- 标题
- 摘要
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- 实验方案
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Distinguishing between closely related species of Allium and of Brassicaceae by narrowband hyperspectral imagery
摘要： Classification of crop species is an actively studied topic in remote sensing using multi-spectral image sensors. Unfortunately, the spectral bands available in the multispectral imagery are broad and limited in number to classify the crop species. In this paper, we propose optimal spectral bands to classify Allium (garlic and onion) and Brassicaceae (Chinese cabbage and radish) by using higher-dimensional data from hyperspectral imagery. A decision-tree classifier was used to determine the optimal method to use the high-dimensional data. The high-dimensional data were analysed for all growth stages and considering bandwidths with different full width at half maximum (FWHM) values at 25, 40, 50 and 80 nm. The spectral bands selected for Allium were differentiated into green, blue, and NIR bands for each growth stage. The results show that Allium can be classified clearly as overall accuracy (OA) 1 and kappa coefficient 1 for all FWHM based on March 22 data. For each April 19 and May 12 data, the decision-tree classifier with each 80 nm FWHM and 50 nm FWHM yielded a better classification accuracy of more than OA 0.921 and kappa coefficient 0.839 than other FWHM. The spectral bands selected for Brassicaceae were found to be similar to blue band for all growth stages. Brassicaceae was classified clearly for all FWHM based on October 27 data. Also, Brassicaceae was classified clearly for 25 nm FWHM based on November 25 data and OA, kappa coefficient for 40 nm FWHM and 50 nm FWHM are high as 0.974, 0.947 respectively.
关键词： Decision-tree classifier,Hyperspectral imagery,Classification,Full width at half maximum,Spectral band
更新于2025-09-23 15:23:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - An Tensor-Based Corn Mapping Scheme with Radarsat-2 Fully Polarimetric Images
摘要： As one of the most essential economic and industrial crops globally, corn holds a very important position in China’s agricultural industry. Corn mapping is one of the most concerned fields in agricultural surveillance. However, compared with the utilization of backscattering coefficients, the polarimetric information was not fully discussed in previous corn mapping researches. In this paper, we use the coherency matrix of mid to late term multi-temporal fully polarimetric synthetic aperture radar (FP SAR) data to discriminate corn cultivation areas. The tensor representation is adopted for PolSAR analysis, with the help of multilinear principal component analysis (MPCA) to reduce feature dimensions. The importance of polarimetric information is discussed. This paper illustrates that good corn discrimination could be achieved with only mid to late term FP SAR data.
关键词： corn mapping,synthetic aperture radar (SAR),decision tree,multi-temporal SAR,multilinear principal component analysis (MPCA),polarimetric SAR (PolSAR)
更新于2025-09-23 15:22:29
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An Ensemble Learner-Based Bagging Model Using Past Output Data for Photovoltaic Forecasting
摘要： As the world is aware, the trend of generating energy sources has been changing from conventional fossil fuels to sustainable energy. In order to reduce greenhouse gas emissions, the ratio of renewable energy sources should be increased, and solar and wind power, typically, are driving this energy change. However, renewable energy sources highly depend on weather conditions and have intermittent generation characteristics, thus embedding uncertainty and variability. As a result, it can cause variability and uncertainty in the power system, and accurate prediction of renewable energy output is essential to address this. To solve this issue, much research has studied prediction models, and machine learning is one of the typical methods. In this paper, we used a bagging model to predict solar energy output. Bagging generally uses a decision tree as a base learner. However, to improve forecasting accuracy, we proposed a bagging model using an ensemble model as a base learner and adding past output data as new features. We set base learners as ensemble models, such as random forest, XGBoost, and LightGBMs. Also, we used past output data as new features. Results showed that the ensemble learner-based bagging model using past data features performed more accurately than the bagging model using a single model learner with default features.
关键词： ensemble,decision tree,bagging,Light GBM,lagged data,machine learning,random forest,XGBoost,photovoltaic power forecasting
更新于2025-09-23 15:21:01
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COMPUTER-AIDED DECISION SYSTEM FOR REFRACTIVE SURGERIES WITH EXCIMER LASER
摘要： 124 patients (248 eyes) who intended to refractive surgery by Excimer laser were studied to implement our goal of this study which is design and operate a computer-aided decision system for optimal choosing the best refractive surgery based on patient needs, Starting from corneal topography and aberration images, using RGB and HSI color spaces and decision tree. The system also can calculate percent of vision correction, ablation and residual stroma with high precision. This highly important transdisciplinary topic combines aspects from biosystems (human visual system), image acquisition and processing and information management (databases).
关键词： Image Processing,Computer-Aided Decision System,Refractive Surgery,Decision Tree
更新于2025-09-23 15:19:57
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Subwavelength polarization optics via individual and coupled helical traveling-wave nanoantennas
摘要： Soil spectral allocation or classification is usually conducted on air-dried soils. However, the field soils are not all air-dried, and the change of soil moisture will affect soil reflectance. We introduce a soil allocation model that considers the effect of soil moisture for the purpose of eliminating the effect of soil moisture. The topsoil spectral curves of four typical soils from the Songnen Plain in Northeast China were re-sampled to 10-nm intervals and converted to first-derivative spectral curves and continuum removal curves. The spectral feature parameters were extracted from continuum removal curves in the visible-near infrared (VNIR) range (350–2500 nm), and the range of 430–2400 nm was used to build soil allocation models for reducing the effect of noise. Samples with different soil moisture were mixed into air-dried soils and we calculated the coefficient of variation (CV) of different inputs to assess the effect of soil moisture and to find allocation indices that were not affected by soil moisture. We used allocation indices of Zhang et al. (2018) because of the high accuracy of their DT (Decision Tree) model to allocate mixed-soil samples. We also used allocation indices that were not affected by soil moisture to allocate mixed-soil samples with decision tree (DT), multinomial logistic regression (MLR) and multi-layer perception neural network (MLPNN), and compared the results of the two methods. The results show the following: 1) As SFPs were built with shorter bands, SFP was less sensitive to soil moisture than PCR and PCFD and thus SFP is more suitable to build soil allocation models that consider the effect of soil moisture as input than PCR and PCFD. 2) Differences in soil moisture had little effect on absorption valley shoulders, symmetry and absorption positions, moderate effect on absorption area and depth, and a major effect on the slope of different bands. 3) The effect of soil moisture on continuum removal curves of different soil classes was variable. There was little effect on Arenosols, a moderate effect on Chernozems and Cambisols, and a large effect on Phaeozems. 4) The accuracy of the DT model using allocation indices that were not affected by soil moisture was 91.892% with a Kappa coefficient of 0.888. Our results suggest that it is feasible to build soil spectral allocation models that are not affected by soil moisture, and this improves the universality of soil spectral allocation, especially to field soils, which can be of considerable help in soil classification.
关键词： Decision tree,Visible-near infrared,Soil moisture,Spectral feature parameter,Soil spectral allocation
更新于2025-09-11 14:15:04
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Detection of Corn and Weed Species by the Combination of Spectral, Shape and Textural Features
摘要： Accurate detection of weeds in farmland can help reduce pesticide use and protect the agricultural environment. To develop intelligent equipment for weed detection, this study used an imaging spectrometer system, which supports micro-scale plant feature analysis by acquiring high-resolution hyper spectral images of corn and a number of weed species in the laboratory. For the analysis, the object-oriented classification system with segmentation and decision tree algorithms was utilized on the hyper spectral images to extract shape and texture features of eight species of plant leaves, and then, the spectral identification characteristics of different species were determined through sensitive waveband selection and using vegetation indices calculated from the sensitive band data of the images. On the basis of the comparison and analysis of the combined characteristics of spectra, shape, and texture, it was determined that the spectral characteristics of the ratio vegetation index of R677/R710 and the normalized difference vegetation index, shape features of shape index, area, and length, as well as the texture feature of the entropy index could be used to build a discrimination model for corn and weed species. Results of the model evaluation showed that the Global Accuracy and the Kappa coefficient of the model were both over 95%. In addition, spectral and shape features can be regarded as the preferred characteristics to develop a device of weed identification from the view of accessibility to crop/weeds discriminant features, according to different roles of various features in classifying plants. Therefore, the results of this study provide valuable information for the portable device development of intelligent weed detection.
关键词： hyper spectral imaging,object-oriented,weed,decision tree,corn
更新于2025-09-10 09:29:36
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[Advances in Intelligent Systems and Computing] Emerging Trends in Expert Applications and Security Volume 841 (Proceedings of ICETEAS 2018) || Exploring Open Source for Machine Learning Problem on Diabetic Retinopathy
摘要： Open-source operating system, as well as its packages, is more powerful and secure than the proprietary sources. In the proprietary source, software source code is not easily available because it is secret; by contrast in the open-source operating system source code is easily available, so any programmer can change the code and implement their ideas and modify it because of its openness. Also, one major advantage is that we do not need to spend a huge amount of money on the software. So, in this paper, we used open-source software for coding purposes and looked at the data available on the UCI machine learning repository on the diabetic retinopathy.
关键词： Neural network,Open-source,Boosted decision tree,Diabetic retinopathy
更新于2025-09-10 09:29:36
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A Novel Industrial Safety IoTs Architecture for External Corrosion Perception Based on Infrared
摘要： In this paper, a novel external corrosion risk online perception method is proposed to solve the dangerous external corrosion threat and supply a measurable safe risk perception ability for the industrial safe Internet of Things (IoTs) with the infrared thermal wave as the direct sensors. The three layers model is established with direct variables measuring layer, external corrosion risk soft measuring layer and monitoring cycle decision-making layer. And in the direct variable measuring layer the infrared thermal wave is applied to measure the three direct variables, area ratio of cladding defects,cladding layer thickness reading and overlapping between external and internal corrosion defects, in the direct variables measuring layer. In the external corrosion risk soft measuring layer and monitoring cycle decision-making layer, external corrosion risk can be soft measured through a cladding-condition-based risk matrix and the most optimal monitoring cycle can also be determined through a decision-making tree based on the three direct variables.
关键词： Internet of things,Industrial safety,Maintenance decision tree
更新于2025-09-04 15:30:14