- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
Investigating the capabilities of multispectral remote sensors data to map alteration zones in the Abhar area, NW Iran
摘要： Economic mineralization is often associated with alterations that are identi?able by remote sensing coupled geological analysis. The present paper aims to investigate the capabilities of Advanced Spaceborne Thermal Emission and Re?ection Radiometer (ASTER), Landsat-8 and Sentinel-2 data to map iron oxide and hydrothermally alteration zones in the Abhar area, NW Iran. To achieve this goal, the principal component analysis (PCA) and two machine learning methods including support vector machine (SVM) and arti?cial neural network (ANN) were employed. PCA method was carried out on four bands of all data and then the appropriate principal components were selected to map alterations. Due to the high precision of ASTER data within the short-wave infrared range, these data results are more satisfactory compared with Landsat-8 and Sentinel-2 sensors in detecting hydrothermally alterations through the PCA technique. Based on the obtained maps, the performance of all data types was approximately similar in the detection of iron oxide zones. Our desired data were classi?ed by two methods of SVM and ANN. The results of these algorithms were presented as confusion matrix. According to these results, for hydrothermally alterations, ASTER data showed better performance in both SVM and ANN than other datasets by gaining values greater than 90%. These data did not perform well in the iron oxide zones detection, while Landsat-8 and Sentinel-2 have been more successful. For iron oxide, based on confusion matrix, Landsat-8 data have obtained the values of 78% and 79% through SVM and ANN algorithms, respectively, and also Sentinel-2 has acquired the values of 88.11% and 90.55% via SVM and ANN, respectively. Therefore, to map iron oxide zones, Sentinel-2 data are more favorable than Landsat-8 data. In addition, the ANN algorithm in ASTER data has represented the highest overall accuracy and Kappa coe?cient with the values of 88.73% and 0.8453, respectively.
关键词： Sentinel-2,SVM,Abhar,alteration zones,ANN,ASTER
更新于2025-09-23 15:19:57
-
Mapping Mangrove Forests of Dongzhaigang Nature Reserve in China Using Landsat 8 and Radarsat-2 Polarimetric SAR Data
摘要： Mangrove forests are distributed in intertidal regions that act as a “natural barrier” to the coast. They have enormous ecological, economic, and social value. However, the world’s mangrove forests are declining under immense pressure from anthropogenic and natural disturbances. Accurate information regarding mangrove forests is essential for their protection and restoration. The main objective of this study was to develop a method to improve the classification of mangrove forests using C-band quad-pol Synthetic Aperture Radar (SAR) data (Radarsat-2) and optical data (Landsat 8), and to analyze the spectral and backscattering signatures of mangrove forests. We used a support vector machine (SVM) classification method to classify the land use in Hainan Dongzhaigang National Nature Reserve (HDNNR). The results showed that the overall accuracy using only optical information was 83.5%. Classification accuracy was improved to a varying extent by the addition of different radar data. The highest overall accuracy was 95.0% based on a combination of SAR and optical data. The area of mangrove forest in the reserve was found to be 1981.7 ha, as determined from the group with the highest classification accuracy. Combining optical data with SAR data could improve the classification accuracy and be significant for mangrove forest conservation.
关键词： Landsat 8,mapping,Radasat-2,classification,SVM,mangrove forest
更新于2025-09-19 17:15:36
-
[IEEE 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) - Vancouver, BC, Canada (2018.11.1-2018.11.3)] 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) - Embedded System Design for Visual Scene Classification
摘要： Computer vision and robotics community is experiencing growing interest in visual scene classification due to availability of low cost and compact visual sensing devices. This paper presents framework aimed at embedded system design for visual scene classification. In the proposed framework we used data fusion of local and global descriptors as feature vectors for scene classification. We construct feature vector by integrating Local Quinary Patterns (LQP), Bag of Visual Words (BoW) and Histogram of Oriented Gradients (HOG). For classification multiclass Support Vector Machines (SVM) is used. Experiments are performed on publicly available MIT indoor scene classification database. Comparison of our approach with other methods show that our approach is efficient in terms of overall accuracy.
关键词： Local quinary patterns,scene classification,Bag of visual words,SVM,histogram of gradients
更新于2025-09-19 17:15:36
-
Deep Convolutional Neural Network for HEp-2 Fluorescence Intensity Classification
摘要： Indirect ImmunoFluorescence (IIF) assays are recommended as the gold standard method for detection of antinuclear antibodies (ANAs), which are of considerable importance in the diagnosis of autoimmune diseases. Fluorescence intensity analysis is very often complex, and depending on the capabilities of the operator, the association with incorrect classes is statistically easy. In this paper, we present a Convolutional Neural Network (CNN) system to classify positive/negative fluorescence intensity of HEp-2 IIF images, which is important for autoimmune diseases diagnosis. The method uses the best known pre-trained CNNs to extract features and a support vector machine (SVM) classifier for the final association to the positive or negative classes. This system has been developed and the classifier was trained on a database implemented by the AIDA (AutoImmunité, Diagnostic Assisté par ordinateur) project. The method proposed here has been tested on a public part of the same database, consisting of 2080 IIF images. The performance analysis showed an accuracy of fluorescent intensity around 93%. The results have been evaluated by comparing them with some of the most representative state-of-the-art works, demonstrating the quality of the system in the intensity classification of HEp-2 images.
关键词： autoimmune diseases,accuracy,SVM,receiver operating characteristic (ROC) curve,Convolutional Neural Network (CNN),IIF images
更新于2025-09-19 17:15:36
-
RAMS: Remote and automatic mammogram screening
摘要： About one in eight women in the U.S. will develop invasive breast cancer at some point in life. Breast cancer is the most common cancer found in women and if it is identified at an early stage by the use of mammograms, x-ray images of the breast, then the chances of successful treatment can be high. Typically, mammograms are screened by radiologists who determine whether a biopsy is necessary to ascertain the presence of cancer. Although historical screening methods have been effective, recent advances in computer vision and web technologies may be able to improve the accuracy, speed, cost, and accessibility of mammogram screenings. We propose a total screening solution comprised of three main components: a web service for uploading images and reviewing results, a machine learning algorithm for accepting or rejecting images as valid mammograms, and an artificial neural network for locating potential malignancies. Once an image is uploaded to our web service, an image acceptor determines whether or not the image is a mammogram. The image acceptor is primarily a one-class SVM built on features derived with a variational autoencoder. If an image is accepted as a mammogram, the malignancy identifier, a ResNet-101 Faster R-CNN, will locate tumors within the mammogram. On test data, the image acceptor had only 2 misclassifications out of 410 mammograms and 2 misclassifications out of 1,640 non-mammograms while the malignancy identifier achieved 0.951 AUROC when tested on BI-RADS 1, 5, and 6 images from the INbreast dataset.
关键词： Faster R-CNN,SVM,Deep Learning,DDSM,Convolutional,TensorFlow,INbreast,Mammograms,Telemedicine,Artificial Neural Network
更新于2025-09-19 17:15:36
-
Riparian trees genera identification based on leaf-on/leaf-off airborne laser scanner data and machine learning classifiers in northern France
摘要： Riparian forests are valuable environments delivering multiples ecological services. Because they face both natural and anthropogenic constraints, riparian forests need to be accurately mapped in terms of genera/species diversity. Previous studies have shown that the Airborne Laser Scanner (ALS) data have the potential to classify trees in di?erent contexts. However, an assessment of important features and classi?cation results for broadleaved deciduous riparian forests mapping using ALS remains to be achieved. The objective of this study was to estimate which features derived from ALS data were important for describing trees genera from a riparian deciduous forest, and provide results of classi?cations using two Machine Learning algorithms. The procedure was applied to 191 trees distributed in eight genera located along the Sélune river in Normandy, northern France. ALS data from two surveys, in the summer and winter, were used. From these data, trees crowns were extracted and global morphology and internal structure features were computed from the 3D points clouds. Five datasets were established, containing for each one an increasing number of genera. This was implemented in order to assess the level of discrimination between trees genera. The most discriminant features were selected using a stepwise Quadratic Discriminant Analysis (sQDA) and Random Forest, allowing the number of features to be reduced from 144 to 3–9, depending on the datasets. The sQDA-selected features highlighted the fact that, with an increasing number of genera in the datasets, internal structure became more discrimi- nant. The selected features were used as variables for classi?cation using Support Vector Machine (SVM) and Random Forest (RF) algorithms. Additionally, Random Forest classi?cations were conducted using all features computed, without selection. The best classi?ca- tion performances showed that using the sQDA-selected features with SVM produced accuracy ranging from 83.15% when using three genera (Oak, Alder and Poplar). A similar result was obtained using RF and all features available for classi?cation. The latter also achieved the best classi?cation performances when using seven and eight genera. The results highlight that ML algorithms are suitable methods to map riparian trees.
关键词： Machine Learning,Riparian forests,tree genera identification,Support Vector Machine (SVM),Airborne Laser Scanner (ALS),Random Forest (RF)
更新于2025-09-19 17:13:59
-
Separation Between Coal and Gangue Based on Infrared Radiation and Visual Extraction of the YCbCr Color Space
摘要： Distinguishing between coal and gangue in the production lines of mining factories based on the thermal energy and infrared radiation emission of an object is feasible. In this paper, we use an infrared camera (IC) to distinguish between coal and gangue in the industrial mining field. Additionally, this system is considered to be a binary classification system that has two classes. We analyze the infrared images of coal and gangue; then extract the appropriate texture features from the infrared images in order to develop an accurate classification system by using support vector machine (SVM). The method applied in this work essentially depends on feature extraction of images. The statistical features based on gray level information (GLI), grey-level cooccurrence matrix (GLCM) and visual features are executed. Thus, we suggest preparation steps to obtain one select feature before importing the data into the SVM classifier, and this approach is adopted as the fundamental basis for our work. We exploit only one feature of the infrared image, namely, Cb, which is extracted from the YCbCr color space, and then compute the mean value of Cb after heating and capturing the photos for the coal and gangue samples. The proposed method achieves a high classification accuracy 97.83 % by using Gaussian-SVM.
关键词： YCbCr,SVM,emissive power,gangue recognition,infrared camera application,Industrial mining
更新于2025-09-19 17:13:59
-
[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Hydrogenation of polycrystalline silicon films for passivating contacts solar cells
摘要： The strong temporal backscatter signature of rice growing above the water’s surface allows for the use of synthetic aperture radar (SAR) for paddy rice crop mapping in Southern Vietnam (Mekong Delta). In Northern Vietnam (Red River Delta), rice mapping using SAR is a challenge and is rarely performed because of the complex land-use/land-cover. Nevertheless, information about rice fields is needed for hydrological simulations in river basins such as the Cau River basin. The objective of this research is to investigate the potential of RADARSAT-2 band-C in identifying rice fields over a large and fragmented land-use area. Two methods are proposed, one for each data type, adapted to the land-use/land-cover of the study area. The thresholding technique, with a statistical analysis of the temporal variation of rice backscattering, was applied to the HH like-polarized ratio of dual-pol data. The support vector machine (SVM) algorithm was applied to the full quad-pol and a single HH-polarization calculated from polarimetric data. This study demonstrates that RADARSAT-2 dual- and quad-pol data can be successfully used to identify cultivated rice fields. However, the dual-pol data seems less efficient than the quad-pol data and the SVM classification is more flexible than the thresholding technique. Between the full quad-pol and a single polarization, the overall classification accuracy shows that the results derived from the single HH polarization are 3 to 10% less accurate than those derived from the classification of full quad-pol data. The results show the usefulness of polarimetric C-band data for the identification of rice fields in Northern Vietnam.
关键词： RADARSAT-2,support vector machine (SVM),thresholding,Cau river basin,rice identification
更新于2025-09-19 17:13:59
-
[IEEE 2019 IEEE 4th International Future Energy Electronics Conference (IFEEC) - Singapore, Singapore (2019.11.25-2019.11.28)] 2019 IEEE 4th International Future Energy Electronics Conference (IFEEC) - Fast Simulation Technique for Photovoltaic Power Systems using Simulink
摘要： P300 speller-based brain–computer interface is a direct communication from human-brain to computer machine without any muscular movements. In conventional P300 speller, a display paradigm is used to present alphanumeric characters to users and a classification system is used to detect the target character from the acquired electroencephalographic signals. In this paper, we present an 8 × 8 matrix consisting of Devanagari characters, digits, and special characters as Devanagari script (DS)-based display paradigm. The larger size of the display paradigm as compared with conventional 6 × 6 English row/column (RC) paradigm, involvement of matras and ardha-aksharas and similar looking characters in DS increase the adjacency problem, crowding effect, fatigue, and task difficulty. This results in deteriorated performance at the classification stage. Binary differential evolution algorithm was employed for optimal channel selection and support vector machine was used to classify target verses non target stimuli for the data set collected from ten healthy subjects using the DS-based paradigm. In order to further improve the system reliability in terms of higher accuracy at word prediction level, this paper proposes a novel spelling correction approach based on weighted edit distance (WED). A custom-built dictionary was incorporated and each misspelled word was replaced by a correct word of minimum WED from it. The proposed work is based on the validation of hypothesis that most of the target-error pairs lie in the same RC. Using the proposed spelling correction approach with optimal channel selection, an average accuracy of 99% was achieved at the word prediction level. The statistical analysis carried out in this paper shows that the proposed WED-based method improves the system reliability by significantly increasing in the accuracy of word prediction. This paper also validates that the proposed method performs better as compared to the conventional edit distance-based spelling correction approach.
关键词： SVM,brain-computer interface,EEG,P300 speller,edit distance,optimization,binary DE,channel selection,Devanagari,spelling correction
更新于2025-09-16 10:30:52
-
A New Plant Indicator ( <i>Artemisia lavandulaefolia</i> DC.) of Mercury in Soil Developed by Fourier-Transform Near-Infrared Spectroscopy Coupled with Least Squares Support Vector Machine
摘要： A rapid indicator of mercury in soil using a plant (Artemisia lavandulaefolia DC., ALDC) commonly distributed in mercury mining area was established by fusion of Fourier-transform near-infrared (FT-NIR) spectroscopy coupled with least squares support vector machine (LS-SVM). The representative samples of ALDC (stem and leaf ) were gathered from the surrounding and distant areas of the mercury mines. As a reference method, the total mercury contents in soil and ALDC samples were determined by a direct mercury analyzer incorporating high-temperature decomposition, catalytic adsorption for impurity removal, amalgamation capture, and atomic absorption spectrometry (AAS). Based on the FT-NIR data of ALDC samples, LS-SVM models were established to distinguish mercury-contaminated and ordinary soil. The results of reference analysis showed that the mercury level of the areas surrounding mercury mines (0–3 kilometers, 7.52–88.59 mg/kg) was significantly higher than that of the areas distant from mercury mines (>5 kilometers, 0–0.75 mg/kg). The LS-SVM classification model of ALDC samples was established based on the original spectra, smoothed spectra, second-derivative (D2) spectra, and standard normal transformation (SNV) spectra, respectively. The prediction accuracy of D2-LS-SVM was the highest (0.950). FT-NIR combined with LS-SVM modeling can quickly and accurately identify the contaminated ALDC. Compared with traditional methods which rely on naked eye observation of plants, this method is objective and more sensitive and applicable.
关键词： soil,LS-SVM,FT-NIR spectroscopy,Artemisia lavandulaefolia DC.,mercury
更新于2025-09-16 10:30:52