- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Robust Hyperspectral Image Domain Adaptation With Noisy Labels
摘要： In hyperspectral image (HSI) classification, domain adaptation (DA) methods have been proved effective to address unsatisfactory classification results caused by the distribution difference between training (i.e., source domain) and testing (i.e., target domain) pixels. However, these methods rely on accurate labels in source domain, and seldom consider the performance drop resulted by noisy label, which often happens since labeling pixel in HSI is a challenging task. To improve the robustness of DA method to label noise, we propose a new unsupervised HSI DA method, which is constructed from both feature-level and classifier-level. First, a linear transformation function is learned in feature-level to align the source (domain) subspace with the target (domain) subspace. Then, a robust low-rank representation based classifier is developed to well cope with the features obtained from the aligned subspace. Since both subspace alignment and the classifier are immune to noisy labels, the proposed method obtains good classification results when confronting with noisy labels in source domain. Experimental results on two DA benchmarks demonstrate the effectiveness of the proposed method.
关键词： low-rank representation,hyperspectral image (HSI) classification,Domain adaptation,subspace alignment
更新于2025-09-23 15:22:29
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Semisupervised Stacked Autoencoder With Cotraining for Hyperspectral Image Classification
摘要： Recently, deep learning (DL) is of great interest in hyperspectral image (HSI) classification. Although many effective frameworks exist in the literature, the generally limited availability of training samples poses great challenges in applying DL to HSI classification. In this paper, we present a novel DL framework, namely, semisupervised stacked autoencoders (Semi-SAEs) with cotraining, for HSI classification. First, two SAEs are pretrained based on the hyperspectral features and the spatial features, respectively. Second, fine-tuning is alternatively conducted for the two SAEs in a semisupervised cotraining fashion, where the initial training set is enlarged by designing an effective region growing method. Finally, the classification probabilities obtained by the two SAEs are fused using a Markov random field model solved by iterated conditional modes. Experimental results based on three popular hyperspectral data sets demonstrate that the proposed method outperforms other state-of-the-art DL methods.
关键词： Deep learning (DL),stacked autoencoders (SAEs),cotraining,hyperspectral image (HSI) semisupervised classification,Markov random field (MRF)
更新于2025-09-23 15:22:29
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High Efficient Deep Feature Extraction and Classification of Spectral-Spatial Hyperspectral Image Using Cross Domain Convolutional Neural Networks
摘要： Recently, numerous remote sensing applications highly depend on the hyperspectral image (HSI). HSI classification, as a fundamental issue, has attracted increasing attention and become a hot topic in the remote sensing community. We implemented a regularized convolutional neural network (CNN), which adopted dropout and regularization strategies to address the overfitting problem of limited training samples. Although many kinds of the literature have confirmed that it is an effective way for HSI classification to integrate spectrum with spatial context, the scaling issue is not fully exploited. In this paper, we propose a high efficient deep feature extraction and the classification method for the spectral-spatial HSI, which can make full use of multiscale spatial feature obtained by guided filter. The proposed approach is the first attempt to lean a CNN for spectral and multiscale spatial features. Compared to its counterparts, experimental results show that the proposed method can achieve 3% improvement in accuracy, according to various datasets such as Indian Pines, Pavia University, and Salinas.
关键词： Convolutional neural network (CNN),hyperspectral image (HSI) classification,guided filter,spectral-spatial fusion
更新于2025-09-23 15:22:29
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[IEEE 2018 Second International Conference on Advances in Electronics, Computers and Communications (ICAECC) - Bangalore (2018.2.9-2018.2.10)] 2018 Second International Conference on Advances in Electronics, Computers and Communications (ICAECC) - Sparse Reconstruction of Hyperspectral Image using Bregman Iterations
摘要： Hyperspectral image processing plays an important role in satellite communication. Hyperspectral Image (HSI) processing requires very high ‘computational resources’ in terms of computational time and storage due to extremely large volumes of data collected by imaging spectrometers on-board the satellite. The bandwidth available to transmit the image data from satellite to the ground station is limited. As a result, Hyperspectral image compression is an active research area in the research community in past few years. The research work in the paper proposes a new scheme, Sparsification of HSI and reconstruction (SHSIR) for the reconstruction of hyperspectral image data acquired in Compressive sensing (CS) fashion. Compressed measurements similar to compressive sensing acquisition are generated using measurement matrices containing gaussian i.i.d entries. Now the reconstruction is solving the constrained optimization problem with non smooth terms. Adaptive Bregman iterations method of multipliers is used to convert the difficult optimization problem into a simple cyclic sequence problem. Experimental results from research work indicates that the proposed method performs better than the other existing techniques.
关键词： SHSIR algorithm,Hyperspectral image (HSI),Compressive sensing (CS)
更新于2025-09-23 15:22:29
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Hierarchical Sub-Pixel Anomaly Detection Framework for Hyperspectral Imagery
摘要： Anomaly detection is an important task in hyperspectral processing. Some previous works, based on statistical information, focus on Reed-Xiaoli (RX), as it is one of the most classical and commonly used methods. However, its performance tends to be affected when anomaly target size is smaller than spatial resolution. Those sub-pixel anomaly target spectra are usually much similar with background spectra, and may results in false alarm for traditional RX method. To address this issue, this paper proposes a hierarchical RX (H-RX) anomaly detection framework to enhance the performance. The proposed H-RX method consists of several different layers of original RX anomaly detector. In each layer, the RX’s output of each pixel is restrained by a nonlinear function and then imposed as a coef?cient on its spectrum for the next iteration. Furthermore, we design a spatial regularization layer to enhance the sub-pixel anomaly detection performance. To better illustrate the hierarchical framework, we provide a theoretical explanation of the hierarchical background spectra restraint and regularization process. Extensive experiments on three hyperspectral images illustrate that the proposed anomaly detection algorithm outperforms the original RX algorithm and some other classical methods.
关键词： hyperspectral image (HSI) analysis,RX,hierarchical structure,anomaly detection
更新于2025-09-23 15:21:01
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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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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
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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 (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 - 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