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- 摘要
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- 实验方案
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Effect of denoising on hyperspectral image classification using deep networks and kernel methods
摘要： Hyperspectral Image (HSI) store the re?ectance values of a single scene or object in several continuous bands of electromagnetic spectrum. When the image is recorded, the information in some of the spectral bands gets mixed with noise. The classi?cation accuracy of hyperspectral image varies inversely with the quantity and nature of noise present in the cluster of spectral bands. Thus, denoising is a fundamental prerequisite in image processing applications like classi?cation, unmixing, etc. In this paper, we compare the effect of denoising via classi?cation using Vectorized Convolutional Neural Network (VCNN), kernel based Support Vector Machine (SVM) and Grand Uni?ed Regularized Least Squares (GURLS) classi?ers. The classi?ers are provided with raw data (without denoising) and denoised data using spectral and spatial Least Square (LS) techniques. The data given to the network are in the form of pixels, so we call the convolutional neural network (CNN) as VCNN. The experiments are performed on three standard HSI datasets. The performance of the classi?ers are evaluated based on overall and class-wise accuracy.
关键词： CNN,Least Square Denoising,IBBC,GURLS,LIBSVM,Hyperspectral Image
更新于2025-09-19 17:15:36
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Locally Weighted Discriminant Analysis for Hyperspectral Image Classification
摘要： A hyperspectral image (HSI) contains a great number of spectral bands for each pixel, which will limit the conventional image classification methods to distinguish land-cover types of each pixel. Dimensionality reduction is an effective way to improve the performance of classification. Linear discriminant analysis (LDA) is a popular dimensionality reduction method for HSI classification, which assumes all the samples obey the same distribution. However, different samples may have different contributions in the computation of scatter matrices. To address the problem of feature redundancy, a new supervised HSI classification method based on locally weighted discriminant analysis (LWDA) is presented. The proposed LWDA method constructs a weighted discriminant scatter matrix model and an optimal projection matrix model for each training sample, which is on the basis of discriminant information and spatial-spectral information. For each test sample, LWDA searches its nearest training sample with spatial information and then uses the corresponding projection matrix to project the test sample and all the training samples into a low-dimensional feature space. LWDA can effectively preserve the spatial-spectral local structures of the original HSI data and improve the discriminating power of the projected data for the final classification. Experimental results on two real-world HSI datasets show the effectiveness of the proposed LWDA method compared with some state-of-the-art algorithms. Especially when the data partition factor is small, i.e., 0.05, the overall accuracy obtained by LWDA increases by about 20% for Indian Pines and 17% for Kennedy Space Center (KSC) in comparison with the results obtained when directly using the original high-dimensional data.
关键词： hyperspectral image (HSI) classification,linear discriminant analysis (LDA),spatial-spectral information,dimensionality reduction
更新于2025-09-19 17:15:36
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Weighted-Fusion-Based Representation Classifiers for Hyperspectral Imagery
摘要： Spatial texture features have been demonstrated to be very useful for the recently-proposed representation-based classifiers, such as the sparse representation-based classifier (SRC) and nearest regularized subspace (NRS). In this work, a weighted residual-fusion-based strategy with multiple features is proposed for these classifiers. Multiple features include local binary patterns (LBP), Gabor features, and the original spectral signatures. In the proposed classification framework, representation residuals for a testing pixel from using each type of features are weighted to generate the final representation residual, and then the label of the testing pixel is determined according to the class yielding the minimum final residual. The motivation of this work is that different features represent pixels from different perspectives and their fusion in the residual domain can enhance the discriminative ability. Experimental results of several real hyperspectral image datasets demonstrate that the proposed residual-based fusion outperforms the original NRS, SRC, support vector machine (SVM) with LBP, and SVM with Gabor features, even in small-sample-size (SSS) situations.
关键词： hyperspectral image classification,Gabor features,local binary patterns (LBP),nearest regularized subspace (NRS)
更新于2025-09-19 17:15:36
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Maize seed classification using hyperspectral image coupled with multi-linear discriminant analysis
摘要： Seed purity is an important parameter for evaluating seed quality and can be effectively studied by seed classification. Hyperspectral images between 400 and 1000 nm were acquired for 1632 maize seeds (17 varieties) for classifying seed varieties. Fourteen features including a spectral feature and 13 imaging features (i.e., 5 first-order and 8 second-order textural features) were extracted from the hyperspectral image data. A multi-linear discriminant analysis (MLDA) algorithm was developed to select the optimal wavelength and transform/reduce the classification features to improve the acquisition and processing speed of the hyperspectral images. Least square support vector machine was used to develop classification models based on MLDA with spectral features, imaging features, and combination of spectral and imaging features. The effects of MLDA, uninformative variable elimination (UVE) coupled with linear discriminant analysis (LDA), and successive projection algorithm (SPA) coupled with LDA were adopted. Experimental results indicate that the combination feature based on the wavelength selection algorithm of MLDA yielded high classification accuracy under the same number of wavelengths (varying between 5 and 15). Meanwhile, the classification model based on MLDA feature transformation/reduction method achieved superior classification accuracy of 99.13% over SPA coupled with LDA (90.31%) and UVE coupled with LDA (94.17%) and improved by 2.74% relative to that of the mean spectrum of the full wavelength model. The proposed method can be used effectively for seed identification and classification.
关键词： Hyperspectral image,Maize seed,Multi-linear discriminant analysis,Feature transformation
更新于2025-09-19 17:13:59
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Grid Integration of Small-Scale Photovoltaic Systems in Secondary Distribution Network- A Review
摘要： Restoration is important in preprocessing hyperspectral images (HSI) to improve their visual quality and the accuracy in target detection or classification. In this paper, we propose a new low-rank spectral nonlocal approach (LRSNL) to the simultaneous removal of a mixture of different types of noises, such as Gaussian noises, salt and pepper impulse noises, and fixed-pattern noises including stripes and dead pixel lines. The low-rank (LR) property is exploited to obtain precleaned patches, which can then be better clustered in our spectral nonlocal method (SNL). The SNL method takes both spectral and spatial information into consideration to remove mixed noises as well as preserve the fine structures of images. Experiments on both synthetic and real data demonstrate that LRSNL, although simple, is an effective approach to the restoration of HSI.
关键词： nonlocal means,Hyperspectral image,spectral and spatial information,restoration,low rank (LR)
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON) - Valparaiso, Chile (2019.11.13-2019.11.27)] 2019 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON) - Series-Resonant DCa??DC Converter for Solar Photovoltaic Non Isolated Applications
摘要： This paper reports the outcomes of the 2014 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (IEEE GRSS). As for previous years, the IADF TC organized a data fusion contest aiming at fostering new ideas and solutions for multisource remote sensing studies. In the 2014 edition, participants considered multiresolution and multi-sensor fusion between optical data acquired at 20-cm resolution and long-wave (thermal) infrared hyperspectral data at 1-m resolution. The Contest was proposed as a double-track competition: one aiming at accurate landcover classification and the other seeking innovation in the fusion of thermal hyperspectral and color data. In this paper, the results obtained by the winners of both tracks are presented and discussed.
关键词： multimodal-,multisource-data fusion,thermal imaging,landcover classification,multiresolution-,Hyperspectral,image analysis and data fusion (IADF)
更新于2025-09-19 17:13:59
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Investigation of Dye-Sensitized Solar Cell With Photoanode Modified by TiOa??-ZnO Nanofibers
摘要： Restoration is important in preprocessing hyperspectral images (HSI) to improve their visual quality and the accuracy in target detection or classification. In this paper, we propose a new low-rank spectral nonlocal approach (LRSNL) to the simultaneous removal of a mixture of different types of noises, such as Gaussian noises, salt and pepper impulse noises, and fixed-pattern noises including stripes and dead pixel lines. The low-rank (LR) property is exploited to obtain precleaned patches, which can then be better clustered in our spectral nonlocal method (SNL). The SNL method takes both spectral and spatial information into consideration to remove mixed noises as well as preserve the fine structures of images. Experiments on both synthetic and real data demonstrate that LRSNL, although simple, is an effective approach to the restoration of HSI.
关键词： Hyperspectral image,nonlocal means,spectral and spatial information,restoration,low rank (LR)
更新于2025-09-19 17:13:59
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[IEEE 2019 IEEE 60th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON) - Riga, Latvia (2019.10.7-2019.10.9)] 2019 IEEE 60th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON) - Flicker Characteristics of a Multi-Channel Current-Controlled PWM Dimming Method of LED Lightings
摘要： Restoration is important in preprocessing hyperspectral images (HSI) to improve their visual quality and the accuracy in target detection or classification. In this paper, we propose a new low-rank spectral nonlocal approach (LRSNL) to the simultaneous removal of a mixture of different types of noises, such as Gaussian noises, salt and pepper impulse noises, and fixed-pattern noises including stripes and dead pixel lines. The low-rank (LR) property is exploited to obtain precleaned patches, which can then be better clustered in our spectral nonlocal method (SNL). The SNL method takes both spectral and spatial information into consideration to remove mixed noises as well as preserve the fine structures of images. Experiments on both synthetic and real data demonstrate that LRSNL, although simple, is an effective approach to the restoration of HSI.
关键词： spectral and spatial information,Hyperspectral image,low rank (LR),restoration,nonlocal means
更新于2025-09-19 17:13:59
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[IEEE 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS) - Amsterdam, Netherlands (2019.9.24-2019.9.26)] 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS) - A Pixel Level Scaled Fusion Model to Provide High Spatial-Spectral Resolution for Satellite Images Using LSTM Networks
摘要： Pixel-level fusion of satellite images coming from multiple sensors allows for an improvement in the quality of the acquired data both spatially and spectrally. In particular, multispectral and hyperspectral images have been fused to generate images with a high spatial and spectral resolution. In literature, there are several approaches for this task, nonetheless, those techniques still present a loss of relevant spatial information during the fusion process. This work presents a multi scale deep learning model to fuse multispectral and hyperspectral data, each with high-spatial-and-low-spectral resolution (HSaLS) and low-spatial-and-high-spectral resolution (LSaHS) respectively. As a result of the fusion scheme, a high-spatial-and-spectral resolution image (HSaHS) can be obtained. In order of accomplishing this result, we have developed a new scalable high spatial resolution process in which the model learns how to transition from low spatial resolution to an intermediate spatial resolution level and finally to the high spatial-spectral resolution image. This step-by-step process reduces significantly the loss of spatial information. The results of our approach show better performance in terms of both the structural similarity index and the signal to noise ratio.
关键词： hyperspectral image,Super resolution,Data Fusion,Long Short Term Memory,Pixel level,multispectral image
更新于2025-09-12 10:27:22
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Multichannel Pulse-Coupled Neural Network-Based Hyperspectral Image Visualization
摘要： Hyperspectral Image (HSI) visualization, which aims at displaying as much material information of original images as possible on a trichromatic monitor with natural color, plays an important role in image interpretation and analysis. However, most of the HSI visualization methods only focus on presenting the detail information of a scene without providing natural colors and distinguishing land covers with similar colors. In order to address this problem, this article proposes a multichannel pulse-coupled neural network (MPCNN)-based HSI visualization method, which consists of the following steps. First, the MPCNN is proposed and explored to fuse the original HSI so as to obtain a fused band with rich spatial details. Then, a color mapping scheme is proposed to determine the weights of red, green, and blue (RGB) channels. Finally, the weighted RGB channels are stacked together for visualization. Experiments performed on four hyperspectral data sets demonstrate that the proposed method not only displays the HSI with nature colors but also improves the details in the image. The effectiveness of the proposed method is demonstrated in terms of both visual effect and objective indexes.
关键词： multichannel pulse-coupled neural network (MPCNN),Color mapping,natural color display,hyperspectral image (HSI) visualization
更新于2025-09-12 10:27:22