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- 2019
- 2018
- pattern recognition
- image
- partial discharge
- convolutional neural network(CNN)
- Conditional Random Fields (CRF)
- Convolutional Neural Network (CNN)
- Fine Classification
- Airborne hyperspectral
- green tide
- Elegant End-to-End Fully Convolutional Network (E3FCN)
- Optoelectronic Information Science and Engineering
- Shanghai Jiao Tong University
- Ocean University of China
- University of Oulu
- Wuhan University
- Central South University
- Hubei University
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Forecasting Solar Power Using Long-Short Term Memory and Convolutional Neural Networks
摘要： As solar photovoltaic (PV) generation becomes cost-effective, solar power comes into its own as the alternative energy with the potential to make up a larger share of growing energy needs. Consequently, operations and maintenance cost now have a large impact on the profit of managing power modules, and the energy market participants need to estimate the solar power in short or long terms of future. In this paper, we propose a solar power forecasting technique by utilizing convolutional neural networks and long–short-term memory networks recently developed for analyzing time series data in the deep learning communities. Considering that weather information may not be always available for the location where PV modules are installed and sensors are often damaged, we empirically confirm that the proposed method predicts the solar power well with roughly estimated weather data obtained from national weather centers as well as it works robustly without sophisticatedly preprocessed input to remove outliers.
关键词： convolutional neural networks,deep learning,long-short term memory,Solar power forecasting
更新于2025-09-23 15:22:29
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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) - Learning Illuminant Estimation from Object Recognition
摘要： In this paper we present a deep learning method to estimate the illuminant of an image. Our model is not trained with illuminant annotations, but with the objective of improving performance on an auxiliary task such as object recognition. To the best of our knowledge, this is the first example of a deep learning architecture for illuminant estimation that is trained without ground truth illuminants. We evaluate our solution on standard datasets for color constancy, and compare it with state of the art methods. Our proposal is shown to outperform most deep learning methods in a cross-dataset evaluation setup, and to present competitive results in a comparison with parametric solutions.
关键词： Illuminant estimation,deep learning,convolutional neural networks,computational color constancy,semi-supervised learning
更新于2025-09-23 15:22:29
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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 - An Approach for Road Material Identification By Dual-Stage Convolutional Networks
摘要： The automatic extraction of road network information from satellite images is a meaningful and challenging task. Particularly, the analysis of road surface materials is very important during transport construction and maintenance. This paper proposes a method to extract road area and identify its corresponding materials. The approach is based on two different convolutional neural network structures. Firstly, we use encoder-decoder symmetric network structure to extract the candidate road area. Then the former outputs is processed by atrous convolutional network with very deep layers, in order to classify the covered substances through their representative spectral features. We also utilize the physical characteristics of road network to design morphology approach to enhance the completeness and formation of the road network. Experiential results on various satellite images show that the method can yields better accuracy and adaptability than other convolutional network based methods.
关键词： road region extraction,convolutional networks,image segmentation,Remote sensing,road material classification
更新于2025-09-23 15:22:29
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A Sample Update-Based Convolutional Neural Network Framework for Object Detection in Large-Area Remote Sensing Images
摘要： This letter addresses the issue of accurate object detection in large-area remote sensing images. Although many convolutional neural network (CNN)-based object detection models can achieve high accuracy in small image patches, the models perform poorly in large-area images due to the large quantity of false and missing detections that arise from complex backgrounds and diverse groundcover types. To address this challenge, this letter proposes a sample update-based CNN (SUCNN) framework for object detection in large-area remote sensing images. The proposed framework contains two stages. In the first stage, a base model—single-shot multibox detector—is trained with the training data set. In the second stage, artificial composite samples are generated to update the training set. The parameters of the first-stage model are fine-tuned with the updated data set to obtain the second-stage model. The first- and second-stage models are evaluated using the large-area remote sensing image test set. Comparison experiments show the effectiveness and superiority of the proposed SUCNN framework for object detection in large-area remote sensing images.
关键词： large-area remote sensing images,sample update,object detection,Convolutional neural networks (CNNs)
更新于2025-09-23 15:22:29
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Hyperspectral Coastal Wetland Classification Based on a Multiobject Convolutional Neural Network Model and Decision Fusion
摘要： The phenomenon of spectral aliasing exists for coastal wetland object types, which leads to class mixing. This letter proposes a multiobject convolutional neural network (CNN) decision fusion classification method for hyperspectral images of coastal wetlands. This method adopts decision fusion based on fuzzy membership rules applied to single-object CNN classification to obtain higher classification accuracy. Experimental results demonstrate the effectiveness of the proposed method for the six object types, including water, tidal flat, reed, and other vegetation types. The overall accuracy of the decision fusion classification method based on fuzzy membership is 82.11%, which is 3.33% and 6.24% higher than those of single-object feature band CNN and support vector machine methods. The classification method based on multiobject CNN decision fusion inherits the characteristics of single-object feature bands of the CNN, making it a practical approach to image classification under the challenging conditions in which class mixing occurs.
关键词： decision fusion,convolutional neural network (CNN),hyperspectral image,Classification
更新于2025-09-23 15:22:29
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A Pipeline Neural Network For Low-Light Image Enhancement
摘要： Low-light image enhancement is an important challenge in computer vision. Most of low-light images taken in low-light conditions usually look noisy and dark, which makes it more difficult for subsequent computer vision tasks. In this paper, inspired by multi-scale retinex, we present a low-light image enhancement pipeline network based on an end-to-end fully convolutional networks and discrete wavelet transformation (DWT). Firstly, we show that Multi Scale Retinex (MSR) can be considered as a convolutional neural network (CNN) with Gaussian convolution kernel and blending the result of DWT can improve the image produced by MSR. Secondly, we propose our pipeline neural network, consisting of denoising net and low light image enhancement net (LLIE-net) which learns a function from a pair of dark and bright images. Finally, we evaluate our method both in synthetic dataset and public dataset. Experiments reveal that in comparison with other state-of-the-art methods, our methods achieve better performance in the perspective of qualitative and quantitative analysis.
关键词： Convolutional Neural Network,LLIE-Net,Low-light image enhancement
更新于2025-09-23 15:22:29
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Ship Classification in High-Resolution SAR Images via Transfer Learning with Small Training Dataset
摘要： Synthetic aperture radar (SAR) as an all-weather method of the remote sensing, now it has been an important tool in oceanographic observations, object tracking, etc. Due to advances in neural networks (NN), researchers started to study SAR ship classification problems with deep learning (DL) in recent years. However, the limited labeled SAR ship data become a bottleneck to train a neural network. In this paper, convolutional neural networks (CNNs) are applied to ship classification by using SAR images with the small datasets. To solve the problem of over-fitting which often appeared in training small dataset, we proposed a new method of data augmentation and combined it with transfer learning. Based on experiments and tests, the performance is evaluated. The results show that the types of the ships can be classified in high accuracies and reveal the effectiveness of our proposed method.
关键词： ship classification,deep learning (DL),convolutional neural networks (CNNs),synthetic aperture radar (SAR)
更新于2025-09-23 15:22:29
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An Attribute-based High-level Image Representation for Scene Classification
摘要： Scene classification is increasingly popular due to its extensive usage in many real-world applications such as object detection, image retrieval, and so on. Traditionally, the low-level hand-crafted image representations are adopted to describe the scene images. However, they usually fail to detect semantic features of visual concepts, especially in handling complex scenes. In this paper, we propose a novel high-level image representation which utilizes image attributes as features for scene classification. More specifically, the attributes of each image are firstly extracted by a deep convolution neural network (CNN), which is trained to be a multi-label classifier by minimizing an element-wise logistic loss function. The process of generating attributes can reduce the 'semantic gap' between the low-level feature representation and the high level scene meaning. Based on the attributes, we then build a system to discover semantically meaningful descriptions of the scene classes. Extensive experiments on four large-scale scene classification datasets show that our proposed algorithm considerably outperforms other state-of-the-art methods.
关键词： high-level image representation,Scene classification,attribute representation,convolutional neural network
更新于2025-09-23 15:22:29
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Semi-supervised Automatic Segmentation of Layer and fluid region in Retinal Optical Coherence Tomography Images Using Adversarial Learning
摘要： Optical coherence tomography (OCT) is a primary imaging technique for ophthalmic diagnosis due to its advantages in high resolution and non-invasiveness. Diabetes is a chronic disease, which could cause retinal layer deformation and fluid accumulation. It might increase the risk of blindness, and thus, it is important to monitor the morphology change of the retinal layer and fluid accumulation for diabetes patients. Due to the existence of deformation and fluid accumulation, the retinal layer and fluid region segmentation in the OCT image is a challenging task. Machine learning-based segmentation methods have been proposed, but they depend on a significant number of pixel-level annotated data, which is often unavailable. In this paper, we proposed a new semi-supervised fully convolutional deep learning method for segmenting retinal layers and fluid regions in retinal OCT B-scans. The proposed semi-supervised method leverages the unlabeled data through an adversarial learning strategy. The segmentation method includes a segmentation network and a discriminator network, and both the networks are with U-Net alike fully convolutional architecture. The objective function of the segmentation network is a joint loss function, including multi-class cross entropy loss, dice overlap loss, adversarial loss, and semi-supervised loss. We show that the discriminator network and the use of unlabeled data can improve the performance of segmentation. The proposed method is investigated on the duke Diabetic Macular Edema dataset and the POne dataset, and the experiment results demonstrate that our method is more effective than the other state-of-the-art methods for layers and fluid segmentation in the OCT images.
关键词： image processing,optical coherence tomography,layer segmentation,Adversarial learning,convolutional neural networks
更新于2025-09-23 15:22:29
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[IEEE 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) - Xi'an, China (2018.11.7-2018.11.10)] 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) - Spontaneous Facial Micro-expression Recognition via Deep Convolutional Network
摘要： The automatic recognition of spontaneous facial micro-expressions becomes prevalent as it reveals the actual emotion of humans. However, handcrafted features employed for recognizing micro-expressions are designed for general applications and thus cannot well capture the subtle facial deformations of micro-expressions. To address this problem, we propose an end-to-end deep learning framework to suit the particular needs of micro-expression recognition (MER). In the deep model, recurrent convolutional networks are utilized to learn the representation of subtle changes from image sequences. To guarantee the learning of deep model, we present a temporal jittering procedure to greatly enrich the training samples. Through performing the experiments on three spontaneous micro-expression datasets, i.e., SMIC, CASME, and CASME2, we verify the effectiveness of our proposed MER approach.
关键词： Recurrent Convolutional Networks,Micro-Expression Recognition,Motion Magnification,Temporal Jittering
更新于2025-09-23 15:22:29