- 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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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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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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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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Active Transfer Learning Network: A Unified Deep Joint Spectral-Spatial Feature Learning Model for Hyperspectral Image Classification
摘要： Deep learning has recently attracted significant attention in the field of hyperspectral images (HSIs) classification. However, the construction of an efficient deep neural network mostly relies on a large number of labeled samples being available. To address this problem, this paper proposes a unified deep network, combined with active transfer learning (TL) that can be well-trained for HSIs classification using only minimally labeled training data. More specifically, deep joint spectral–spatial feature is first extracted through hierarchical stacked sparse autoencoder (SSAE) networks. Active TL is then exploited to transfer the pretrained SSAE network and the limited training samples from the source domain to the target domain, where the SSAE network is subsequently fine-tuned using the limited labeled samples selected from both source and target domains by the corresponding active learning (AL) strategies. The advantages of our proposed method are threefold: 1) the network can be effectively trained using only limited labeled samples with the help of novel AL strategies; 2) the network is flexible and scalable enough to function across various transfer situations, including cross data set and intraimage; and 3) the learned deep joint spectral–spatial feature representation is more generic and robust than many joint spectral–spatial feature representations. Extensive comparative evaluations demonstrate that our proposed method significantly outperforms many state-of-the-art approaches, including both traditional and deep network-based methods, on three popular data sets.
关键词： multiple-feature representation,transfer learning (TL),hyperspectral image (HSI) classification,deep learning,Active learning (AL),stacked sparse autoencoder (SSAE)
更新于2025-09-10 09:29:36
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Markov Random Fields Integrating Adaptive Interclass-Pair Penalty and Spectral Similarity for Hyperspectral Image Classification
摘要： This paper presents a novel Markov random field (MRF) method integrating adaptive interclass-pair penalty (aICP2) and spectral similarity information (SSI) for hyperspectral image (HSI) classification. aICP2 structurally combines K (K ? 1)/2 (K is the number of classes) classical “Potts model” with K (K ? 1)/2 interaction coefficients. aICP2 tries a new way to solve the key problems, insufficient correction within homogeneous regions, and over-smoothness at class boundaries, in MRF-based HSI classification. It is assumed that different class pairs should be assigned with various degrees of penalties in MRF smoothness process, according to pairwise class separability and spatial class confusion in raw classification map. The Fisher ratio is modified to measure pairwise class separability with a training set. And, gray level co-occurrence matrix is used to measure spatial class confusion degree. Then, aICP2 is constructed by combining Fisher ratio and GCLM. aICP2 applies larger penalty on class pairs that confuse with each other seriously to provide sufficient smoothness, and vice versa. In addition, to protect class edges and details, SSI is introduced to make the penalty of related neighboring pixels small. aICP2ssi denotes the integration of aICP2 and SSI. The further improved method is both interclass-pair and interpixel adaptive in spatial term. A graph-cut-based α–β-swap method is introduced to optimize the proposed energy function. The experimental results on real HSI data indicate that the proposed method outperforms compared MRF-based and other spectral–spatial approaches in terms of classification accuracies and region uniformity.
关键词： graph cut,hyperspectral image (HSI) classification,Markov random fields (MRFs),Potts model,spectral similarity,Class separability
更新于2025-09-10 09:29:36
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Extreme Sparse Multinomial Logistic Regression: A Fast and Robust Framework for Hyperspectral Image Classification
摘要： Although sparse multinomial logistic regression (SMLR) has provided a useful tool for sparse classification, it suffers from inefficacy in dealing with high dimensional features and manually set initial regressor values. This has significantly constrained its applications for hyperspectral image (HSI) classification. In order to tackle these two drawbacks, an extreme sparse multinomial logistic regression (ESMLR) is proposed for effective classification of HSI. First, the HSI dataset is projected to a new feature space with randomly generated weight and bias. Second, an optimization model is established by the Lagrange multiplier method and the dual principle to automatically determine a good initial regressor for SMLR via minimizing the training error and the regressor value. Furthermore, the extended multi-attribute profiles (EMAPs) are utilized for extracting both the spectral and spatial features. A combinational linear multiple features learning (MFL) method is proposed to further enhance the features extracted by ESMLR and EMAPs. Finally, the logistic regression via the variable splitting and the augmented Lagrangian (LORSAL) is adopted in the proposed framework for reducing the computational time. Experiments are conducted on two well-known HSI datasets, namely the Indian Pines dataset and the Pavia University dataset, which have shown the fast and robust performance of the proposed ESMLR framework.
关键词： extreme sparse multinomial logistic regression (ESMLR),extended multi-attribute profiles (EMAPs),hyperspectral image (HSI) classification,linear multiple features learning (MFL),sparse multinomial logistic regression (SMLR),Lagrange multiplier
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 - Deep Tensor Factorization for Hyperspectral Image Classification
摘要： High-dimensional spectral feature and limited training samples have caused a range of difficulties for hyperspectral image (HSI) classification. Feature extraction is effective to tackle this problem. Specifically, tensor factorization is superior to some prominent methods such as principle component analysis (PCA) and non-negative matrix factorization (NMF) because it takes spatial information into consideration. Recently, deep learning has gotten more and more attention for efficiently extracting hierarchical features for various tasks. In this paper, we propose a novel feature extraction method, deep tensor factorization (DTF), to extract hierarchical and meaningful features from observed HSI. This method takes advantage of tensor in representing HSI and the merits of convolutional neural network (CNN) in hierarchical feature extraction. Specifically, a convolution operation is firstly applied in the spectral dimension of HSI to suppress the effect of noise. Then, the convolved HSI is fed into tensor factorization to learn a low rank representation of data. After that, the above two process are repeated to learn a hierarchical representation of HSI. Experimental results on two real hyperspectral datasets show the superiority of the proposed method.
关键词： Hyperspectral image (HSI) classification,feature extraction,convolutional neural network (CNN),tensor decomposition
更新于2025-09-10 09:29:36
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Convolutional Neural Network Trained by Joint Loss for Hyperspectral Image Classification
摘要： In this letter, is proposed the hyperspectral image classification method based on the convolutional neural network, which is trained jointly by the reconstruction and discriminative loss functions. In the network, small convolutional kernels are cascaded with the pooling operator to perform feature abstraction, and a decoding channel composed of the deconvolutional and unpooling operators is established. The unsupervised reconstruction, performed by the decoding channel, not only introduces priors to the network training but also is made use to enhance the discriminability of the abstracted features by the control gate. By the experiments, it is shown that the proposed method performs better than the state-of-the-art neural network-based classification methods.
关键词： Control gate,unsupervised reconstruction,convolutional neural network (CNN),joint loss (JL),hyperspectral image (HSI) classification
更新于2025-09-10 09:29:36
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Self-Supervised Feature Learning With CRF Embedding for Hyperspectral Image Classification
摘要： The challenges in hyperspectral image (HSI) classification lie in the existence of noisy spectral information and lack of contextual information among pixels. Considering the three different levels in HSIs, i.e., subpixel, pixel, and superpixel, offer complementary information, we develop a novel HSI feature learning network (HSINet) to learn consistent features by self-supervision for HSI classification. HSINet contains a three-layer deep neural network and a multifeature convolutional neural network. It automatically extracts the features such as spatial, spectral, color, and boundary as well as context information. To boost the performance of self-supervised feature learning with the likelihood maximization, the conditional random field (CRF) framework is embedded into HSINet. The potential terms of unary, pairwise, and higher order in CRF are constructed by the corresponding subpixel, pixel, and superpixel. Furthermore, the feedback information derived from these terms are also fused into the different-level feature learning process, which makes the HSINet-CRF be a trainable end-to-end deep learning model with the back-propagation algorithm. Comprehensive evaluations are performed on three widely used HSI data sets and our method outperforms the state-of-the-art methods.
关键词： self-supervision,feature learning,convolutional neural network (CNN),Conditional random field (CRF),hyperspectral image (HSI) classification
更新于2025-09-09 09:28:46