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A review on graph-based semi-supervised learning methods for hyperspectral image classification
摘要： In this article, a comprehensive review of the state-of-art graph-based learning methods for classification of the hyperspectral images (HSI) is provided, including a spectral information based graph semi-supervised classification and a spectral-spatial information based graph semi-supervised classification. In addition, related techniques are categorized into the following sub-types: (1) Manifold representation based Graph Semi-supervised Learning for HSI Classification (2) Sparse representation based Graph Semi-supervised Learning for HSI Classification. For each technique, methodologies, training and testing samples, various technical difficulties, as well as performances, are discussed. Additionally, future research challenges imposed by the graph-based model are indicated.
关键词： Image classification,Hyperspectral images,Semi-supervised learning,Graph-based learning
更新于2025-09-23 15:23:52
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Imaging analysis of chlorophyll fluorescence induction for monitoring plant water and nitrogen treatments
摘要： The objective of this study was to check whether different water and nitrogen treatments and, even the water-nitrogen coupling effect of plants could be correctly differentiated via chlorophyll a fluorescence image. We developed a classification method using the imaging analysis of chlorophyll a fluorescence induction based on Artificial Neural Network. The measurements were carried out on scheffera octophylla (Lour.) Harms, and the images were recorded at 690 nm with a high-resolution imaging device consisting of LEDs for an excitation at 460 nm and an Electron-Multiplying CCD camera. The effect of three different water and three different nitrogen treatments on the fluorescence parameters were obtained by hundreds of time-resolved fluorescence images. We used a Radial Basis Function neural network to model and test the sample data. The results showed that the different water and nitrogen statuses of plants were identified by the chlorophyll a fluorescence images and showed a high recognition accuracy. Compared with nitrogen, water had more of an influence on chlorophyll a fluorescence and was easier to identify. However, because the water and nitrogen restrict and promote each other, studying the coupling effect of water and nitrogen is necessary. Nine levels of water-nitrogen coupling plants were tested and classified. We discovered that a significant decrease on the classified accuracy was observed for the high nitrogen and low nitrogen treatments, while under a medium N-supply, the recognition rate was high. The method in this paper allowed plants to be classified under different water and nitrogen treatments, and has the potential to monitor the water and nitrogen coupling effect of plants in situ.
关键词： Artificial Neural Network,Classification,Recognition,Chlorophyll a Fluorescence
更新于2025-09-23 15:23:52
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Using multi-scale and hierarchical deep convolutional features for 3D semantic classification of TLS point clouds
摘要： Point cloud classification, which provides meaningful semantic labels to the points in a point cloud, is essential for generating three-dimensional (3D) models. Its automation, however, remains challenging due to varying point densities and irregular point distributions. Adapting existing deep-learning approaches for two-dimensional (2D) image classification to point cloud classification is inefficient and results in the loss of information valuable for point cloud classification. In this article, a new approach that classifies point cloud directly in 3D is proposed. The approach uses multi-scale features generated by deep learning. It comprises three steps: (1) extract single-scale deep features using 3D convolutional neural network (CNN); (2) subsample the input point cloud at multiple scales, with the point cloud at each scale being an input to the 3D CNN, and combine deep features at multiple scales to form multi-scale and hierarchical features; and (3) retrieve the probabilities that each point belongs to the intended semantic category using a softmax regression classifier. The proposed approach was tested against two publicly available point cloud datasets to demonstrate its performance and compared to the results produced by other existing approaches. The experiment results achieved 96.89% overall accuracy on the Oakland dataset and 91.89% overall accuracy on the Europe dataset, which are the highest among the considered methods.
关键词： point cloud,multi-scale,classification,3D,Deep learning
更新于2025-09-23 15:23:52
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Learning Dual Geometric Low-Rank Structure for Semisupervised Hyperspectral Image Classification
摘要： Most of the available graph-based semisupervised hyperspectral image classification methods adopt the cluster assumption to construct a Laplacian regularizer. However, they sometimes fail due to the existence of mixed pixels whose recorded spectra are a combination of several materials. In this paper, we propose a geometric low-rank Laplacian regularized semisupervised classifier, by exploring both the global spectral geometric structure and local spatial geometric structure of hyperspectral data. A new geometric regularized Laplacian low-rank representation (GLapLRR)-based graph is developed to evaluate spectral-spatial affinity of mixed pixels. By revealing the global low-rank and local spatial structure of images via GLapLRR, the constructed graph has the characteristics of spatial–spectral geometry description, robustness, and low sparsity, from which a more accurate classification of mixed pixels can be achieved. The proposed method is experimentally evaluated on three real hyperspectral datasets, and the results show that the proposed method outperforms its counterparts, when only a small number of labeled instances are available.
关键词： Dual geometric low-rank structure,mixed pixels,spectral-spatial affinity,hyperspectral image classification (HIC),support vector machine,semisupervised
更新于2025-09-23 15:23:52
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Evaluation of ForestPA for VHR RS image classification using spectral and superpixel-guided morphological profiles
摘要： In very high resolution (VHR) remote sensing (RS) classification tasks, conventional pixel-based contextual information extraction methods such as morphological profiles (MPs), extended MPs (EMPs) and MPs with partial reconstruction (MPPR) with limited numbers, sizes and shapes of structural elements (SEs) cannot perfectly match all sizes and shapes of the objects in an image. To overcome such limitation, we introduce novel spatial feature extractors, namely, the superpixel-guided morphological profiles (SPMPs), where the superpixels are used as SEs in opening by reconstruction and closing by reconstruction operations. Moreover, to avoid possible side effects from unusual maximum and minimum values within superpixels, the mean pixel value of superpixels is adopted (SPMPsM). Additionally, new decision forest based on penalizing the attributes in previous trees, the ForestPA is introduced and evaluated through a comparative investigation on three VHR multi-/hyperspectral RS image classification tasks. Support vector machine and benchmark ensemble classifiers, including bagging, AdaBoost, MultiBoost, ExtraTrees, Random Forest and Rotation Forest, are adopted. The experimental results confirm the effectiveness and superior performances of the proposed SPMPs and SPMPsM relative to those of the MPs and MPPR. Moreover, ForestPA outperforms only bagging and is not suitable for learning from large numbers of samples with high dimensionality from the computational efficiency and classification accuracy perspective.
关键词： ForestPA,superpixel,MPs,superpixel-guided morphological profiles,MPPR,image classification,VHR images
更新于2025-09-23 15:23:52
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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 - Towards Joint Land Cover and Crop Type Mapping with Numerous Classes
摘要： The detailed, accurate and frequent land cover and crop-type mapping emerge as essential for several scientific communities and geospatial applications. This paper presents a methodology for the semi-automatic production of land cover and crop type maps using a highly analytic nomenclature of more than 40 classes. An intensive manual annotation procedure was carried out for the production of reference data. A class nomenclature based on CORINE land cover Level-3 was employed along with several additional crop-type classes. Multitemporal surface reflectance Landsat-8 data for the year of 2016 were used for all classification experiments with a linear SVM classifier. Quantitative and qualitative evaluation highlighted the efficiency of the proposed approach achieving high accuracy rates. Further analysis on individual classes’ performance highlighted the challenges in the proposed classification scheme as well as important outcomes regarding the spectral behavior of the considered categories.
关键词： support vector machines,CORINE Land Cover,Landsat-8,classification
更新于2025-09-23 15:23:52
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Hyperspectral band selection for soybean classification based on information measure in FRS theory
摘要： Soybeans and soy foods have attracted widespread attention due to their health benefits. Special varieties of soybeans are in demand from soybean processing enterprises. Because of the advantage of rapid measurement with minimal sample preparation, hyperspectral imaging technology is used for classifying soybean varieties. Based on fuzzy rough set (FRS) theory, the research of hyperspectral band selection can provide the foundation for variety classification. The performance of band selection with Gaussian membership functions and triangular membership functions under various parameters were explored. Appropriate ranges of parameters were determined by the number of bands and mutual information of subsets relative to the decision. The effectiveness of the proposed algorithms was validated through experiments on soybean hyperspectral datasets by building two classification methods, namely Extreme Learning Machine and Random Forest. Compared with ranking methods, the proposed algorithm provides a promising improvement in classification accuracy by selecting highly informative bands. To further reduce the size of the subset, a post-pruning design was used. For the Gaussian membership function, a subset containing eight bands achieved an average accuracy of 99.11% after post-pruning. As well as classification accuracy, we explored stability of band selection algorithm under small perturbations. The band selection algorithm of the Gaussian membership function was more stable than that of the triangular membership function. The results showed that the information measure (IM) based band selection algorithm is effective at obtaining satisfactory classification accuracy and providing stable results under perturbations.
关键词： Soybean classification,Information measure,Band selection,Fuzzy rough set,Hyperspectral imaging
更新于2025-09-23 15:23:52
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Noise reduction for near-infrared spectroscopy data using extreme learning machines
摘要： The near infrared (NIR) spectra technique is an effective approach to predict chemical properties and it is typically applied in petrochemical, agricultural, medical, and environmental sectors. NIR spectra are usually of very high dimensions and contain huge amounts of information. Most of the information is irrelevant to the target problem and some is simply noise. Thus, it is not an easy task to discover the relationship between NIR spectra and the predictive variable. However, this kind of regression analysis is one of the main topics of machine learning. Thus machine learning techniques play a key role in NIR based analytical approaches. Pre-processing of NIR spectral data has become an integral part of chemometrics modeling. The objective of the pre-processing is to remove physical phenomena (noise) in the spectra in order to improve the regression or classification model. In this work, we propose to reduce the noise using extreme learning machines which have shown good predictive performances in regression applications as well as in large dataset classification tasks. For this, we use a novel algorithm called C-PL-ELM, which has an architecture in parallel based on a non-linear layer in parallel with another non-linear layer. Using the soft margin loss function concept, we incorporate two Lagrange multipliers with the objective of including the noise of spectral data. Six real-life dataset were analyzed to illustrate the performance of the developed models. The results for regression and classification problems confirm the advantages of using the proposed method in terms of root mean square error and accuracy.
关键词： Parallel layers,Constrained optimization,Regression,Near-infrared spectroscopy,Classification
更新于2025-09-23 15:23:52
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Segmented and non-segmented stacked denoising autoencoder for hyperspectral band reduction
摘要： Hyperspectral image (HSI) analysis often requires selecting the most informative bands instead of processing the whole data without losing the key information. Existing band reduction (BR) methods have the capability to reveal the nonlinear properties exhibited in the data but at the expense of losing its original representation. To cope with the said issue, an unsupervised non-linear segmented and non-segmented stacked denoising autoencoder (UDAE)-based BR method is proposed. Our aim is to find an optimal mapping and construct a lower-dimensional space that has a similar structure to the original data with least reconstruction error. The proposed method first confronts the original HS data into smaller regions in the spatial domain and then each region is processed by UDAE individually. This results in reduced complexity and improved efficiency of BR for classification. Our experiments on publicly available HS datasets with various types of classifiers demonstrate the effectiveness of UDAE method which equates favorably with other state-of-the-art dimensionality reduction and BR methods.
关键词： Autoencoder (AE),Hyperspectral imaging (HSI),Classification,Clustering,Band reduction (BR)
更新于2025-09-23 15:23:52
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Multi-label chest X-ray image classification via category-wise residual attention learning
摘要： This paper considers the problem of multi-label thorax disease classification on chest X-ray images. Identifying one or more pathologies from a chest X-ray image is often hindered by the pathologies unrelated to the targets. In this paper, we address the above problem by proposing a category-wise residual attention learning (CRAL) framework. CRAL predicts the presence of multiple pathologies in a class-specific attentive view. It aims to suppress the obstacles of irrelevant classes by endowing small weights to the corresponding feature representation. Meanwhile, the relevant features would be strengthened by assigning larger weights. Specifically, the proposed framework consists of two modules: feature embedding module and attention learning module. The feature embedding module learns high-level features with a convolutional neural network (CNN) while the attention learning module focuses on exploring the assignment scheme of different categories. The attention module can be flexibly integrated into any feature embedding networks with end-to-end training. The comprehensive experiments are conducted on the Chest X-ray14 dataset. CRAL yields the average AUC score of 0.816 which is a new state of the art.
关键词： Image classification,Chest X-ray,Convolutional neural network,Residual attention
更新于2025-09-23 15:23:52