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中国地质大学科学技术发展院陈伟涛 等，计算机学院. Remote Sensing（2019）, Fine Land Cover Classification in an Open Pit Mining Area Using Optimized Support Vector Machine and WorldView-3 Imagery
地质环境；遥感；智能；解译；研究领域
2021/10/15
近日，中国地质大学计算机学院陈伟涛副教授团队再次在地质环境遥感智能解译研究领域取得重要进展。相关研究成果发表在国际著名期刊《Remote Sensing》（2018年影响因子为4.118）。论文第一作者为陈伟涛副教授，通讯作者为李显巨副教授。
Comparison of partial least squares-discriminant analysis, support vector machines and deep neural networks for spectrometric classification of seed vigour in a broad range of tree species
least squares-discriminant analysis spectrometric classification seed tree species
2023/6/5
Seed vigour significantly influences the seed production and plant regeneration performance. The capability of NIR spectroscopy to identify seed vigour across multiple tree species rapidly and cost-ef...
IMAGE CLASSIFICATION FOR MAPPING OIL PALM DISTRIBUTION VIA SUPPORT VECTOR MACHINE USING SCIKIT-LEARN MODULE
Landsat oil palm Python remote sensing Scikit-learn support vector machine
2018/11/9
The world has been alarmed with the global warming effects. Global warming has been a distress towards the environment, thus shorten the Earth’s lifespan. It is a challenging task to reduce the global...
WATERBODIES EXTRACTION FROM LANDSAT8-OLI IMAGERY USING AWATER INDEXS-GUIED STOCHASTIC FULLY-CONNECTED CONDITIONAL RANDOM FIELD MODEL AND THE SUPPORT VECTOR MACHINE
Water index CRF Landsa Water extraction SVM
2018/5/15
One of the most important applications of remote sensing classification is water extraction. The water index (WI) based on Landsat images is one of the most common ways to distinguish water bodies fro...
THE LOW BACKSCATTERING OBJECTS CLASSIFICATION IN POLSAR IMAGE BASED ON BAG OF WORDS MODEL USING SUPPORT VECTOR MACHINE
Low Backscattering Objects Bag of Words SIFT features Support Vector Machine
2018/5/16
Due to the forward scattering and block of radar signal, the water, bare soil, shadow, named low backscattering objects (LBOs), often present low backscattering intensity in polarimetric synthetic ape...
Image Classification using non-linear Support Vector Machines on Encrypted Data
cryptographic protocols SHE
2017/9/13
In image processing, algorithms for object classification are typically based around machine learning. From the algorithm developer's perspective, these can involve a considerable amount of effort and...
LANDSLIDES IDENTIFICATION USING AIRBORNE LASER SCANNING DATA DERIVED TOPOGRAPHIC TERRAIN ATTRIBUTES AND SUPPORT VECTOR MACHINE CLASSIFICATION
Airborne laser scanning support vector machine landslide mapping Pricncipal Component Analysis
2016/11/30
Since the availability of high-resolution Airborne Laser Scanning (ALS) data, substantial progress in geomorphological research, especially in landslide analysis, has been carried out. First and secon...
CLOUD DETECTION OF OPTICAL SATELLITE IMAGES USING SUPPORT VECTOR MACHINE
Cloud Detection Classification Support Vector Machine
2016/11/23
Cloud covers are generally present in optical remote-sensing images, which limit the usage of acquired images and increase the difficulty of data analysis, such as image compositing, correction of atm...
BALANCED VS IMBALANCED TRAINING DATA:CLASSIFYING RAPIDEYE DATA WITH SUPPORT VECTOR MACHINES
Land cover classification Imbalanced training data Support Vector Machines RapidEye Agriculture
2016/11/23
The accuracy of supervised image classification is highly dependent upon several factors such as the design of training set (sample selection, composition, purity and size), resolution of input imager...
THE APPLICATION OF SUPPORT VECTOR MACHINE (SVM) USING CIELAB COLOR MODEL, COLOR INTENSITY AND COLOR CONSTANCY AS FEATURES FOR ORTHO IMAGE CLASSIFICATION OF BENTHIC HABITATS IN HINATUAN,SURIGAO DEL SUR,PHILIPPINES
Benthic habitat mapping Image processing CIELAB Color science OBIA SVM
2016/11/23
This study demonstrates the application of CIELAB, Color intensity, and One Dimensional Scalar Constancy as features for image recognition and classifying benthic habitats in an image with the coastal...
Efficient and Private Scoring of Decision Trees, Support Vector Machines and Logistic Regression Models based on Pre-Computation
privacy-preserving private data
2016/7/29
Many data-driven personalized services require that private data of users is scored against a trained machine learning model. In this paper we propose a novel protocol for privacy-preserving classific...
LAND COVER CLASSIFICATION FROM FULL-WAVEFORM LIDAR DATA BASED ON SUPPORT VECTOR MACHINES
LiDAR Support Vector Machines (SVM) Full-Waveform Land Cover Classification Waveform decomposition Feature extraction
2016/7/27
In this study, a land cover classification method based on multi-class Support Vector Machines (SVM) is presented to predict the types of land cover in Miyun area. The obtained backscattered full-wave...
ANALYSIS OF THE TRANSFERABILITY OF SUPPORT VECTOR MACHINES FOR VEGETATION CLASSIFICATION
Remote Sensing Mapping Classification Accuracy High resolution Ikonos
2015/12/31
This paper reports on research analysing the potential of Support Vector Machines (SVMs) for mapping vegetation from high spatial resolution Ikonos imagery. The work investigated the utility of SVMs f...
A GENETIC ALGORITHM BASED WRAPPER FEATURE SELECTION METHOD FOR CLASSIFICATION OF HYPERSPECTRAL IMAGES USING SUPPORT VECTOR MACHINE
Feature Selection Hyperspectral Genetic Algorithm Supported Vector Machine
2015/12/31
The high-dimensional feature vectors of hyper spectral data often impose a high computational cost as well as the risk of "over fitting" when classification is performed. Therefore it is necessary to ...
MODEL-BASED ESTIMATION OF IMPERVIOUS SURFACE BY APPLICATION OF SUPPORT VECTOR MACHINES
Urban Automation Modeling Impervious Surface Spatial Planning
2015/12/26
Due to its various negative consequences impervious surface is increasingly recognized as a key issue in assessing the sustainability of land use - particularly in urban environments. In Germany, a re...