- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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HCKBoost: Hybridized composite kernel boosting with extreme learning machines for hyperspectral image classification
摘要： Utilization of contextual information on the hyperspectral image (HSI) analysis is an important fact. On the other hand, multiple kernels (MKs) and hybrid kernels (HKs) in connection with kernel methods have significant impact on the classification process. Activation of spatial information via composite kernels (CKs) and exploiting hidden features of the spectral information via MKs and HKs have been shown great successes on hyperspectral images separately. In this work, it is aimed to aggregate composite and hybrid kernels to obtain high classification success with a boosting based community learner. Spatial and spectral hybrid kernels are constructed using weighted convex combination approach with respect to individual success of the predefined kernels. Composite kernel formation is realized with certain proportions of the obtained spatial and spectral HKs. Computationally fast and effective extreme learning machine (ELM) classification algorithm is adopted. Since, main objective is to obtain optimal kernel during ensemble formation operation, unlike the standard MKL methods, proposed method disposes off the complex optimization processes and allows multi-class classification. Pavia University, Indian Pines, and Salinas hyperspectral scenes that have ground truth information are used for simulations. Hybridized composite kernels (HCK) are constructed using Gaussian, polynomial, and logarithmic kernel functions with various parameters and then obtained results are presented comparatively along with the state-of-the-art MKL, CK, sparse representation, and single kernel based methods.
关键词： Hyperspectral images,Composite kernels,Adaptive boosting,Extreme learning machines,Hybrid kernels
更新于2025-09-23 15:22:29
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Deriving probabilistic SVM kernels from flexible statistical mixture models and its application to retinal images classification
摘要： This paper aims to propose a robust hybrid probabilistic learning approach that combines appropriately the advantages of both the generative and discriminative models for the challenging problem of diabetic retinopathy classification in retinal images. We build new probabilistic kernels based on information divergences and Fisher score from the mixture of scaled Dirichlet distributions for support vector machines (SVMs). We also investigate the incorporation of a minimum description length criterion into the learning model to deal with the common problems of determining suitable components and also selecting the best model that describes the dataset. The developed hybrid model is introduced in this paper as an effective SVM kernel able to incorporate prior knowledge about the nature of data involved in the problem at hand and, therefore, permits a good data discrimination. Our approach has been shown to be a better alternative to other methods, which is able to describe the intrinsic nature of datasets and to be of a significant value in a variety of applications involving data classification. We demonstrate the flexibility and the merits of the proposed framework for the problem of diabetic retinopathy detection in eye images.
关键词： Retinal images,SVM,probabilistic kernels,MDL,scaled Dirichlet mixture,generative-discriminative learning
更新于2025-09-23 15:22:29
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MCK-ELM: multiple composite kernel extreme learning machine for hyperspectral images
摘要： Multiple kernel (MK) learning (MKL) methods have a significant impact on improving the classification performance. Besides that, composite kernel (CK) methods have high capability on the analysis of hyperspectral images due to making use of the contextual information. In this work, it is aimed to aggregate both CKs and MKs autonomously without the need of kernel coefficient adjustment manually. Convex combination of predefined kernel functions is implemented by using multiple kernel extreme learning machine. Thus, complex optimization processes of standard MKL are disposed of and the facility of multi-class classification is profited. Different types of kernel functions are placed into MKs in order to realize hybrid kernel scenario. The proposed methodology is performed over Pavia University, Indian Pines, and Salinas hyperspectral scenes that have ground-truth information. Multiple composite kernels are constructed using Gaussian, polynomial, and logarithmic kernel functions with various parameters, and then the obtained results are presented comparatively along with the state-of-the-art standard machine learning, MKL, and CK methods.
关键词： Multiple kernel learning,Composite kernels,Hybrid kernels,Extreme learning machines,Hyperspectral images
更新于2025-09-19 17:15:36
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A Novel Fault Classification Approach for Photovoltaic Systems
摘要： Photovoltaic (PV) energy has become one of the main sources of renewable energy and is currently the fastest-growing energy technology. As PV energy continues to grow in importance, the investigation of the faults and degradation of PV systems is crucial for better stability and performance of electrical systems. In this work, a fault classification algorithm is proposed to achieve accurate and early failure detection in PV systems. The analysis is carried out considering the feature extraction capabilities of the wavelet transform and classification attributes of radial basis function networks (RBFNs). In order to improve the performance of the proposed classifier, the dynamic fusion of kernels is performed. The performance of the proposed technique is tested on a 1 kW single-phase stand-alone PV system, which depicted a 100% training efficiency under 13 s and 97% testing efficiency under 0.2 s, which is better than the techniques in the literature. The obtained results indicate that the developed method can effectively detect faults with low misclassification.
关键词： feature extraction,radial basis function networks (RBFN),fault classification,photovoltaic system,wavelet analysis,kernels
更新于2025-09-19 17:13:59
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Detection of moisture content in peanut kernels using hyperspectral imaging technology coupled with chemometrics
摘要： Hyperspectral imaging technology at 416–1000 nm was investigated to detect moisture content in peanut kernels. Four varieties of peanuts were scanned using a “push-broom” system to acquire hyperspectral images. In this study, three models including partial least squares regression (PLSR), principal component regression (PCR), and support vector machine regression (SVR) were established to detect moisture content in peanut kernels based on full wavelengths. The performance of SVR was the best with determination coefficient (R2) of .9432, root mean square errors (RMSE) of 0.7054%, and residual prediction deviation (RPD) of 3.9694 for prediction set. In order to simplify modeling process and improve calculation speed of the models, successive projections algorithm (SPA) and regression coefficient were applied for optimal wavelengths selection. Then, PCR, PLSR, and SVR models were established based on these selected wavelengths, respectively. As a result, SPA–SVR generated a satisfied effect with R2 of .9363, RMSE of 0.7021%, and RPD of 3.988 for prediction set. All results in this study indicated that the combination of chemometrics and hyperspectral imaging technology could achieve rapid and nondestructive detection of moisture content in peanut kernels.
关键词： moisture content,nondestructive detection,peanut kernels,chemometrics,hyperspectral imaging technology
更新于2025-09-19 17:13:59
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Determination and Visualization of Different Levels of Deoxynivalenol in Bulk Wheat Kernels by Hyperspectral Imaging
摘要： A hyperspectral imaging system is proposed as a method to rapidly and nondestructively predict mycotoxin deoxynivalenol (DON) levels in FHB-infected wheat kernels. Standard normal variate transformation and multiplicative scatter correction (MSC) were used in spectral preprocessing. The successive projections algorithm (SPA) and random frog algorithm were used to select the optical wavelengths. Finally, the support vector machine (SVM) technique and partial least squares discriminant analysis were applied to establish different models for determining DON levels. Based on a comparison of the results, the MSC–SPA–SVM model, with the highest classi?cation accuracy (100.00% for the training test and 97.92% for the testing set), gave the best performance, and a visualization map of the DON content level based on this model was created.
关键词： optical wavelength selection,hyperspectral imaging,classi?cation model,deoxynivalenol content,bulk wheat kernels,visualization map
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 - Single-Sample Aeroplane Detection in High-Resolution Optimal Remote Sensing Imagery
摘要： In remote sensing images, detecting aeroplanes of special shapes is difficult due to limited number of samples. Without enough training samples, most supervised learning based algorithms will fail. Focusing on the specially-shaped aeroplanes in high-resolution optical remote sensing imagery, this paper presents a single-sample approach. The proposed approach takes one sample as input and directly searches for similar matches from the image. Unlike the supervised learning algorithms which extracts information from positive and negative samples, the hyperspectral algorithm estimates the statistics of background by analyzing the global information of the target image, needless to provide negative samples. Furthermore, this algorithm tries to find a hyperplane projected on which the background is compressed while the target is preserved, making it more data-adaptive than the conventional similarity measurements. Experiments on real data have presented the robustness of the proposed method.
关键词： locally adaptive regression kernels,Aeroplane detection,constrained energy minimization
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 (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - A Novel Multiple Kernel Learning Framework for Remote Sensing Scene Classification
摘要： In the paper we propose a novel multiple kernel learning framework for representation-based classification (MKL-RC) of remote sensing image scenes. Unlike the existing methods that often greedily learn an optimal combined kernel from predefined base kernels by optimization method, resulting in high computation time but relatively better performance. The proposed approach is different from traditional kernel methods and characterized by multiple feature and multiple kernel learning in a representation-based classification manner. Experimental results on two real remote sensing scene datasets demonstrate that the proposed methods can achieve superior performance than the state-of-the-art classification methods.
关键词： representation-based classification,multiple kernels learning (MKL),extended multi-attribute profile,Remote sensing scene classification
更新于2025-09-04 15:30:14