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- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) - Chengdu, China (2018.7.15-2018.7.18)] 2018 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR) - Spectral-Spatial Graph Convolutional Networks for Semel-Supervised Hyperspectral Image Classification
摘要： Collecting label samples is quite costly and time consuming for hyperspectral image (HSI) classification tasks. Semi-supervised learning framework, which combines the intrinsic information of labeled and unlabeled samples can alleviate the deficient labeled samples and increase the accuracy of HSI classification. In this paper, we propose a novel framework for semi-supervised learning on multiple spectral-spatial graphs that is based on graph convolutional networks (SGCN). In the filtering operation on graphs we consider the spatial information and spectral signatures of HSI simultaneously. The experimental results on three real-life HSI data sets, i.e. Botswana Hyperion, Kennedy Space Center, and Indian Pines, show that the proposed SGCN can significantly improve the classification accuracy. For instance, the over accuracy on Indian Pine data is increased from 78% to 93%.
关键词： Hyperspectral image classification,Graph fourier transform,Graph convolutional,Neural networks,Semi-supervised learning
更新于2025-09-23 15:21:01
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Detecting Building Changes between Airborne Laser Scanning and Photogrammetric Data
摘要： Detecting topographic changes in an urban environment and keeping city-level point clouds up-to-date are important tasks for urban planning and monitoring. In practice, remote sensing data are often available only in different modalities for two epochs. Change detection between airborne laser scanning data and photogrammetric data is challenging due to the multi-modality of the input data and dense matching errors. This paper proposes a method to detect building changes between multimodal acquisitions. The multimodal inputs are converted and fed into a light-weighted pseudo-Siamese convolutional neural network (PSI-CNN) for change detection. Different network configurations and fusion strategies are compared. Our experiments on a large urban data set demonstrate the effectiveness of the proposed method. Our change map achieves a recall rate of 86.17%, a precision rate of 68.16%, and an F1-score of 76.13%. The comparison between Siamese architecture and feed-forward architecture brings many interesting findings and suggestions to the design of networks for multimodal data processing.
关键词： convolutional neural networks,change detection,dense image matching,airborne laser scanning,Siamese networks,multimodal data
更新于2025-09-23 15:19:57
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[IEEE 2019 PhotonIcs & Electromagnetics Research Symposium - Spring (PIERS-Spring) - Rome, Italy (2019.6.17-2019.6.20)] 2019 PhotonIcs & Electromagnetics Research Symposium - Spring (PIERS-Spring) - Functionalized Materials for Integrated Photonics: Hybrid Integration of Organic Materials in Silicon- based Photonic Integrated Circuits for Advanced Optical Modulators and Light-sources
摘要： It is of significant importance for any classification and recognition system, which claims near or better than human performance to be immune to small perturbations in the dataset. Researchers found out that neural networks are not very robust to small perturbations and can easily be fooled to persistently misclassify by adding a particular class of noise in the test data. This, so-called adversarial noise severely deteriorates the performance of neural networks, which otherwise perform really well on unperturbed dataset. It has been recently proposed that neural networks can be made robust against adversarial noise by training them using the data corrupted with adversarial noise itself. Following this approach, in this paper, we propose a new mechanism to generate a powerful adversarial noise model based on K-support norm to train neural networks. We tested our approach on two benchmark datasets, namely the MNIST and STL-10, using muti-layer perceptron and convolutional neural networks. Experimental results demonstrate that neural networks trained with the proposed technique show significant improvement in robustness as compared to state-of-the-art techniques.
关键词： robustness,generalization,convolutional neural networks,adversarial,K-Support norm
更新于2025-09-23 15:19:57
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Implementation of deep convolutional neural network for classification of multiscaled and multiangled remote sensing scene
摘要： With the evolution of convolutional neural networks, extraction of deep features for accurate classification of Remote Sensed (RS) images have gained lot of momentum. However, due to variation in the scale of high resolution remote sensed images, accurate classification still remains a challenging task. Moreover, along with the scale, variation in the angle also decreases the accuracy of extraction of deep features using convolutional neural network. In this paper, a Multiscale and Multiangle convolutional neural network (MSMA-CNN) is proposed which extracts deep features of the RS images by employing several convolutional, pooling and fully connected layers which are discriminant, nonlinear and invariant. In MSMA-CNN, along with the spatial features, spectral features are also considered for classification of remote sensing scenes thus, making the entire system robust. The RS images are scaled at different levels using Gaussian Pyramid Decomposition and rotated at different angles and further features are derived using maximally stable extremal regions (MSER) at spectral and spatial level which are further concatenated and fed to the MSMA-CNN. A regularization parameter is added to get the results for test images as close as the trained images. A hybrid MSMA-CNN structure is designed by altering various parameters of the CNN structure to get improved optimized performance. To demonstrate the effectiveness of the proposed method, we compared the results on six challenging high-resolution remote sensing datasets and achieve a classification accuracy of 92.25% which shows significant improvement compared to the other state-of-the-art scene classification methods in terms classifcational accuracy and computational cost.
关键词： Deep feature extraction,classification of remote sensed images,multiscale and multiangle remote sensed images,convolutional neural networks
更新于2025-09-23 15:19:57
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Micro-cracks detection of solar cells surface via combining short-term and long-term deep features
摘要： The machine vision based methods for micro-cracks detection of solar cells surface have become one of the main research directions with its efficiency and convenience. The existed methods are roughly classified into two categories: current viewing information based methods, prior knowledge based methods, however, the former usually adopt hand-designed features with poor generality and lacks the guidance of prior knowledge, the latter are usually implemented through the machine learning, and the generalization ability is also limited since the large-scale annotation dataset is scarce. To resolve above problems, a novel micro-cracks detection method via combining short-term and long-term deep features is proposed in this paper. The short-term deep features which represent the current viewing information are learned from the input image itself through stacked denoising auto encoder (SDAE), the long-term deep features which represent the prior knowledge are learned from a large number of natural scene images that people often see through convolutional neural networks (CNNs). The subjective and objective evaluations demonstrate that: 1) the performance of combing the short-term and long-term deep features is better than any of them alone, 2) the performance of proposed method is superior to the shallow learning based methods, 3) the proposed method can effectively detect various kinds of micro-cracks.
关键词： solar cell,stacked denoising auto encoder,long-term,convolutional neural networks,short-term,micro-cracks detection
更新于2025-09-23 15:19:57
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A High-Performance Deep Learning Algorithm for the Automated Optical Inspection of Laser Welding
摘要： The battery industry has been growing fast because of strong demand from electric vehicle and power storage applications.Laser welding is a key process in battery manufacturing. To control the production quality, the industry has a great desire for defect inspection of automated laser welding. Recently, Convolutional Neural Networks (CNNs) have been applied with great success for detection, recognition, and classi?cation. In this paper, using transfer learning theory and pre-training approach in Visual Geometry Group (VGG) model, we proposed the optimized VGG model to improve the e?ciency of defect classi?cation. Our model was applied on an industrial computer with images taken from a battery manufacturing production line and achieved a testing accuracy of 99.87%. The main contributions of this study are as follows: (1) Proved that the optimized VGG model, which was trained on a large image database, can be used for the defect classi?cation of laser welding. (2) Demonstrated that the pre-trained VGG model has small model size, lower fault positive rate, shorter training time, and prediction time; so, it is more suitable for quality inspection in an industrial environment. Additionally, we visualized the convolutional layer and max-pooling layer to make it easy to view and optimize the model.
关键词： defect classi?cation,optimized VGG model,laser welding,convolutional neural networks (CNNs),automatic optical inspection
更新于2025-09-23 15:19:57
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Multi-Pooled Inception Features for No-Reference Image Quality Assessment
摘要： Image quality assessment (IQA) is an important element of a broad spectrum of applications ranging from automatic video streaming to display technology. Furthermore, the measurement of image quality requires a balanced investigation of image content and features. Our proposed approach extracts visual features by attaching global average pooling (GAP) layers to multiple Inception modules of on an ImageNet database pretrained convolutional neural network (CNN). In contrast to previous methods, we do not take patches from the input image. Instead, the input image is treated as a whole and is run through a pretrained CNN body to extract resolution-independent, multi-level deep features. As a consequence, our method can be easily generalized to any input image size and pretrained CNNs. Thus, we present a detailed parameter study with respect to the CNN base architectures and the effectiveness of different deep features. We demonstrate that our best proposal—called MultiGAP-NRIQA—is able to outperform the state-of-the-art on three benchmark IQA databases. Furthermore, these results were also confirmed in a cross database test using the LIVE In the Wild Image Quality Challenge database.
关键词： deep learning,no-reference image quality assessment,convolutional neural networks
更新于2025-09-23 15:19:57
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Study of Room Temperature Photoluminescence For 1-stage Co-Evaporated Ultra-Thin Cu(In,Ga)Se <sub/>2</sub> Solar Cells
摘要： In this paper, we investigate the feasibility of recognizing human hand gestures using micro-Doppler signatures measured by Doppler radar with a deep convolutional neural network (DCNN). Hand gesture recognition using radar can be applied to control electronic appliances. Compared with an optical recognition system, radar can work regardless of light conditions and it can be embedded in a case. We classify ten different hand gestures, with only micro-Doppler signatures on spectrograms without range information. The ten gestures, which included swiping from left to right, swiping from right to left, rotating clockwise, rotating counterclockwise, pushing, double pushing, holding, and double holding, were measured using Doppler radar and their spectrograms investigated. A DCNN was employed to classify the spectrograms, with 90% of the data utilized for training and the remaining 10% for validation. After ?ve-fold validation, the classi?cation accuracy of the proposed method was found to be 85.6%. With seven gestures, the accuracy increased to 93.1%.
关键词： Doppler radar,micro-Doppler signatures,Hand gesture,deep convolutional neural networks
更新于2025-09-23 15:19:57
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Identification and diagnosis of whole body and fragments of Trogoderma granarium and Trogoderma variabile using visible near infrared hyperspectral imaging technique coupled with deep learning
摘要： The khapra beetle, Trogoderma granarium Everts, is the most critical biosecurity pest threat which threatens the grains industry worldwide. To prevent incursion of the khapra beetle, very accurate and reliable diagnostic tools are required to differentiate the khapra beetle from other morphologically, closely related Trogoderma sp., in particular the larva stage. However, at present, it can only be identified by highly skilled taxonomists. Furthermore, often suspected Trogoderma sp. found in grain products are the body fractions such as larval skins or fragmented adult, which are impossible to diagnose morphologically. This work explored the combination of visible near infrared hyperspectroscopy (VNIH) and deep learning tools to identify the khapra beetle. About 2000 hyperspectral images were acquired under this study. Images of T. granarium and Trogoderma variabile, adult, larvae, larvae skin, fragments of adult and larvae images, were subjected to two deep learning models; Convolutional Neural Networks (CNN) and Capsule Network for analysis. Overall, above 90% accuracy was obtained with both models, whereas Capsule Network achieved a higher accuracy of 96%. For whole adult body and adult fragments, the accuracy achieved was 96.2% and 91.7%, respectively. For whole larvae, larvae skin and larvae fragment, accuracies of 93.4%, 91.6%, and 90.3% were achieved. Ventral orientation gave better accuracy over dorsal orientation of the insects for both larvae and adult stages. Based on the above results, VNIH imaging technology coupled with appropriate machine learning tools can be used to identify one of the most notorious stored grain pests, the khapra beetle, from other morphologically similar Trogoderma sp like T. variabile. Particularly, the technology offers a new approach and possibility of an effective identification of Trogoderma sp. from its body fragments and larvae skins, which are otherwise impossible to diagnose taxonomically.
关键词： Deep learning,Visible near infrared hyperspectroscopy (VNIH),Trogoderma diagnostic,Capsule network,Convolutional neural networks
更新于2025-09-23 15:19:57
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A deep learning-based model for defect detection in laser-powder bed fusion using in-situ thermographic monitoring
摘要： Additive manufacturing of metal components with laser-powder bed fusion is a very complex process, since powder has to be melted and cooled in each layer to produce a part. Many parameters influence the printing process; however, defects resulting from suboptimal parameter settings are usually detected after the process. To detect these defects during the printing, different process monitoring techniques such as melt pool monitoring or off-axis infrared monitoring have been proposed. In this work, we used a combination of thermographic off-axis imaging as data source and deep learning-based neural network architectures, to detect printing defects. For the network training, a k-fold cross validation and a hold-out cross validation were used. With these techniques, defects such as delamination and splatter can be recognized with an accuracy of 96.80%. In addition, the model was evaluated with computing class activation heatmaps. The architecture is very small and has low computing costs, which means that it is suitable to operate in real time even on less powerful hardware.
关键词： Additive manufacturing,Convolutional neural networks,Machine learning,Quality assurance
更新于2025-09-23 15:19:57