- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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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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Faulty elements diagnosis of phased array antennas using a generative adversarial learning-based stacked denoising sparse autoencoder
摘要： Diagnosis of faulty elements in a linear phased array antenna is of great importance in the wireless communication field which has been received increasing attention. As a result of element or elements failure in the linear phased array antennas, the whole radiation pattern will suffer from high side lobe levels, wide bandwidth and unexpected nulls. To this end, we suggest a novel approach by combining the generative adversarial learning and the stacked denoising sparse autoencoder to determine the location of the faulty elements in antennas. The suggested approach can learn discriminative features from radiation pattern images adaptively and automatically with less expert knowledge. Meanwhile, the suggested approach is able to overcome the strong noise, the high dimensional size of the radiation pattern and the small fault samples. In this regard, the suggested approach possesses superiority in discriminant capability in contrast to the existing related approaches.
关键词： stacked denoising sparse autoencoder,phased array antennas,Faulty elements diagnosis,generative adversarial learning
更新于2025-09-23 15:22:29
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Ultrafast UV AlGaN Metala??Semiconductora??Metal Photodetector With a Response Time Below 25 ps
摘要： Land-use (LU) scene classification is one of the most challenging tasks in the field of remote sensing (RS) image processing due to its high intraclass variability and low interclass distance. Motivated by the challenge posed by this problem, we propose a novel hybrid architecture, deep filter banks, combining multicolumn stacked denoising sparse autoencoder (SDSAE) and Fisher vector (FV) to automatically learn the representative and discriminative features in a hierarchical manner for LU scene classification. SDSAE kernels describe local patches and a robust global feature of the RS image is built through the FV pooling layer. Unlike previous handcrafted features, we use machine-learning mechanisms to optimize our proposed feature extractor so that it can learn more suitable internal features from the RS data, boosting the final performance. Our approach achieves superior performance compared with the state-of-the-art methods, obtaining average classification accuracies of 92.7% and 90.4%, respectively, on the UC Merced and RSSCN7 data sets.
关键词： Fisher vector (FV),land-use (LU) scene classification,Deep filter banks,stacked denoising sparse autoencoder (SDSAE)
更新于2025-09-23 15:21:01
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A novel spectral-spatial classification technique for multispectral images using extended multi-attribute profiles and sparse autoencoder
摘要： Image classification is a prominent topic and a challenging task in the field of remote sensing. Recently many various classification methods have been proposed for satellite images specifically the frameworks based on spectral-spatial feature extraction techniques. In this paper, a feature extraction strategy of multispectral data is taken into account in order to develop a new classification framework by combining Extended Multi-Attribute Profiles (EMAP) and Sparse Autoencoder (SAE). Extended Multi-Attribute Profiles is employed to extract the spatial information, then it is joined to the original spectral information to describe the spectral-spatial property of the multispectral images. The obtained features are fed into a Sparse Autoencoder as input. Finally, the learned spectral-spatial features are embedded into the Support Vector Machine (SVM) for classification. Experiments are conducted on two multispectral (MS) images such as we construct the ground truth maps of the corresponding images. Our approach based on EMAP and deep learning (DL), proves its huge potential to achieve a high classification accuracy in reasonable running time and outperforms traditional classifiers and others classification approaches.
关键词： Remote sensing,image classification,Extended Multi-Attribute Profiles,spectral-spatial feature extraction,Sparse Autoencoder,Support Vector Machine
更新于2025-09-23 15:21:01
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[Lecture Notes in Computer Science] Neural Information Processing Volume 11306 (25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13–16, 2018, Proceedings, Part VI) || Fast Image Recognition with Gabor Filter and Pseudoinverse Learning AutoEncoders
摘要： Deep neural network has been successfully used in various ?elds, and it has received signi?cant results in some typical tasks, especially in computer vision. However, deep neural network are usually trained by using gradient descent based algorithm, which results in gradient vanishing and gradient explosion problems. And it requires expert level professional knowledge to design the structure of the deep neural network and ?nd the optimal hyper parameters for a given task. Consequently, training a deep neural network becomes a very time consuming problem. To overcome the shortcomings mentioned above, we present a model which combining Gabor ?lter and pseudoinverse learning autoencoders. The method referred in model optimization is a non-gradient descent algorithm. Besides, we presented the empirical formula to set the number of hidden neurons and the number of hidden layers in the entire training process. The experimental results show that our model is better than existing benchmark methods in speed, at same time it has the comparative recognition accuracy also.
关键词： Pseudoinverse learning autoencoder,Gabor ?lter,Handcraft feature,Image recognition
更新于2025-09-23 15:19:57
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[IEEE 2019 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia) - Bangkok, Thailand (2019.6.12-2019.6.14)] 2019 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia) - A.I. in Laser Diode Module Manufacturing
摘要： This paper describes application of Artificial Intelligence using machine learning and deep learning at our laser diode module manufacturing facility. Implementing A.I. into data analysis and classification problems, various benefits such as quality control, human work reduction and efficient usage of big data have been obtained.
关键词： machine learning,autoencoder,Scikit learn,laser diode module,convolutional neural network
更新于2025-09-19 17:13:59
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Knowledge Discovery in Nanophotonics Using Geometric Deep Learning
摘要： We present here a distinctive approach for using the intelligence aspects of artificial intelligence for knowledge discovery rather than the conventional task of device optimization in electromagnetic (EM) nanostructures. This approach uses training data obtained through full-wave EM simulations of a series of nanostructures to train geometric deep learning algorithms to assess the range of feasible responses as well as the feasibility of a desired response from a class of nanophotonic structures. To facilitate the knowledge discovery and reduce the computation complexity, our approach combines the dimensionality reduction technique (using an autoencoder) with convex-hull and one-class support-vector-machine (SVM) algorithms to find the range of the feasible responses in the latent (or the reduced) response space of the EM nanostructure. We show that by using a small set of training instances (compared to all possible structures), our approach can provide better than 95% accuracy in assessing the feasibility of a given response. More importantly, the one-class SVM algorithm can be trained to provide the degree of feasibility (or unfeasibility) of a response from a given nanostructure. This important information can be used to modify the initial structure to an alternative one that can enable an initially unfeasible response. To show the applicability of our approach, we apply it to two important classes of binary metasurfaces (MSs), formed by an array of plasmonic nanostructures, and periodic MSs formed by an array of dielectric nanopillars. In addition to theoretical results, we show the experimental results obtained by fabricating several MSs of the second class. Our theoretical and experimental results confirm the unique features of this approach for knowledge discovery in nanophotonics applications.
关键词： convex-hull,one-class SVM,geometric deep learning,knowledge discovery,nanophotonics,autoencoder,electromagnetic nanostructures
更新于2025-09-12 10:27:22
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[Lecture Notes in Computer Science] Advances in Visual Computing Volume 11241 (13th International Symposium, ISVC 2018, Las Vegas, NV, USA, November 19 – 21, 2018, Proceedings) || Road User Abnormal Trajectory Detection Using a Deep Autoencoder
摘要： In this paper, we focus on the development of a method that detects abnormal trajectories of road users at tra?c intersections. The main di?culty with this is the fact that there are very few abnormal data and the normal ones are insu?cient for the training of any kinds of machine learning model. To tackle these problems, we proposed the solution of using a deep autoencoder network trained solely through augmented data considered as normal. By generating arti?cial abnormal trajectories, our method is tested on four di?erent outdoor urban users scenes and performs better compared to some classical outlier detection methods.
关键词： Deep autoencoder,Data augmentation,Abnormal trajectory detection,Unsupervised learning
更新于2025-09-10 09:29:36
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Active Transfer Learning Network: A Unified Deep Joint Spectral-Spatial Feature Learning Model for Hyperspectral Image Classification
摘要： Deep learning has recently attracted significant attention in the field of hyperspectral images (HSIs) classification. However, the construction of an efficient deep neural network mostly relies on a large number of labeled samples being available. To address this problem, this paper proposes a unified deep network, combined with active transfer learning (TL) that can be well-trained for HSIs classification using only minimally labeled training data. More specifically, deep joint spectral–spatial feature is first extracted through hierarchical stacked sparse autoencoder (SSAE) networks. Active TL is then exploited to transfer the pretrained SSAE network and the limited training samples from the source domain to the target domain, where the SSAE network is subsequently fine-tuned using the limited labeled samples selected from both source and target domains by the corresponding active learning (AL) strategies. The advantages of our proposed method are threefold: 1) the network can be effectively trained using only limited labeled samples with the help of novel AL strategies; 2) the network is flexible and scalable enough to function across various transfer situations, including cross data set and intraimage; and 3) the learned deep joint spectral–spatial feature representation is more generic and robust than many joint spectral–spatial feature representations. Extensive comparative evaluations demonstrate that our proposed method significantly outperforms many state-of-the-art approaches, including both traditional and deep network-based methods, on three popular data sets.
关键词： multiple-feature representation,transfer learning (TL),hyperspectral image (HSI) classification,deep learning,Active learning (AL),stacked sparse autoencoder (SSAE)
更新于2025-09-10 09:29:36
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[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 - Stacked Autoencoders for Multiclass Change Detection in Hyperspectral Images
摘要： Change detection (CD) in multitemporal datasets is a key task in remote sensing. In this paper, a scheme to perform multi-class CD for remote sensing hyperspectral datasets extracting features by means of Stacked Autoencoders (SAEs) is introduced. The scheme combines multiclass and binary CD to obtain an accurate multiclass change map. The multiclass CD begins with the fusion of the multitemporal data followed by Feature Extraction (FE) by SAEs. The binary CD is based on the spectral information by calculating pixel-wise distances and thresholding, and it also incorporates spatial information through watershed segmentation. The processed image is filtered by using the binary CD map and later classified by a Support Vector Machine or an Extreme Learning Machine algorithm. The scheme was evaluated over a multitemporal hyperspectral dataset obtained from the Hyperion sensor. Experimental results show the effectiveness of the proposed scheme using a SAE for extracting the relevant features of the fused information when compared to other published FE methods.
关键词： Change Detection,Stacked Autoencoder,Feature Extraction,Hyperspectral,Support Vector Machine,Extreme Learning Machine
更新于2025-09-10 09:29:36