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- 2019
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- pattern recognition
- image
- partial discharge
- convolutional neural network(CNN)
- Conditional Random Fields (CRF)
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- Airborne hyperspectral
- green tide
- Elegant End-to-End Fully Convolutional Network (E3FCN)
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[IEEE 2018 International Conference on Network Infrastructure and Digital Content (IC-NIDC) - Guiyang, China (2018.8.22-2018.8.24)] 2018 International Conference on Network Infrastructure and Digital Content (IC-NIDC) - Ship detection in foggy remote sensing image via scene classification R-CNN
摘要： The object detection networks via Faster R-CNN for ship detection have demonstrated impressive performance. However, the complexity of weather conditions in high resolution satellite images exposes the limited capacity of these networks. Images interfered by fog are common in optical remote sensing images. In this paper, we embrace this observation and introduce our research. Unlike SAR images, optical sensor images are very susceptible to the effects of the weather, especially clouds and fog.So, accurate target information cannot be obtained from these image, which reduces the accuracy of ship detection. To solve this problem, we attempts to introduce the image defogging methods into object detection networks to suppress the interference of clouds. Secondly, the SC-R-CNN structure is proposed, which uses the scene classification network (SCN) to realize the classification of fog-containing images and cascaded with the object detection network to form a dual-stream object detection framework. In addition, the combination of defogging methods and the SC-R-CNN network also produces more optimized results. We use the remote sensing image data set containing various types of weather conditions to confirm the validity and accuracy of the proposed method.
关键词： Remote sensing,Image processing,Defogging,Object detection,Convolutional neural network,Deep learning
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 - Fusenet: End- to-End Multispectral Vhr Image Fusion and Classification
摘要： Classification of very high resolution (VHR) satellite images faces two major challenges: 1) inherent low intra-class and high inter-class spectral similarities and 2) mismatching resolution of available bands. Conventional methods have addressed these challenges by adopting separate stages of image fusion and spatial feature extraction steps. These steps, however, are not jointly optimizing the classification task at hand. We propose a single-stage framework embedding these processing stages in a multiresolution convolutional network. The network, called FuseNet, aims to match the resolution of the panchromatic and multispectral bands in a VHR image using convolutional layers with corresponding downsampling and upsampling operations. We compared FuseNet against the use of separate processing steps for image fusion, such as pansharpening and resampling through interpolation. We also analyzed the sensitivity of the classification performance of FuseNet to a selected number of its hyperparameters. Results show that FuseNet surpasses conventional methods.
关键词： image fusion,Convolutional networks,deep learning,land cover classification,VHR image
更新于2025-09-10 09:29:36
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[IEEE 2018 IEEE 22nd International Conference on Intelligent Engineering Systems (INES) - Las Palmas de Gran Canaria, Spain (2018.6.21-2018.6.23)] 2018 IEEE 22nd International Conference on Intelligent Engineering Systems (INES) - Comparison of Different Deep-Learning Methods for Image Classification
摘要： In this paper, we showed how to solve exemplary image classification problem. The goal of image classification problem is to correctly classify the input image within the expected class/label. In our case, we focused on classifying the animal image into one of two categories: mammals or birds. That classification problem is not a trivial task using standard machine learning algorithms. The main reason for that is the fact that the algorithms are based on previously prepared features for classifying object. It is often done by hand by a researcher. Defining specific features for example how a beak looks, wing, tail, fur and etc. is natural for humans, but understanding it by computers is extremely difficult. However, nowadays deep learning algorithms managed to overcome some obstacles and work best for these types of problems. We can use Deep Neural Networks (DNN) in two ways - by developing it from scratch for the specific problem or using method calls transfer learning. The main goal of this paper is comparing the two methods by using them in a real-life example. We showed how to correctly prepare a dataset, create a DNN model from scratch and how to adjust it. We also showed how to use the transfer learning technique. All the steps we made are described in a way which allows for easy adaptation of these algorithms to similar problems.
关键词： deep learning,convolutional neural network,image classification,transfer learning
更新于2025-09-10 09:29:36
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[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) - Unfeatured Weld Positioning Technology Based on Neural Network and Machine Vision
摘要： In machine vision, image processing technology is the basis of target recognition and positioning. When the background of the image is complex, especially when the background feature is similar to the target feature, the accuracy of the target recognition by traditional image processing methods cannot be guaranteed. In this paper, based on the background of automatic welding technology, proposing a new method of combining the neural networks and machine vision. Specifically, the image is preprocessed by using an improved convolutional auto-encoder to enhance the target features and remove the characteristics of the main interferers. Then, use image processing technology to extract the target and complete the processing of the featureless image. Finally, use a binocular camera to achieve accurate positioning of the target. This paper provides a new idea for the identification and positioning of the target.
关键词： weld positioning,machine vision,neural networks,featureless image,convolutional auto-encoder
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 (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - FPGA Based Implementation of Convolutional Neural Network for Hyperspectral Classification
摘要： convolutional neural network (CNN) has been widely used for hyperspectral classification. Current researches of CNN based hyperspectral image classification is mainly implemented on graphics processing unit (GPU) platform. However, GPU is not suitable for onboard processing due to the problem of space radiation and power supply on image acquiring platform. Therefore, in this paper, FPGA is selected to implement CNN based hyperspectral classification for further onboard processing. Specially, a hardware model is designed for the forward classification step of CNN using hardware description language, including computation structure for CNN, implementation of different layers, weight loading scheme, and data interfere. Simulation results over Pavia dataset validate the proposed FPGA based implementation is coincide with that on GPU platform.
关键词： classification,FPGA,hyperspectral,Convolutional neural network
更新于2025-09-10 09:29:36
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Activity Recognition using Temporal Optical Flow Convolutional Features and Multi-Layer LSTM
摘要： Nowadays digital systems are installed universally for continuously collecting enormous amounts of data, thereby requiring human monitoring for identification of different activities and events. Smarter surveillance is the need of this era through which normal and abnormal activities can be automatically identified using artificial intelligence and computer vision technology. In this paper, we propose a framework for activity recognition in surveillance videos captured over industrial systems. The continuous surveillance video stream is first divided into important shots, where shots are selected using the proposed convolutional neural network (CNN) based human saliency features. Next, temporal features of an activity in the sequence of frames are extracted by utilizing the convolutional layers of a FlowNet2 CNN model. Finally, a multi-layer long short-term memory (LSTM) is presented for learning long-term sequences in the temporal optical flow features for activity recognition. Experiments are conducted using different benchmark action and activity recognition datasets and the results reveal the effectiveness of the proposed method for activity recognition in industrial settings compared to state-of-the-art methods.
关键词： surveillance applications,deep learning,convolutional neural network,Activity recognition,long short-term memory,industrial systems,artificial intelligence
更新于2025-09-10 09:29:36
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[IEEE 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) - Hangzhou (2018.8.6-2018.8.9)] 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) - Fine Classification of Typical Farms in Southern China Based on Airborne Hyperspectral Remote Sensing Images
摘要： In the southern part of China, peculiar land fragmentation so that crop planting is characterized by small planting area of a single block, alternate cropping in multiple plots and diversified planting in space. Based on the unique crop planting characteristics in southern part of China, this paper take typical southern farm in Honghu City, Hubei Province as an example, adopting the platform of unmanned aerial vehicle imaging spectrometer to obtain the “double high” (high spectral and high spatial resolution) images at the same time. To complete the crop fine classification of 'double high' images , the CNN- CRF algorithm is proposed. The CNN-CRF algorithm acquires 91.5% accuracy with only 1% train samples on remote sensing images, which performs far better than most traditional classification approaches.
关键词： Conditional Random Fields (CRF),Convolutional Neural Network (CNN),Fine Classification,Airborne hyperspectral
更新于2025-09-10 09:29:36
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[IEEE 2018 IEEE International Conference on Computational Electromagnetics (ICCEM) - Chengdu, China (2018.3.26-2018.3.28)] 2018 IEEE International Conference on Computational Electromagnetics (ICCEM) - Deep Learning of Raw Radar Echoes for Target Recognition
摘要： Synthetic aperture radar (SAR) based classification approaches are commonly used methods for automatic target recognition. However, SAR imaging requires complex two-dimensional matched filtering and interpolation algorithms. In this paper, we propose deep learning technology for automatic target recognition based on raw radar echoes instead of SAR images. A modern convolutional neural network (CNN) model is trained directly by radar-echo training data set, and is evaluated on the testing data set. The experimental results show that the proposed method could achieve high accuracy and efficiency for the target recognition.
关键词： target classification,convolutional neural network,radar echoes
更新于2025-09-10 09:29:36
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[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 ADCs-Based CNN Classification System for Precise Diagnosis of Prostate Cancer
摘要： This paper addresses the issue of early diagnosis of prostate cancer from diffusion-weighted magnetic resonance imaging (DWI) using a convolutional neural network (CNN) based computer-aided diagnosis (CAD) system. The proposed CNN-based CAD system first segments the prostate using a geometric deformable model. The evolution of this model is guided by a stochastic speed function that exploits first-and second-order appearance models besides shape prior. The fusion of these guiding criteria is accomplished using a nonnegative matrix factorization (NMF) model. Then, the apparent diffusion coefficients (ADCs) within the segmented prostate are calculated at each b-value. They are used as imaging markers for the blood diffusion of the scanned prostate. For the purpose of classification/diagnosis, a three dimensional CNN has been trained to extract the most discriminatory features of these ADC maps for distinguishing malignant from benign prostate tumors. The performance of the proposed CNN-based CAD system is evaluated using DWI datasets acquired from 45 patients (20 benign and 25 malignant) at seven different b-values. The acquisition of these DWI datasets is performed using two different scanners with different magnetic field strengths (1.5 Tesla and 3 Tesla). The conducted experiments on in-vivo data confirm that the use of ADCs makes the proposed system nonsensitive to the magnetic field strength.
关键词： Prostate Cancer,Convolutional Neural Networks,Apparent Diffusion Coefficients
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 - An Hybrid Recurrent Convolutional Neural Network for Crop Type Recognition Based on Multitemporal Sar Image Sequences
摘要： Agriculture monitoring is a key task for producers, governments and decision makers. The analysis of multitemporal remote sensing data provides a cost-effective way to perform this task. Recurrent Neural Networks (RNNs) have been successfully used in temporal modeling problems, while Convolutional Neural Networks (CNNS) are the state-of-the-art in image classification, mainly due to their ability to capture spatial context. In this work, we propose the use of a hybrid network architecture for crop mapping that combines RNNs and CNNs. We evaluate this architecture experimentally upon a Sentinel-1A database from a tropical region in Brazil. The ability of recurrent networks to model temporal context is compared with the conventional image stacking approach. The impact of using CNN learned features rather than context aware handcrafted features is also investigated. In our analysis the hybrid architecture achieved better average class accuracy than alternative approaches based on image stacking and GLCM features.
关键词： Crop Recognition,Convolutional Neural Networks,Recurrent Neural Networks
更新于2025-09-10 09:29:36