- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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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 - The Earth Obsevation Data Ecosystem Monitoring (Eodesm) System
摘要： Through the EU FP7 Horizon 2020 ECOPOTENTIAL project, a novel and innovative approach to classification has been developed, which is termed the Earth Observation Data for Ecosystem Monitoring (EODESM), and has been built on concepts behind an implementation of the Earth Observation Data for Habitat Monitoring (EODHaM) system generated as part of the EU FP7 BIOSOS project, applied to Very High Resolution (VHR) Worldview data. The EODESM system facilitates routine classification of land covers according to the Food and Agricultural Organisations Land Cover Classification System (FAO-LCCS), translates these to other taxonomies (including General Habitat Classifications; GHCs) and facilitates routine detection of change and the generation of maps indicating the causes and consequences of such change.
关键词： change detection,earth observation,environmental variables,Land cover classification
更新于2025-09-23 15:23:52
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Evaluation on Spaceborne Multispectral Images, Airborne Hyperspectral, and LiDAR Data for Extracting Spatial Distribution and Estimating Aboveground Biomass of Wetland Vegetation Suaeda salsa
摘要： Suaeda salsa (S. salsa) has a significant protective effect on salt marshes in coastal wetlands. In this study, the abilities of airborne multispectral images, spaceborne hyperspectral images, and LiDAR data in spatial distribution extraction and aboveground biomass (AB) estimation of S. salsa were explored for mapping the spatial distribution of S. salsa AB. Results showed that the increasing spectral and structural features were conducive to improving the classification accuracy of wetland vegetation and the AB estimation accuracy of S. salsa. The fusion of hyperspectral and LiDAR data provided the highest accuracies for wetlands classification and AB estimation of S. salsa in the study. Multispectral images alone provided relatively high user's and producer's accuracies of S. salsa classification (87.04% and 88.28%, respectively). Compared to multispectral images, hyperspectral data with more spectral features slightly improved the Kappa coefficient and overall accuracy. The AB estimation reached a relatively reliable accuracy based only on hyperspectral data (R2 of 0.812, root-mean-square error of 0.295, estimation error of 24.56%, residual predictive deviation of 2.033, and the sums of squares ratio of 1.049). The addition of LiDAR data produced a limited improvement in the process of extraction and AB estimation of S. salsa. The spatial distribution of mapped S. salsa AB was consistent with the field survey results. This study provided an important reference for the effective information extraction and AB estimation of wetland vegetation S. salsa.
关键词： multispectral image,Suaeda salsa,LiDAR data,fine classification,Aboveground biomass,hyperspectral image
更新于2025-09-23 15:23:52
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[IEEE 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA) - Rome (2018.6.11-2018.6.13)] 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA) - A Multi-label Architecture for Vision-based Measurement of Intervals of Pain
摘要： Vision-based measurement of pain can provide significant advantage in patient care and cost reduction. The subjective nature of pain, however, poses metrological challenges yet to be addressed. With this work, we designed and validated a measurement architecture for automatically estimating intervals of pain over time based on the analysis of facial expressions. A reference measurement procedure was set up for labelling subjective levels of pain as observed by a set of independent evaluators. By means of a multi-label strategy, the system was calibrated for managing the uncertainty of the information provided by the evaluators at the ground-truth level. Results obtained for different coverage probability values support the efficacy of the proposed platform and motivate further investigations.
关键词： vision-based measurement,pain monitoring,reference measurement procedure,ordinal quantity,Analysis of facial expression,multi-label classification
更新于2025-09-23 15:23:52
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A CNN With Multiscale Convolution and Diversified Metric for Hyperspectral Image Classification
摘要： Recently, researchers have shown the powerful ability of deep methods with multilayers to extract high-level features and to obtain better performance for hyperspectral image classification. However, a common problem of traditional deep models is that the learned deep models might be suboptimal because of the limited number of training samples, especially for the image with large intraclass variance and low interclass variance. In this paper, novel convolutional neural networks (CNNs) with multiscale convolution (MS-CNNs) are proposed to address this problem by extracting deep multiscale features from the hyperspectral image. Moreover, deep metrics usually accompany with MS-CNNs to improve the representational ability for the hyperspectral image. However, the usual metric learning would make the metric parameters in the learned model tend to behave similarly. This similarity leads to obvious model’s redundancy and, thus, shows negative effects on the description ability of the deep metrics. Traditionally, determinantal point process (DPP) priors, which encourage the learned factors to repulse from one another, can be imposed over these factors to diversify them. Taking advantage of both the MS-CNNs and DPP-based diversity-promoting deep metrics, this paper develops a CNN with multiscale convolution and diversified metric to obtain discriminative features for hyperspectral image classification. Experiments are conducted over four real-world hyperspectral image data sets to show the effectiveness and applicability of the proposed method. Experimental results show that our method is better than original deep models and can produce comparable or even better classification performance in different hyperspectral image data sets with respect to spectral and spectral–spatial features.
关键词： deep metric learning,determinantal point process (DPP),image classification,multiscale features,Convolutional neural network (CNN),hyperspectral image
更新于2025-09-23 15:23:52
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Automated Method for Retinal Artery/Vein Separation via Graph Search Metaheuristic Approach
摘要： Separation of the vascular tree into arteries and veins is a fundamental prerequisite in the automatic diagnosis of retinal biomarkers associated with systemic and neurodegenerative diseases. In this paper, we present a novel graph search metaheuristic approach for automatic separation of arteries/veins (A/V) from color fundus images. Our method exploits local information to disentangle the complex vascular tree into multiple subtrees, and global information to label these vessel subtrees into arteries and veins. Given a binary vessel map, a graph representation of the vascular network is constructed representing the topological and spatial connectivity of the vascular structures. Based on the anatomical uniqueness at vessel crossing and branching points, the vascular tree is split into multiple subtrees containing arteries and veins. Finally, the identified vessel subtrees are labeled with A/V based on a set of hand-crafted features trained with random forest classifier. The proposed method has been tested on four different publicly available retinal datasets with an average accuracy of 94.7%, 93.2%, 96.8% and 90.2% across AV-DRIVE, CT-DRIVE, INSPIRE-AVR and WIDE datasets, respectively. These results demonstrate the superiority of our proposed approach in outperforming state-of-the-art methods for A/V separation.
关键词： Graph search,Vessel keypoints,Artery/Vein classification,Retinal image
更新于2025-09-23 15:23:52
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[IEEE 2018 WRC Symposium on Advanced Robotics and Automation (WRC SARA) - Beijing, China (2018.8.16-2018.8.16)] 2018 WRC Symposium on Advanced Robotics and Automation (WRC SARA) - Improved Monocular ORB-SLAM2 Inspired By The Optical Flow With Better Accuracy
摘要： ORB-SLAM2 is currently the best open source SLAM system with high positioning accuracy and map reusability. However, when using a monocular camera in a dynamic environment, the accuracy will be disturbed by the moving objects. Besides, even though there are no moving objects in the frame, there is space for further improvement in accuracy. This article improves the feature point selection based on monocular ORB-SLAM2 system, by creatively using the idea comes from optical flow and then using the K-Means algorithm to classify the matched feature point pairs. The existing open source datasets are used for evaluating the improvement. Under the pre-requirement that the improved system should ensure the real-time performance, the positioning accuracy of the improved system has been significantly improved.
关键词： Accuracy,Feature Point Classification,Optical Flow,K-Means
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 - Environmental Monitoring Using Drone Images and Convolutional Neural Networks
摘要： Recently, drone images have been used in a number of applications, mainly for pollution control and surveillance purposes. In this paper, we introduce the well-known Convolutional Neural Networks in the context of environmental monitoring using drone images, and we show their robustness in real-world images obtained from uncontrolled scenarios. We consider a transfer learning-based approach and compare two neural models, i.e., VGG16 and VGG19, to distinguish four classes: 'water', 'deforesting area', 'forest', and 'buildings'. The results are analyzed by experts in the field and considered pretty much reasonable.
关键词： Land-use classification,Convolutional Neural Networks,Drones
更新于2025-09-23 15:23:52
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[IEEE 2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) - Poznan, Poland (2018.9.19-2018.9.21)] 2018 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA) - On the influence of the image normalization scheme on texture classification accuracy
摘要： Texture can be a very rich source of information about the image. Texture analysis finds applications, among other things, in biomedical imaging. One of the widely used methods of texture analysis is the Gray Level Co-occurrence Matrix (GLCM). Texture analysis using the GLCM method is most often carried out in several stages: determination of areas of interest, normalization, calculation of the GLCM, extraction of features, and finally, the classification. Values of the GLCM based features depend on the choice of the normalization method, which was examined in this work. The normalization is necessary, since acquired images often suffer from noise and intensity artifacts. Certainly, the normalization will not eliminate these two effects, however it was demonstrated, that its application improves texture analysis accuracy. The aim of the work was to analyze the influence of different normalization methods on the discriminating ability of features estimated from the GLCM. The analysis was performed both for Brodatz textures and real magnetic resonance data. Brodatz textures were corrupted by three types of distortion: intensity nonuniformity, Gaussian noise and Rician Noise. Three types of normalizations were tested: min?max, 1?99% and +/?3σ.
关键词： normalization,classification,image processing,texture analysis,GLCM
更新于2025-09-23 15:23:52
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Multiple deep-belief-network-based spectral-spatial classification of hyperspectral images
摘要： A deep-learning-based feature extraction has recently been proposed for HyperSpectral Images (HSI) classification. A Deep Belief Network (DBN), as part of deep learning, has been used in HSI classification for deep and abstract feature extraction. However, DBN has to simultaneously deal with hundreds of features from the HSI hyper-cube, which results into complexity and leads to limited feature abstraction and performance in the presence of limited training data. Moreover, a dimensional-reduction-based solution to this issue results in the loss of valuable spectral information, thereby affecting classification performance. To address the issue, this paper presents a Spectral-Adaptive Segmented DBN (SAS-DBN) for spectral-spatial HSI classification that exploits the deep abstract features by segmenting the original spectral bands into small sets/groups of related spectral bands and processing each group separately by using local DBNs. Furthermore, spatial features are also incorporated by first applying hyper-segmentation on the HSI. These results improved data abstraction with reduced complexity and enhanced the performance of HSI classification. Local application of DBN-based feature extraction to each group of bands reduces the computational complexity and results in better feature extraction improving classification accuracy. In general, exploiting spectral features effectively through a segmented-DBN process and spatial features through hyper-segmentation and integration of spectral and spatial features for HSI classification has a major effect on the performance of HSI classification. Experimental evaluation of the proposed technique on well-known HSI standard data sets with different contexts and resolutions establishes the efficacy of the proposed techniques, wherein the results are comparable to several recently proposed HSI classification techniques.
关键词： hyperspectral image classification,support vector machine,deep belief network,segmentation
更新于2025-09-23 15:23:52
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Multi-target joint detection, tracking and classification based on random finite set for aerospace applications
摘要： Multi-target detection, tracking and classification are important problems in aerospace applications, such as reconnaissance, airborne and spaceborne sensing. These problems are correlated but are difficult to be solved simultaneously, especially for systems with multiple sensors. This paper summarized the existing work for multi-target joint detection, tracking and classification based on labeled random finite set. Furthermore, a new algorithm is proposed for multi-sensor multi-target joint detection, tracking and classification problem. A conditional multi-sensor multi-target state estimator is derived, and the optimal solution is then obtained based on the minimum Bayes risk criterion. The numerical simulations are performed, and the results are shown to be more accurate than that of the approximate solutions based on the unlabeled random finite set. It is observed that the labeled random finite set theory provides a good foundation for a joint solution for multi-target detection, tracking and classification.
关键词： Multiple targets,Tracking and classification,Labeled RFS,Generalized bayesian risk,Sensor registration,Joint detection
更新于2025-09-23 15:23:52