- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Fluorescence hyperspectral image technique coupled with HSI method to predict solanine content of potatoes
摘要： In order to ensure the edibility of potatoes, fluorescence hyperspectral images of potato samples were obtained to predict the solanine content in potatoes. For the best ROI (region of interest), the S‐component of saturation was extracted by the HSI colorimetric technology to characterize the bud eye of potatoes in three‐dimensional geometric space. The effective bud eye was located as the geometric center of ROI and the average spectral information was obtained. After pretreatment and selection of feature wavelengths, the predicting mode of SVR was established and was optimized by adjusting the penalty coefficient c and the core coefficient g of radial basis function (RBF). Finally, the determinant coefficient of the model was 0.9143 and the root mean square error was 0.0296, which could basically meet the application requirements. It was concluded that the method based on hyperspectral fluorescence image and HSI colorimetry could predict the solanine content in potatoes accurately through the optimized SVR model.
关键词： SVR model,fluorescence hyperspectral image,potatoes,HSI colorimetry,solanine content
更新于2025-09-11 14:15:04
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[IEEE 2018 OCEANS - MTS/IEEE Kobe Techno-Ocean (OTO) - Kobe (2018.5.28-2018.5.31)] 2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO) - An Improved Method for Modeling the Island Photovoltaic Power Generation System With MPPT
摘要： Simplex maximum distance (SMD) is an algorithm based on that the pixel with the biggest distance from simplex formed by known endmembers is most likely to be the next endmember. However, SMD involves calculation of some intermediate variables, such as simplex’s normal vector, and intersection point of simplex and line, leading to computation complexity. In addition, high brightness points, outliers and isolated noise points in hyperspectral image are often extracted as endmembers in SMD. To overcome these two shortages, an improved simplex maximum distance (ISMD) algorithm is presented in the paper. To simplify computation procedure, ISMD defines the distance from pixel to simplex as ratio of volumes of parallel polyhedrons with adjacent dimensions. Once distances of all pixels from existing simplex are received, a set of similar pixels was selected from multiple pixels with a larger distance according to the spectral angle. The set of pixels is averaged to be the new endmember. The ISMD algorithm was assessed using simulated and real AVIRIS images. Compared with SMD, ISMD better extracted real endmembers in simulated image. And spectral angle between endmember obtained by ISMD and corresponding mineral from USGS spectral library is less for AVIRIS image.
关键词： endmember extraction,hyperplane,hyperspectral image,simplex maximum distance formatting
更新于2025-09-11 14:15:04
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Statistical Detection Theory Approach to Hyperspectral Image Classification
摘要： This paper presents a statistical detection theory approach to hyperspectral image (HSI) classification which is quite different from many conventional approaches reported in the HSI classification literature. It translates a multi-target detection problem into a multi-class classification problem so that the well-established statistical detection theory can be readily applicable to solving classification problems. In particular, two types of classification, a priori classification and a posteriori classification, are developed in corresponding to Bayes detection and maximum a posteriori (MAP) detection, respectively, in detection theory. As a result, detection probability and false alarm probability can also be translated to classification rate and false classification rate derived from a confusion classification matrix used for classification. To evaluate the effectiveness of a posteriori classification, a new a posteriori classification measure, to be called precision rate (PR), is also introduced by MAP classification in contrast to overall accuracy (OA) that can be considered as a priori classification measure and has been used for Bayes classification. The experimental results provide evidence that a priori classifier as Bayes classifier which performs well in terms of OA does not necessarily perform well as a posteriori classifier in terms of PR. That is, PR is the only criterion that can be used as a posteriori classification measure to evaluate how well a classifier performs.
关键词： precision rate (PR),hyperspectral image (HSI) classification,average accuracy (AA),A posteriori classification,overall accuracy (OA),a priori classification
更新于2025-09-11 14:15:04
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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 - A Two-Branch Network with Semi-Supervised Learning for Hyperspectral Classification
摘要： In order to promote progress on fusion and analysis methodologies for multi-source remote sensing data, The Image Analysis and Data Fusion Technical Committee organized the 2018 IEEE GRSS Data Fusion contest. In this contest, we proposed a two-branch convolution network for hyperspectral image classification with a data re-sampling strategy and semi-supervised learning to address three existing problems, i.e. multi-scale feature learning, data imbalance, and small size of the dataset. The contest showed that our proposal achieved the best performance on two metrics: the overall accuracy of 77.39% and a kappa coefficient of 0.76 on the hyperspectral images provided by 2018 IEEE GRSS Data Fusion Contest.
关键词： deep learning,Hyperspectral image,image classification,semi-supervised learning
更新于2025-09-10 09:29:36
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Semi-Supervised hyperspectral image classification using local low-rank representation
摘要： In the area of hyperspectral image (HSI) classification, graph-based semi-supervised learning (SSL) has been proved to be highly effective. Constructing a proper graph is critical for graph-based SSL tasks. In HSI, spectral distance is widely used to calculate the weight of graph edge, though it can be influenced by noise and outliers. Meanwhile, links among all the data points are incorporated in the graph, including those from different subspaces. Thus the constructed graph might contain incorrect information. In this letter, a novel semi-supervised HSI classification method using local low-rank representation (SL2R) is proposed. Edge weight calculation will not be affected by noise or outliers thanks to the robustness of low-rank representation (LRR). Since each graph is constructed at local level, where pixels are basically embedded in the same subspace, links among uncorrelated pixels can be removed. Moreover, spatial context is naturally characterized by low-rank constraint on adjacent pixels. Experimental results on two data sets (Indian Pines and Botswana) confirm the effectiveness of the proposed method.
关键词： spectral-spatial classification,semi-supervised learning,hyperspectral image classification,low-rank representation
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 Nonconvex Sparsity Measure for Hyperspectral Images Restoration
摘要： Recently, robust principal component analysis (RPCA) based methods have been used for hyperspectral images (HSIs) restoration to simultaneously remove several types of noise, including Gaussian noise, impulse noise, stripes, and so on. However, most of these RPCA methods formulate the optimization problem with a convex l1-norm penalty, which over-penalizes large entries of vectors, and results in a biased solution. In this paper, a novel nonconvex sparsity regularizer (NonSR) for measuring the clean HSI low rank structure and noise sparsity structure is proposed, which can effectively approximate rank function and noise sparsity instead of the convex l1-norm. By embedding the sparsity regularizer into the RPCA framework, we formulate a new model, which enhance the capability in simultaneously removing several types of noise. In addition, an iterative algorithm based on the alternative direction multiplier method (ADMM) is developed to effectively solve the proposed model. Experimental results demonstrate that the proposed NonSR method outperforms state-of-the-art HSIs restoration techniques.
关键词： nonconvex sparsity measure,robust principal component analysis (RPCA),restoration,Hyperspectral image (HSI)
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 - AI-NET: Attention Inception Neural Networks for Hyperspectral Image Classification
摘要： Recently, deep learning methods have dominated many ?elds thanks to its powerful discriminative feature learning ability. While for hyperspectral images (HSI) analysis, these deep neural networks methods suffer from over?tting as the number of labeled training samples are limited. Thus more ef?- cient neural network architecture should be designed to improve the performance of HSI classi?cation task. In this paper, a novel attention inception module is introduced to extract features dynamically from multi-resolution convolutional ?lters. The AI-NET constructed by stacking the proposed attention inception module can adaptively learn the network architecture by dynamically routing between the attention inception modules. By exploiting different spatial size convolutional ?lters and dynamic CNN architecture, more representative feature can be learned with limited training samples. Extensive experimental results have shown that the proposed method can adaptively adjust the network architecture and obtain better classi?cation performance.
关键词： Dynamic routing,Attention model,Hyperspectral image classification,Deep learning,Inception model
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 - Detection of Fusarium Wilt on Phalaenopsis Stem Base Region Using Band Selection Techniques
摘要： Phalaenopsis is a significant agriculture product with high economic value in Taiwan. However, the fusarium wilt causes Phalaenopsis leaves turning yellow, thinning, water loss, and finally died. This paper presents an emerging method to detect fusarium wilt on Phalaenopsis stem base. In order to build the detection models, the hyperspectral databases are generated form two statues of Phalaenopsis samples, which are health and disease sample. We applied band selection (BS) processing base on band prioritization (BP) and band de-correlation (BD) to extract the significant bands and eliminate the redundant bands. Then, three algorithms were used, orthogonal subspace projection (OSP), constrain energy minimization (CEM), and support vector machine (SVM) to detect the fusarium wilt.
关键词： OSP,SVM,Hyperspectral image,Phalaenopsis,Band selection,fusarium wilt,CEM
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 - Sub-Pixel Mapping with Hyperspectral Images Using Super-Resolution
摘要： Hyperspectral images are rich in spectral content but their spatial resolution is relatively poor. It can lead to mixed pixels and sub-pixel targets. In order to improve the reliability of information provided by hyperspectral image analysis and make the results practically usable, one needs to improve their spatial resolution. Due to physical constraints and associated cost, increasing the resolution by improving the sensors may not be a practical option. Thus one effective solution is some form of post-processing of hyperspectral data. Such an algorithmic resolution enhancement is called “super-resolution”. In this paper single image super-resolution of hyperspectral image has been attempted. The use of Hopfield Neural Network for successful landuse/landcover classification of Hyperspectral image has been shown. A successful attempt was made to improve initialization of the Hopfield neural network. The results were verified visually as well as statistically.
关键词： Sub-pixel Mapping,Hyperspectral Image,Landuse/landcover,Mixed Pixel,Super-resolution,Hopfield Neural Network
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 - Non-Convex Low-Rank Approximation for Hyperspectral Image Recovery with Weighted Total Varaition Regularization
摘要： Low-rank representation has been widely used as a powerful tool in hyperspectral image (HSI) recovery. The existing studies involving low-rank problems are commonly under the nuclear norm penalization. However, nuclear norm minimization tends to over-shrink the components of rank, which leads to modeling bias. In this paper, a new non-convex penalty is introduced to obtain an unbiased low-rank approximation. In Addition, local spatial neighborhood weighted spectral-spatial total variation (TV) regularization is introduced to preserve spatial structural information. And sparse 1l-norm is used as a constraint to sparse noise. Finally, a novel HSI non-convex low-rank relaxation restoration model is proposed. A number of experiments show that the proposed method can effectively remove the mixed-noise, and result in an unbiased estimate with better robustness.
关键词： Hyperspectral image(HSI),total variation(TV),low-rank representation,non-convex relaxation
更新于2025-09-10 09:29:36