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- 摘要
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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 - Classification of Hyperspectral Image Based on Hybrid Neural Networks
摘要： Convolutional neural networks (CNN), which are able to extract spatial semantic features, have achieved outstanding performance in many computer vision tasks. In this paper, hybrid neural networks (HNN) are proposed to extract both spatial and spectral features in the same deep networks. The proposed networks consist of different types of hidden layers, including spatial structure layer, spatial contextual layer, and spectral layer. All those layers work as organic networks to explore as much valuable information as possible from hyperspectral data for classification. Experimental results demonstrate competitive performance of the proposed approach over other state-of-the-art neural networks methods. Moreover, the proposed method is a new way to deal with multidimensional data with deep networks.
关键词： supervised classification,feature learning,hyperspectral image (HSI),convolutional neural networks (CNN)
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 - Representative Signature Generation for Plant Detection in Hyperspectral Images
摘要： In this study, the effect of utilizing different type of signatures on plant detection success is evaluated on hyperspectral aerial images. Plant regions are tried to detect using spectral signatures of leaf, stem and tassel belonging to the plant separately and the plant representative signature (PRS) is created by averaging of signatures of selected region on the aerial images. The signatures used for detection are generated from hyperspectral images taken from 10m distance to target plant. The Spectral Angle Mapper (SAM) and Generalized Likelihood Ratio Test (GLRT) algorithms are used for target detection. Performance evaluation is made by Receiver Operating Characteristic (ROC) curves. When the results are evaluated, it is observed that the detection performance with the use of PRS is higher.
关键词： hyperspectral image processing,plant classification,corn detection,spectral library
更新于2025-09-10 09:29:36
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Efficient quantitative hyperspectral image unmixing method for large-scale Raman micro-spectroscopy data analysis
摘要： Vibrational micro-spectroscopy is a powerful optical tool, providing a non-invasive label-free chemically specific imaging for many chemical and biomedical applications. However, hyperspectral image produced by Raman micro-spectroscopy typically consists of thousands discrete pixel points, each having individual Raman spectrum at thousand wavenumbers, and therefore requires appropriate image unmixing computational methods to retrieve non-negative spatial concentration and corresponding non-negative spectra of the image biochemical constituents. Here, we present a new efficient Quantitative Hyperspectral Image Unmixing (Q-HIU) method for large-scale Raman micro-spectroscopy data analysis. This method enables to simultaneously analyse multi-set Raman hyperspectral images in three steps: (i) Singular Value Decomposition with innovative Automatic Divisive Correlation which autonomously filters spatially and spectrally uncorrelated noise from data; (ii) a robust subtraction of fluorescent background from the data using a newly developed algorithm called Bottom Gaussian Fitting; (iii) an efficient Quantitative Unsupervised/Partially Supervised Non-negative Matrix Factorization method, which rigorously retrieves non-negative spatial concentration maps and spectral profiles of the samples' biochemical constituents with no a priori information or when one or several samples’ constituents are known. As compared with state-of-the-art methods, our approach allows to achieve significantly more accurate results and efficient quantification with several orders of magnitude shorter computational time as verified on both artificial and real experimental data. We apply Q-HIU to the analysis of large-scale Raman hyperspectral images of human atherosclerotic aortic tissues and our results show a proof-of-principle for the proposed method to retrieve and quantify the biochemical composition of the tissues, consisting of both high and low concentrated compounds. Along with the established hallmarks of atherosclerosis including cholesterol/cholesterol ester, triglyceride and calcium hydroxyapatite crystals, our Q-HIU allowed to identify the significant accumulations of oxidatively modified lipids co-localizing with the atherosclerotic plaque lesions in the aortic tissues, possibly reflecting the persistent presence of inflammation and oxidative damage in these regions, which are in turn able to promote the disease pathology. For minor chemical components in the diseased tissues, our Q-HIU was able to detect the signatures of calcium hydroxyapatite and b-carotene with relative mean Raman concentrations as low as 0.09% and 0.04% from the original Raman intensity matrix with noise and fluorescent background contributions of 3% and 94%, respectively.
关键词： Baseline correction,Biochemical quantification,Hyperspectral image analysis,Multivariate curve resolution,Non-negative matrix factorization,Raman spectroscopy
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 - Relative Attribute Based Unmixing
摘要： The abundance of a mixed pixel of certain class can be understood as to get the relative score referring to the pure representative of this class, while not be classified with two absolute and discrete value as “1 or 0”. This is in accordance with the Relative Attribute Learning (RAL) problem in computer vision. In RAL, the concept of “relative attribute” is used to describe the belonging level of an object to certain class with a score which is achieved from a learn-to-rank problem using rankSVM framework. To utilize information between data samples and even of mixed pixels, Relative Attribute based Unmixing (RAU) is proposed first time by using relative attribute to describe the abundance of mixed pixel as relative purity of certain class and learn the abundance with rankSVM. The mixed data sample are used to construct training comparisons set in rankSVM with archetypes generated by the reported Kernel Archetypal Analysis (KAA) unmixing method. In addition, spectral variability is also addressed by constructing comparisons set with synonyms spectrum achieved from KAA. Experiments on both synthetic and real hyperspectral mixed image have demonstrated the potential value of proposed method for mixed pixel analysis.
关键词： relative attribute,spectral unmixing,Hyperspectral image,NMF,spectral variability
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 - Multiscale Spectral-Spatial Hyperspectral Image Classification with Adaptive Filtering
摘要： Hyperspectral images (HSI) contain a wealth of spectral and spatial information, spectral-spatial combination is an effective way in improving the classification accuracy for HSI. To characterize the variability of spatial features at different scales better, a multiscale spectral-spatial classification method with adaptive filtering (MSAF) is proposed. The proposed method consists of the following four steps. Firstly, the spectral features are extracted by a feature selection algorithm. Secondly, the adaptive edge-preserving filtering with different scales are conducted on each feature, and then several stacks of data blocks containing spatial information can be obtained. Thirdly, the combinations of the spectral and spatial data blocks are classified using support vector machine (SVM). Finally, a post-processing is conducted to improve the classification results further. The experiments on the hyperspectral data demonstrate that the proposed method can improve the classification accuracy significantly compared to the SVM classifier, especially need less parameters than the spectral-spatial EPF method.
关键词： adaptive filtering,multiscale spatial information,Hyperspectral image classification
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 - The Influence of Sampling Methods on Pixel-Wise Hyperspectral Image Classification with 3D Convolutional Neural Networks
摘要： Supervised image classification is one of the essential techniques for generating semantic maps from remotely sensed images. The lack of labeled ground truth datasets, due to the inherent time effort and cost involved in collecting training samples, has led to the practice of training and validating new classifiers within a single image. In line with that, the dominant approach for the division of the available ground truth into disjoint training and test sets is random sampling. This paper discusses the problems that arise when this strategy is adopted in conjunction with spectral-spatial and pixel-wise classifiers such as 3D Convolutional Neural Networks (3D CNN). It is shown that a random sampling scheme leads to a violation of the independence assumption and to the illusion that global knowledge is extracted from the training set. To tackle this issue, two improved sampling strategies based on the Density-Based Clustering Algorithm (DBSCAN) are proposed. They minimize the violation of the train and test samples independence assumption and thus ensure an honest estimation of the generalization capabilities of the classifier.
关键词： DBSCAN,clustering,sampling strategies,Convolutional Neural Networks (CNNs),deep learning,Hyperspectral image classification
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 - Spatially Regularzied Sparsecem for Target Detection in Hyperspectral Images
摘要： Constrained energy minimization (CEM) is a popular method for target detection in hyperspectral images. Its variant Sparse CEM uses a sparsity regularization term to promote the sparsity of the detection output. However, these approaches do not consider the spatial correlation of hyperspectral pixels, and target detection can further benefit from exploiting the spatial information. In this paper, we propose a novel constrained detection algorithm, referred to as Spatial-Sparse CEM, to simultaneously force the sparsity of the output and piecewise continuity via proper regularizations. The formulated problem is solved efficiently by using alternating direction method of multipliers (ADMM). We illustrate the enhanced performance of the Spatial-Sparse CEM algorithm via both synthetic and real hyperspectral data.
关键词： Hyperspectral image,spatially-regularized detection,target detection,(cid:96)1-norm regularization,ADMM,constrained energy minimization
更新于2025-09-10 09:29:36
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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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[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 - Superpixel Based Dimension Reduction for Hyperspectral Imagery
摘要： This paper focuses on dimension reduction (DR) technique for hyperspectral image (HSI). In this paper, we proposed a superpixel-based linear discriminant analysis (SP-LDA) dimension reduction method for HSI classification. Pixels within a local spatial neighborhood are expected to have similar spectral curves and share the same class label. To fully exploit the spatial structure, superpixel segmentation is firstly introduced to generate the superpixel map, which can adaptively explore the neighborhood structure information. Moreover, we extend the SP-LDA algorithm by combining the extracted feature from spectral and spatial dimensions, which can fully exploit complementary and consistent information from both dimensions. The experimental results on two standard hyperspectral datasets confirm the superiority of the proposed algorithms.
关键词： Hyperspectral image,superpixel,dimension reduction,spectral-spatial
更新于2025-09-10 09:29:36