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- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Innovative Multi Pcnn Based Network for Green Area Monitoring - Identification and Description of Nearly Indistinguishable Areas - In Hyperspectral Satellite Images
摘要： The paper presents an original neural network approach for region of interest detection and classification in multi-spectral satellite images. The proposed method uses a sequence of Pulse Coupled Neural Networks that identifies plausible regions of interest. These regions are passed to a dimension reduction algorithm, Principle Component Analysis, in order to generate the input data for a Support Vector Machine classifier, that validates the data. The algorithm's parameters are optimized using a Genetic Algorithm. The algorithm is designed to distinguish regions that are extremely similar, such as parks in a city that has entire districts made up of houses with yards. The algorithm has been tested on images provided by the Sentinel-2 satellite, and it proved that it can recall 76.85% of the pixels marked as park in the ground truth data, which was obtained from OpenStreetMap.
关键词： Genetic Algorithm (GA),Pulse Coupled Neural Network (PCNN),Principle Component Analysis (PCA),Support Vector Machine (SVM)
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Benthic Mapping Using High Resolution Multispectral and Hyperspectral Imagery
摘要： Coastal ecosystems are essential due to their high biodiversity and primary production, however they are extremely complex and with high spatial and temporal variability. Thus, to properly manage them it is necessary a systematic monitoring. Remote sensing can be very useful due to the spatial and spectral improvement of satellites and the availability of airborne or drone hyperspectral sensors. Unfortunately, the mapping of coastal areas is challenging due to the low SNR received at the sensor, as a consequence of the minimum reflectivity of the seafloor and the atmospheric and water column disturbances. In this context, the goal of this work is to obtain a robust classification methodology to generate accurate benthic habitat maps applying object-oriented and pixel-based classification methods in shallow waters using WorldView-2 and AHS (Airborne Hyperspectral Scanner) images. Maspalomas (Gran Canaria, Spain) was studied due to its complexity and the presence of important seagrass meadows.
关键词： Benthic mapping,SVM,classification,hyperspectral,seagrass
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Classification of Hyperspectral Remote Sensing Images by an Ensemble of Support Vector Machines Under Imbalanced Data
摘要： It is found very often that training data contains unequal number of representative samples for classes. Some of the classes might be represented by a larger number of samples while the rest with lower number of samples. Classification of remote sensing images with imbalanced class distribution could result in a significant drawback in the classification performance attainable by most standard classifier learning algorithms which assume a relatively balanced class distribution and equal misclassification costs. So it is worth exploring if ensemble method could give an improved performance under the condition of imbalanced training data. In the proposed work, Support Vector Machine (SVM) is used as base classifiers in the ensemble committee. An ensemble of SVMs will be constructed using popular Bagging method. Standard Hyperspectral data such as Salinas is used as test data. The proposed work will explore the efficiency of ensemble technique in improving classification accuracy, even in cases of robust classifier such as SVM.
关键词： SVM,Bagging,Classification,Ensemble method,imbalanced data
更新于2025-09-10 09:29:36
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[IEEE 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Honolulu, HI (2018.7.18-2018.7.21)] 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - An mRMR-SVM Approach for Opto-Fluidic Microorganism Classification
摘要： The detection of microorganisms is important in numerous applications such as water quality monitoring, blood analysis, and food testing. The conventional detection methods are tedious and labour-intensive. Establish methods involve culturing, counting and identification of the pathogen by an experienced technician which typically can take several days. The use of opto-fluidic technology to capture microorganism images offers o route to reduce the overall assay time. However, the detection still requires a trained technician. This paper proposes an image processing method that can be used to classify microorganism images captured by an opto-fluidic set up in an automatic manner. The proposed algorithm incorporates some of the features used in other microorganism image detection methods and proposes two new features - Entropy of Histogram of Oriented Gradients (HOG) and the filtered intensities. In addition, we propose to apply the minimal-Redundancy-Maximal-Relevance (mRMR) criterion to select and rank these features. The probability and joint probability distribution functions of the mRMR are estimated using a Gaussian model and the Kernel Density Estimation model. The performance of the proposed method was validated using SVM and data collected from an experimental setup. The results show that our proposed method outperforms existing methods and is capable of achieving a classification accuracy up to 95.8%.
关键词： mRMR,SVM,image processing,opto-fluidic technology,microorganism detection
更新于2025-09-10 09:29:36
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Comparison of pixel-based and object-based image classification techniques in extracting information from UAV imagery data
摘要： As the rapid development is being focused in the urban area, there is a need for the utilisation of a rapid system for updating this profile immediately. One of the current technologies being applied in recent years is the use of unmanned aerial vehicle (UAV) for mapping purposes. The use of UAV is widespread in various fields because it is low cost, has high resolution and is able to fly at low altitude without the constraints of cloudy weather. Typically, the method of data extraction for UAV in Malaysia is still very limited and the traditional methods are still being implemented by some industries. The features from aerial photo orthomosaic are manually detected and digitised from visual interpretation for the mapping purposes. Unfortunately, these methods are tedious, expensive, consume much time, and may involve much fieldwork, to acquire only a limited information. Pixel-based technique is often used to extract low level features where the image is classified according to the spectral information where the pixels in the overlapping region will be misclassified due to the confusion among image analysis (OBIA) classification technique is widely used nowadays for automatic data extraction. Therefore, the general objective of this study is to assess the capability of UAV with high resolution data for image classifications. The pixel-based and OBIA classifications were compared using the Support Vector Machine (SVM) classifier. The classifications were assessed using different numbers of sample size. The result shows that OBIA gives a better result of Overall Accuracy (OA) than pixel-based. The consequences of this study accommodate further understanding and additional insight of utilising OBIA technique with different classifiers for the extended study.
关键词： object-based,UAV,SVM,pixel-based,image classification
更新于2025-09-10 09:29:36
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[IEEE 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Chongqing (2018.6.27-2018.6.29)] 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Improved Local Texture Features for Pedestrian Detection
摘要： Pedestrian detection is a hot issue in the field of computer vision and image processing in recent years. It has important application value in the domain of unmanned cars and driver assistance systems and so on, but there are existed many problems that need to be solved. In this paper, we present an improved texture feature MLBP (Mean of Local Binary Pattern) and the CMLBP (Color based on Mean of Local Binary Pattern) feature based on various color spaces. When the uniform LBP feature does not consider the influence of noise, the mutation of central pixel and neighborhood pixel is not taken into account and therefore the extraction processes of MLBP feature improve the calculation method of the uniform LBP, which makes the extracted feature more stable. The MLBP feature is extracted from gray images, yet color images transformed into gray images generally loss a great amount of information. In view of this point, we also propose the CMLBP feature based on multiple color spaces that is a more comprehensive description of the texture feature of images. In the INRIA pedestrian dataset, many experiments have been conducted with SVM and HIKSVM classifier, and the results manifest that the detection rates of MLBP and CMLBP are much better than the uniform LBP and the basic LBP. The combination of MLBP, CMLBP and other features has been applied to pedestrian detection, which also achieves good results.
关键词： SVM,pedestrian detection,MLBP,HIKSVM,CMLBP
更新于2025-09-10 09:29:36
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Instantaneous brain stroke classification and localization from real scattering data
摘要： This work presents a 2-step Learning-by-Examples approach for the real-time classification of hemorrhagic/ischemic brain strokes and their successive localization from microwave scattering data collected around the human head. An experimental assessment against laboratory-controlled data is performed to assess the potentialities of the proposed approach towards a reliable monitoring and instantaneous diagnosis clinic protocol.
关键词： inverse scattering,experimental data,support vector machine (SVM),brain stroke microwave imaging,learning-by-examples (LBE)
更新于2025-09-10 09:29:36
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Multifeature Extraction and Seafloor Classification Combining LiDAR and MBES Data around Yuanzhi Island in the South China Sea
摘要： Airborne light detection and ranging (LiDAR) full waveforms and multibeam echo sounding (MBES) backscatter data contain rich information about sea?oor features and are important data sources representing sea?oor topography and geomorphology. Currently, to classify sea?oor types using MBES, curve features are extracted from backscatter angle responses or grayscale, and texture features are extracted from backscatter images based on gray level co-occurrence matrix (GLCM). To classify sea?oor types using LiDAR, waveform features are extracted from bottom returns. This paper comprehensively considers the features of both LiDAR waveforms and MBES backscatter images that include the eight feature factors of the LiDAR full waveforms (amplitude, peak location, full width half maximum (FWHM), skewness, kurtosis, area, distance, and cross-section) and the eight feature factors of MBES backscatter images (mean, standard deviation (STD), entropy, homogeneity, contrast, angular second moment (ASM), correlation, and dissimilarity). Based on a support vector machine (SVM) algorithm with different kernel functions and penalty factors, a new sea?oor classi?cation method that merges multiple features is proposed for a bene?cial exploration of acousto-optic fusion. The experimental results of the sea?oor classi?cation around Yuanzhi Island in the South China Sea indicate that, when LiDAR waveform features are merged (using an Optech Aquarius system) with MBES backscatter image features (using a Sonic 2024) to classify three types of sands, reefs, and rocks, the overall accuracy is improved to 96.71%, and the kappa reaches 0.94. After merging multiple features, the classi?cation accuracies of the SVM, genetic algorithm SVM (GA-SVM) and particle swarm optimization SVM (PSO-SVM) increase by an average of 9.06%, 3.60%, and 2.75%, respectively.
关键词： MBES,LiDAR,SVM,multifeature,sea?oor classi?cation
更新于2025-09-10 09:29:36
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[IEEE 2017 14th IEEE India Council International Conference (INDICON) - Roorkee (2017.12.15-2017.12.17)] 2017 14th IEEE India Council International Conference (INDICON) - An Effective Solar PV Fed Modified Vector Control of IMD for Water Pumping
摘要： The present paper deals with a speed sensorless technique of vector controlled induction motor drive fed from solar PV (Photovoltaic) array used for water pumping. This sensorless technique is based stator flux oriented in stationary reference frame. The special feature of the proposed topology is the additional feedforward loop added to the existing vector control loop, which makes it decoupled as a key feature of vector control. The mechanical sensorless induction motor drive is powered by solar photovoltaic (SPV) array to be utilized to drive the submersible water pump. The simulation of proposed topology is performed in MATLAB/Simulink environment and its performance is validated on test setup developed in the laboratory.
关键词： induction motor drive (IMD),Solar photovoltaic (SPV) array,DC-DC converter,decoupled control,SVM technique,water pumps,three-phase inverter
更新于2025-09-10 09:29:36
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Detection of <i>Firmiana danxiaensis</i> Canopies by a Customized Imaging System Mounted on an UAV Platform
摘要： The objective of this study was to test the effectiveness of mapping the canopies of Firmiana danxiaensis (FD), a rare and endangered plant species in China, from remotely sensed images acquired by a customized imaging system mounted on an unmanned aerial vehicle (UAV). The work was conducted in an experiment site (approximately 10 km2) at the foot of Danxia Mountain in Guangdong Province, China. The study was conducted as an experimental task for a to-be-launched large-scale FD surveying on Danxia Mountain (about 200 km2 in area) by remote sensing on UAV platforms. First, field-based spectra were collected through hand-held hyperspectral spectroradiometer and then analyzed to help design a classification schema which was capable of differentiating the targeted plant species in the study site. Second, remote-sensed images for the experiment site were acquired and calibrated through a variety of preprocessing steps. Orthoimages and a digital surface model (DSM) were generated as input data from the calibrated UAV images. The spectra and geometry features were used to segment the preprocessed UAV imagery into homogeneous patches. Lastly, a hierarchical classification, combined with a support vector machine (SVM), was proposed to identify FD canopies from the segmented patches. The effectiveness of the classification was evaluated by on-site GPS recordings. The result illustrated that the proposed hierarchical classification schema with a SVM classifier on the remote sensing imagery collected by the imaging system on UAV provided a promising method for mapping of the spatial distribution of the FD canopies, which serves as a replacement for field surveys in the attempt to realize a wide-scale plant survey by the local governments.
关键词： UAV,SVM,hierarchical classification,Firmiana danxiaensis,spectral analysis,remote sensing,image segmentation,vegetation indices
更新于2025-09-10 09:29:36