- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Soil Particles and Phenanthrene Interact in Defining the Metabolic Profile of Pseudomonas putida G7: A Vibrational Spectroscopy Approach
摘要： In soil, organic matter and mineral particles (soil particles; SPs) strongly influence the bio-available fraction of organic pollutants, such as polycyclic aromatic hydrocarbons (PAHs), and the metabolic activity of bacteria. However, the effect of SPs as well as comparative approaches to discriminate the metabolic responses to PAHs from those to simple carbon sources are seldom considered in mineralization experiments, limiting our knowledge concerning the dynamics of contaminants in soil. In this study, the metabolic profile of a model PAH-degrading bacterium, Pseudomonas putida G7, grown in the absence and presence of different SPs (i.e., sand, clays and humic acids), using either phenanthrene or glucose as the sole carbon and energy source, was characterized using vibrational spectroscopy (i.e., FT-Raman and FT-IR spectroscopy) and multivariate classification analysis (i.e., PLS-DA). The different type of SPs specifically altered the metabolic profile of P. putida, especially in combination with phenanthrene. In comparison to the cells grown in the absence of SPs, sand induced no remarkable change in the metabolic profile of the cells, whereas clays and humic acids affected it the most, as revealed by the higher discriminative accuracy (R2, RMSEP and sensitivity) of the PLS-DA for those conditions. With respect to the carbon-source (phenanthrene vs. glucose), no effect on the metabolic profile was evident in the absence of SPs or in the presence of sand. On the other hand, with clays and humic acids, more pronounced spectral clusters between cells grown on glucose or on phenanthrene were evident, suggesting that these SPs modify the way cells access and metabolize PAHs. The macromolecular changes regarded mainly protein secondary structures (a shift from α-helices to β-sheets), amino acid levels, nucleic acid conformation and cell wall carbohydrates. Our results provide new interesting evidences that SPs specifically interact with PAHs in defining bacteria metabolic profiles and further emphasize the importance of studying the interaction of bacteria with their surrounding matrix to deeply understand PAHs degradation in soils.
关键词： phenanthrene,FTIR spectroscopy,soil particles,multivariate classification analysis,bacteria,metabolic profile,FT-Raman spectroscopy
更新于2025-11-14 15:16:37
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Semisupervised Scene Classification for Remote Sensing Images: A Method Based on Convolutional Neural Networks and Ensemble Learning
摘要： The scarcity of labeled samples has been the main obstacle to the development of scene classification for remote sensing images. To alleviate this problem, the efforts have been dedicated to semisupervised classification which exploits both labeled and unlabeled samples for training classifiers. In this letter, we propose a novel semisupervised method that utilizes the effective residual convolutional neural network (ResNet) to extract preliminary image features. Moreover, the strategy of ensemble learning (EL) is adopted to establish discriminative image representations by exploring the intrinsic information of all available data. Finally, supervised learning is performed for scene classification. To verify the effectiveness of the proposed method, it is further compared with several state-of-the-art feature representation and semisupervised classification approaches. The experimental results show that by combining ResNet features with EL, the proposed method can obtain more effective image representations and achieve superior results.
关键词： remote sensing (RS) images,Semi-supervised classification,ensemble learning (EL),scene classification,Convolutional neural networks (CNNs)
更新于2025-09-23 15:23:52
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Tree-Shaped Sampling Based Hybrid Multi-Scale Feature Extraction for Texture Classification
摘要： Efficiency, distinctiveness and robustness are three main goals for feature extractors in application of texture classification. In this paper, a new feature extractor is designed which aims to achieve these three goals simultaneously. The contributions are threefold. Firstly, a tree-shaped multi-scale sampling structure is proposed to acquire points distributed along two circles and one octagon. Secondly, four histogram vectors are obtained by quantizing the sampling values through a hybrid strategy. In order to suppress the noise, mean filtering is used as a preprocessing step and the four vectors are concatenated to form the discriminant vector. Thirdly, experiments are conducted on different datasets with several well-known feature extractors. The results show that the proposed method improves the classification accuracy effectively and robustly, while has a moderate complexity. The source code is available at: https://github.com/madd2014/TSSHM.
关键词： texture classification,Feature extraction,multi-scale structure,tree-shaped sampling
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 - Fusion of Sentinel-1 and Sentinel-2 Images for Classification of Agricultural Areas Using a Novel Classification Approach
摘要： A continuously growing world population increases steadily the demand of foods. This results in strong changes that occur on agricultural sites. Remote sensing data provides an excellent opportunity to monitor these changes which is a crucial base to assess the impact of these changes on the climate or the natural resources. In the presented study, we tested the performance of a new crop classification method for a stack of Sentinel-1 (S1) and Sentinel-2 (S2) images taken within one growing season. We proved, that the new PSP method performs better for S1 images revealing an overall accuracy (OA) of 75% compared to 60% for the Random Forest classifier (RF). The PSP method outperformed also for the fused dataset of S1 and S2 images (72% OA for PSP, 62% for RF). The results illustrate the benefits for crop classifications provided by PSP and give crucial insights for the advantages and limits of S1 and S2 data fusion.
关键词： Classification,Fusion,Agriculture,Sentinel-1,Sentinel-2
更新于2025-09-23 15:23:52
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Superpixel-Based Semisupervised Active Learning for Hyperspectral Image Classification
摘要： In this work, we propose a new semisupervised active learning approach for hyperspectral image classification. The proposed method aims at improving machine generalization by using pseudolabeled samples, both confident and informative, which are automatically and actively selected, via semisupervised learning. The learning is performed under two assumptions: a local one for the labeling via a superpixel-based constraint dedicated to the spatial homogeneity and adaptivity into the pseudolabels, and a global one modeling the data density by a multinomial logistic regressor with a Markov random field regularizer. Furthermore, we propose a density-peak-based augmentation strategy for pseudolabels, due to the fact that the samples without manual labels in their superpixel neighborhoods are out of reach for the automatic sampling. Three real hyperspectral datasets were used in our experiments to evaluate the effectiveness of the proposed superpixel-based semisupervised learning approach. The obtained results indicate that the proposed approach can greatly improve the potential for semisupervised learning in hyperspectral image classification.
关键词： semisupervised learning,hyperspectral image classification,superpixel,clustering,Active learning
更新于2025-09-23 15:23:52
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[IEEE 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) - Vancouver, BC, Canada (2018.8.29-2018.8.31)] 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) - Deep Transfer Learning for Hyperspectral Image Classification
摘要： Hyperspectral image (HSI) includes a vast quantities of samples, large number of bands, as well as randomly occurring redundancy. Classifying such complex data is challenging, and the classification performance generally is affected significantly by the amount of labeled training samples. Collecting such labeled training samples is labor and time consuming, motivating the idea of borrowing and reusing labeled samples from other pre-existing related images. Therefore transfer learning, which can mitigate the semantic gap between existing and new HSI, has recently drawn increasing research attention. However, existing transfer learning methods for HSI which concentrated on how to overcome the divergence among images, may neglect the high level latent features during the transfer learning process. In this paper, we present two novel ideas based on this observation. We propose constructing and connecting higher level features for the source and target HSI data, to further overcome the cross-domain disparity. Different from existing methods, no priori knowledge on the target domain is needed for the proposed classification framework, and the proposed framework works for both homogeneous and heterogenous HSI data. Experimental results on real world hyperspectral images indicate the significance of the proposed method in HSI classification.
关键词： supervised classification,salient samples,Hyperspectral image,Transfer learning
更新于2025-09-23 15:23:52
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[IEEE 2018 3rd International Conference on Computer Science and Engineering (UBMK) - Sarajevo, Bosnia and Herzegovina (2018.9.20-2018.9.23)] 2018 3rd International Conference on Computer Science and Engineering (UBMK) - Hyperspectral Image Classification Using Reduced Extreme Learning Machine
摘要： In the classification of hyperspectral images, kernel based approaches have been shown to be successful results. Too much training or testing data in the images increases the computation time and memory requirements in the kernel computations. Extreme learning machines that can be used with the kernel approach also need the same requirements in kernel computations. In this study, improvements were made in terms of computation time and memory using reduced kernel extreme learning machines (RKELM). The obtained results are presented comparatively through the tables of performance and time information with kernel extreme learning machine (KELM).
关键词： classification,spectral information,Hyperspectral images,reduced kernel extreme learning machine
更新于2025-09-23 15:23:52
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Analysis of NIR spectroscopic data using decision trees and their ensembles
摘要： Decision trees and their ensembles became quite popular for data analysis during the past decade. One of the main reasons for that is current boom in big data, where traditional statistical methods (such as, e.g., multiple linear regression) are not very efficient. However, in chemometrics these methods are still not very widespread, first of all because of several limitations related to the ratio between number of variables and observations. This paper presents several examples on how decision trees and their ensembles can be used in analysis of NIR spectroscopic data both for regression and classification. We will try to consider all important aspects including optimization and validation of models, evaluation of results, treating missing data and selection of most important variables. The performance and outcome of the decision tree-based methods are compared with more traditional approach based on partial least squares.
关键词： Decision trees,Classification and regression trees,Random forests,NIR spectroscopy
更新于2025-09-23 15:23:52
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Identifying Mangrove Species Using Field Close-Range Snapshot Hyperspectral Imaging and Machine-Learning Techniques
摘要： Investigating mangrove species composition is a basic and important topic in wetland management and conservation. This study aims to explore the potential of close-range hyperspectral imaging with a snapshot hyperspectral sensor for identifying mangrove species under field conditions. Specifically, we assessed the data pre-processing and transformation, waveband selection and machine-learning techniques to develop an optimal classification scheme for eight mangrove species in Qi’ao Island of Zhuhai, Guangdong, China. After data pre-processing and transformation, five spectral datasets, which included the reflectance spectra R and its first-order derivative d(R), the logarithm of the reflectance spectra log(R) and its first-order derivative d[log(R)], and hyperspectral vegetation indices (VIs), were used as the input data for each classifier. Consequently, three waveband selection methods, including the stepwise discriminant analysis (SDA), correlation-based feature selection (CFS), and successive projections algorithm (SPA) were used to reduce dimensionality and select the effective wavebands for identifying mangrove species. Furthermore, we evaluated the performance of mangrove species classification using four classifiers, including linear discriminant analysis (LDA), k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM). Application of the four considered classifiers on the reflectance spectra of all wavebands yielded overall classification accuracies of the eight mangrove species higher than 80%, with SVM having the highest accuracy of 93.54% (Kappa = 0.9256). Using the selected wavebands derived from SPA, the accuracy of SVM reached 93.13% (Kappa = 0.9208). The addition of hyperspectral VIs and d[log(R)] spectral datasets further improves the accuracies to 93.54% (Kappa = 0.9253) and 96.46% (Kappa = 0.9591), respectively. These results suggest that it is highly effective to apply field close-range snapshot hyperspectral images and machine-learning classifiers to classify mangrove species.
关键词： machine learning,waveband selection,mangrove species classification,close-range hyperspectral imaging,field hyperspectral measurement
更新于2025-09-23 15:23:52
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Dictionaries of deep features for land-use scene classification of very high spatial resolution images
摘要： Land-use classification in very high spatial resolution images is critical in the remote sensing field. Consequently, remarkable efforts have been conducted towards developing increasingly accurate approaches for this task. In recent years, deep learning has emerged as a dominant paradigm for machine learning, and methodologies based on deep convolutional neural networks have received particular attention from the remote sensing community. These methods typically utilize transfer learning and/or data augmentation to accommodate a small number of labeled images in the publicly available datasets in this field. However, they typically require powerful computers and/or a long time for training. In this work, we propose a simple and novel method for land-use classification in very high spatial resolution images, which efficiently combines transfer learning with a sparse representation. Specifically, the proposed method performs the classification of land-use scenes using a modified version of the well-known sparse representation-based classification method. While this method directly uses the training images to form dictionaries, which are employed to classify test images, our method utilizes a pre-trained deep convolutional neural network and the Gaussian mixture model to generate more robust and compact 'dictionaries of deep features.' The effectiveness of the proposed method was evaluated on two publicly available datasets: UC Merced and Brazilian Cerrado–Savana. The experimental results suggest that our method can potentially outperform state-of-the-art techniques for land-use classification in very high spatial resolution images.
关键词： Dictionary learning,Land-use classification,Sparse representation,Feature learning,Deep learning
更新于2025-09-23 15:23:52