- 标题
- 摘要
- 关键词
- 实验方案
- 产品
过滤筛选
- 2018
- Conditional Random Fields (CRF)
- Convolutional Neural Network (CNN)
- Fine Classification
- Airborne hyperspectral
- Optoelectronic Information Science and Engineering
- Wuhan University
- Central South University
- Hubei University
-
[IEEE 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Honolulu, HI, USA (2018.7.18-2018.7.21)] 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Features Extraction for Cuffless Blood Pressure Estimation by Autoencoder from Photoplethysmography
摘要： Several studies have been proposed to estimate blood pressure (BP) with cuffless devices using only a Photoplethysmograph (PPG) sensor on the basis of the physiological knowledge that the PPG changes depend on the state of the cardiovascular system. In these studies, machine learning algorithms were used to extract various features from the wave height and the elapsed time from the rising point of the pulse wave to feature points have been used to estimate the BP. However, the accuracy is still not adequate to be used as medical equipment because their features cannot express fully information of the pulse waveform which changes according to the BP. And, no other effective knowledge about the pulse waveform for estimating BP has been found yet. Therefore, in this study, we focus on the autoencoder which can extract complex features and can add new features of the pulse waveform for estimating the BP. By using autoencoder, we extracted 100 features from the coupling signal of the pulse wave and from its first-order differentiation and second-order differentiation. The result of examination with 1363 test subjects show that the correlation coefficients and the standard deviation of the difference between the measured BP and the estimated BP got improved from R = 0.67, SD = 13.97 without autoencoder to R = 0.78, SD = 11.86 with autoencoder.
关键词： blood pressure estimation,autoencoder,cuffless devices,neural network,Photoplethysmography
更新于2025-09-09 09:28:46
-
[IEEE 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Chongqing (2018.6.27-2018.6.29)] 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Change Detection Based on the Combination of Improved SegNet Neural Network and Morphology
摘要： Through the analysis of satellite remote sensing image data, the identification of newly added buildings in the same area can be realized to judge the use of land. The identification of newly added buildings based on remote sensing images, involving image object extraction, semantic segmentation and change detection. The difficulty is not only to identify the changes of remote sensing images in different periods, but also to identify the newly added buildings with the original buildings. Both of the recognition effect and the detection precision of the traditional method based on mathematical modeling need to be improved. SegNet neural network is a kind of deep convolution neural network. It shows good performance in dealing with the task of semantic segmentation of single image, but it is directly applied to building change detection with low accuracy. The simulation results show that the improved SegNet neural network method improves the accuracy of the quantitative evaluation index F1 score by 8.6% compared with the conventional SegNet network in the newly added building detection effect in the same area in 2015 and 2017. In addition, the situation that the change detection result will produce a large number of noise, a combination of improved SegNet network and morphological method is adopted to eliminate the noise and reduce the misjudgment. The simulation results show that the F1 index increased further by 1.4% on the basis of 8.6%.
关键词： convolutional neural network,deep learning,remote sensing images,building change detection,morphology
更新于2025-09-09 09:28:46
-
[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - Lightweight Deep Residue Learning for Joint Color Image Demosaicking and Denoising
摘要： Color demosaicking and image denoising each plays an important role in digital cameras. Conventional model-based methods often fail around the areas of strong textures and produce disturbing visual artifacts such as aliasing and zippering. Recently developed deep learning based methods were capable of obtaining images of better qualities though at the price of high computational cost, which make them not suitable for real-time applications. In this paper, we propose a lightweight convolutional neural network for joint demosaicking and denoising (JDD) problem with the following salient features. First, the densely connected network is trained in an end-to-end manner to learn the mapping from the noisy low-resolution space (CFA image) to the clean high-resolution space (color image). Second, the concept of deep residue learning and aggregated residual transformations are extended from image denoising and classification to JDD supporting more efficient training. Third, the design of our end-to-end network architecture is inspired by a rigorous analysis of JDD using sparsity models. Experimental results conducted for both demosaicking-only and JDD tasks have shown that the proposed method performs much better than existing state-of-the-art methods (i.e., higher visual quality, smaller training set and lower computational cost).
关键词： Convolutional neural network,Joint demosaicking and denoising,Residue Learning
更新于2025-09-09 09:28:46
-
[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - A Novel Model for Multi-label Image Annotation
摘要： Multi-label image annotation is one of the most important open problems in machine learning and computer vision. In this paper, we propose a novel model for image annotation. Unlike existing works that usually use conventional visual features to annotate images, this paper adopts features based on convolutional neural network (CNN), which have shown potential to achieve outstanding performance. In particular, we use CNN to extract image features with higher semantic meaning and apply them to the image annotation method – Tag Propagation (TagProp). Experimental results on four challenging datasets indicate that our model makes a marked improvement as compared to the current state-of-the-art.
关键词： image annotation,convolutional neural network,Multi-label learning,Tag Propagation
更新于2025-09-09 09:28:46
-
[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 - Urban Land Use/Land Cover Classification Based on Feature Fusion Fusing Hyperspectral Image and Lidar Data
摘要： Hyperspectral images have been widely used in classification because of the abundant spectral information. But it can’t distinguish the objective with similar spectral character but different elevation. However, LiDAR data can obtain elevation information. Therefore, it will obtain better classification maps if fusing the two data. In recent years, CNN has attracted much attention due to its powerful ability to excavate the potential representation and features of the raw data. However, it’s difficult to distinguish the objects with different spectral information but similar surface character. Unlike CNN features, the traditional manual features, such as the normalized vegetation index (NDVI), have a certain characteristic expression significance. In order to consider both the semantic information of traditional manual features and the advanced features of CNN features, this paper proposes a fusion algorithm of hyperspectral and LiDAR fusion based on feature fusion. The proposed algorithm has achieved a good fusion classification effect on the MUUFL Gulfport Hyperspectral and LiDAR Data set.
关键词： convolutional neural network,land-use/land-cover classification,Hyperspectral,deep learning,feature fusion,LIDAR
更新于2025-09-09 09:28:46
-
[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 - A CNN-Based Fusion Method for Super-Resolution of Sentinel-2 Data
摘要： Sentinel-2 data represent a rich source of information for the community due to the free access and to the temporal-spatial coverage assured. However, some of the spectral bands are sensed at reduced resolution due to a compromise between technological limitations and Copernicus program's objectives. For this reason in this work we present a new super-resolution method based on Convolutional Neural Networks (CNNs) to rise the resolution of the shortwave infra-red (SWIR) band from 20 to 10 meters, that is the highest resolution provided. This is accomplished by fusing the target band with the finer-resolution ones. The proposed solution compares favourably against several alternative methods according to different quality indexes. In addition we have also tested the use of the super-resolved band from an applicative perspective by detecting water basins through the Modified Normalized Difference Water Index (MNDWI).
关键词： convolutional neural network,Deep learning,Sentinel-2,pansharpening,normalized difference water index
更新于2025-09-09 09:28:46
-
[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 - Feature Learning For SAR Images Using Convolutional Neural Network
摘要： Convolutional neural network (CNN) has been widely used in many research areas due to its powerful ability of feature learning. In this paper, the powerful ability of feature learning in CNN is explored by constructing a novel convolutional network (ConvNet) for SAR image processing. The proposed ConvNet is firstly trained under classification task, in which effective features can be learned automatically from the training data. Specifically, data argument is adopted to overcome the small-sample-problem in SAR images. When well-trained, the proposed ConvNet can be directly used for feature extraction of other images, even though their classes may be not used in the training. Experimental results on benchmark MSTAR dataset demonstrate that the proposed ConvNet is effective for classification of SAR images, and the features learned from it are more effective than traditional hand-crafted features in SAR image processing.
关键词： Synthetic Aperture Radar (SAR),feature learning,convolutional neural network (CNN),feature extraction,classification
更新于2025-09-09 09:28:46
-
[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 - Polarimetric SAR Terrain Classification Using 3D Convolutional Neural Network
摘要： Terrain classification is an important application of polarimetric SAR (PolSAR) data. Traditional classification methods need to extract the feature and then classify by classifiers. Besides, it should consider the influence of speckle noise. As a new method for image processing, convolutional neural network (CNN) has attracted more and more attention because of its good performance in image processing. It can deal with the original image directly with a higher classification accuracy without considering the impact of speckle noise. Moreover, three-dimensional convolutional neural network (3D CNN) has stronger feature extraction capability compared with traditional two-dimensional convolutional neural network (2D CNN). In this paper, the application of 3D CNN in terrain classification is studied, in which a new convolutional neural network architecture is designed and the elements of polarimetric coherency matrix are used as the input data of this network. The experiments of two real PolSAR data are conducted to verify the performance of the proposed network.
关键词： terrain classification,three-dimensional convolutional neural network (3D CNN),polarmetric SAR
更新于2025-09-09 09:28:46
-
[IEEE 2018 9th International Conference on Ultrawideband and Ultrashort Impulse Signals (UWBUSIS) - Odessa (2018.9.4-2018.9.7)] 2018 9th International Conference on Ultrawideband and Ultrashort Impulse Signals (UWBUSIS) - Application of UWB Electromagnetic Waves for Subsurface Object Location Classification by Artificial Neural Networks
摘要： The problem of determination of object position in a plane is solved by the analysis of ultrawideband electromagnetic wave reflected from the subsurface object. The model of ground containing perfectly conducting object inside is irradiated by short impulse wave with Gaussian time dependence. The direct problem is solved by FDTD method to receive a time dependence of reflected wave amplitude. To recognize the presence of the object and depths of its position the multilayer artificial neural networks (ANN) is used. The amplitudes of electric component of the reflected field in different time and special points above the ground surface are the input data for multilayer ANN of different structures. The work of the trained ANN is verified for arbitrary depths of object position.
关键词： artificial neural network,object recognition,subsurface radar,impulse electromagnetic wave
更新于2025-09-09 09:28:46
-
[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 - High Quality Remote Sensing Image Super-Resolution Using Deep Memory Connected Network
摘要： Single image super-resolution is an effective way to enhance the spatial resolution of remote sensing image, which is crucial for many applications such as target detection and image classification. However, existing methods based on the neural network usually have small receptive fields and ignore the image detail. We propose a novel method named deep memory connected network (DMCN) based on a convolutional neural network to reconstruct high-quality super-resolution images. We build local and global memory connections to combine image detail with environmental information. To further reduce parameters and ease time-consuming, we propose down-sampling units, shrinking the spatial size of feature maps. We test DMCN on three remote sensing datasets with different spatial resolution. Experimental results indicate that our method yields promising improvements in both accuracy and visual performance over the current state-of-the-art.
关键词： convolutional neural network,image fusion,super-resolution,remote sensing image
更新于2025-09-09 09:28:46