasingly helping people to better monitor their heart health status at a medical level. OBJECTIVE The aim of this study was to develop a flexible and non-contact wearable electrocardiogram system, which can achieve real-time monitoring and primary diagnosis. METHODS A flexible electrocardiogram (ECG) acquisition device (wearable ECG) is designed based on flexible front-end circuit and textile capacitive electrodes, which are based on a conductive textile instead of rigid metal plates. The multi-domain feature space consists of time-domain features and frequency-domain statistical features, which can be used for classification via a back-propagation neural network (BPNN) and a support vector machine (SVM), both of which are optimized using a genetic algorithm. RESULTS The BPNN classifier exhibits good performance, with an accuracy of 98.33%, a sensitivity of 98.33%, a specificity of 99.63% and a positive predictive value of 97.85%. The SVM classifier achieves a higher classification accuracy of 98.89% and also performs better than the BPNN classifier in terms of the sensitivity, specificity and positive predictive value, achieving values of 98.89%, 99.81% and 98.89%, respectively. CONCLUSIONS The experimental results reveal that there is a better classification effect of SVM when classifying normal heart rhythms and 8 types of arrhythmia. The proposed wearable ECG monitoring can aid in the primary diagnosis of certain heart diseases.

Parameter Optimization of SVM Based on Maximum Variance – Entropy Criterion

2013 ◽  
Vol 373-375 ◽  
pp. 1053-1059
Author(s):  
Jian Liao ◽  
Shao Lei Zhou ◽  
Xian Jun Shi
Keyword(s):  
Parameter Optimization ◽  
Feature Space ◽  
Pso Algorithm ◽  
Parameter Selection ◽  
Support Vector ◽  
Svm Classifier ◽  
Maximum Variance ◽  
Kernel Parameter ◽  
Biquadratic Filter ◽  
Entropy Criterion

Kernel parameter selection of support vector machine (SVM) is difficult in practical application. A parameter selection algorithm of SVM was proposed based on data maximum variance - entropy criterion by analyzing the principle of SVM classifier. The algorithm uses data maximum variance - entropy criterion to measure the linear separability of dataset in the feature space, and combines with particle swarm optimization (PSO) algorithm for parameter optimization. The experiment results on datasets from UCI show that the algorithm is excellence in accuracy and improves the training performance of SVM. To further verify the effectiveness of the algorithm, applying the method in fault diagnosis of biquadratic filter circuit, results prove it improves the diagnostic accuracy.

scholarly journals From pixels to patches: a cloud classification method based on bag of micro-structures

2015 ◽  
Vol 8 (10) ◽  
pp. 10213-10247 ◽  
Author(s):  
Q. Li ◽  
Z. Zhang ◽  
W. Lu ◽  
J. Yang ◽  
Y. Ma ◽  
...  
Keyword(s):  
Image Representation ◽  
Feature Space ◽  
Classification Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Clear Sky ◽  
Cloud Classification ◽  
Image Patches ◽  
Sky Conditions ◽  
Micro Structures

Abstract. Automatic cloud classification has attracted more and more attention with the increasing development of whole sky imagers, but it is still in progress for ground-based cloud observation. This paper proposes a new cloud classification method, named bag of micro-structures (BoMS). This method treats an all-sky image as a collection of micro-structures mapped from image patches, rather than a collection of pixels. And then it constructs an image representation with a weighted histogram of micro-structures. Lastly, a support vector machine (SVM) classifier is applied on the image representation because SVM is appealing for sparse and high dimensional feature space. Five different sky conditions are identified: cirriform, cumuliform, stratiform, clear sky and mixed cloudiness that often appears in all-sky images but is seldom addressed in literature. BoMS is evaluated on a large dataset, which contains 5000 all-sky images that are captured by a total-sky cloud imager located in Tibet (29.25° N, 88.88° E). BoMS achieves an accuracy of 90.9 % for 10 fold cross-validation, and it outperforms the state-of-the-art method with an increase of about 19 %. Furthermore, influence of key parameters in BoMS are investigated to verify their robustness.

Cloud e-mail security: An accurate e-mail spam classification based on enhanced binary differential evolution (BDE) algorithm

2021 ◽  
pp. 1-13
Author(s):  
Nadir O. Hamed ◽  
Ahmed H. Samak ◽  
Mostafa A. Ahmad
Keyword(s):  
Differential Evolution ◽  
Correlation Coefficient ◽  
Fitness Function ◽  
Differential Evolution Algorithm ◽  
Feature Space ◽  
Support Vector ◽  
Svm Classifier ◽  
Binary Differential Evolution ◽  
E Mail ◽  
Email Spam

The evolution of technology has brought new challenges and opportunities for the different dimensions of feature space. The higher dimension of the feature space is one of the most critical issues in e-mail classification problems due to accuracy considerations. The problem of finding the subset features that significantly influence the performance of e-mail spam classification has become one of the important challenges. This paper proposes to overcome such a problem, an intelligent approach to Binary Differential Evolution Support Vector Machine (BDE-SVM). The proposed approach enhances the Binary Differential Evolution (BDE) algorithm based on the correlation coefficient as a fitness function to select the significant subset feature evaluated by an SVM classifier. To our best of knowledge, the correlation coefficient as the fitness function has not been used in the differential evolution algorithm before. The selected subset feature is used to assess the most features that contribute to the reliability of the email spam classification. The finding of the enhanced BDE is to present a powerful accuracy. The tests were conducted using “Spambase” and “SpamAssassin.” Identified benchmark datasets are to assess the feasibility of the proposed solution. The result with full-feature accuracy was 93.55 percent compared to the proposed BDE-SVM approach, which is 93.99 percent. Empirical findings also show that our method is capable of effectively increasing the number of features required to enhance the reliability of the email spam classification.

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