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- 2019
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- pattern recognition
- image
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- convolutional neural network(CNN)
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- green tide
- Elegant End-to-End Fully Convolutional Network (E3FCN)
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[IEEE 2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) - Las Vegas, NV (2018.4.8-2018.4.10)] 2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) - Underwater Image Restoration using Deep Networks to Estimate Background Light and Scene Depth
摘要： Images taken underwater often suffer color distortion and low contrast because of light scattering and absorption. An underwater image can be modeled as a blend of a clear image and a background light, with the relative amounts of each determined by the depth from the camera. In this paper, we propose two neural network structures to estimate background light and scene depth, to restore underwater images. Experimental results on synthetic and real underwater images demonstrate the effectiveness of the proposed method.
关键词： depth estimation,image restoration,convolutional neural networks,Underwater images
更新于2025-09-23 15:21:21
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[IEEE 2018 15th European Radar Conference (EuRAD) - Madrid, Spain (2018.9.26-2018.9.28)] 2018 15th European Radar Conference (EuRAD) - Deep Learning based Human Activity Classification in Radar Micro-Doppler Image
摘要： A convolutional neural network (CNN) based deep learning (DL) approach to classify human activities in micro-Doppler spectrogram of radar is investigated. MOCAP dataset, from Carnegie Mellon University, is used for spectrogram simulation. Seven activities are classified with the proposed CNN network. Our network outperforms several previously published DL-based approaches. To understand the network’s impact on classification performance, we investigate some key parameters of the proposed network. Experiment result demonstrates that a deeper network does not necessarily result in a higher accuracy. We also examine the network size and the number of output feature maps to find out their impact on the result.
关键词： Deep Learning,Convolutional Neural Network,Human Activity Classification,Micro-Doppler Spectrogram,Radar image
更新于2025-09-23 15:21:21
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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 - Ship Detection Based on Deep Convolutional Neural Networks for Polsar Images
摘要： In this paper, we proposed a ship detection method based on deep convolutional neural networks for PolSAR images. The proposed ship detector firstly segments PolSAR images into sub-samples using a sliding window of fixed size to effectively extract translational-invariant spatial features. Further, the modified faster region based convolutional neural network (Faster-RCNN) method is utilized to realize ship detection for ships with different sizes and fusion the detection result. Finally, the proposed method was validated using real measured NASA/JPL AIRSAR datasets by comparing the performance with the modified constant false alarm rate (CFAR) detector. The comparison results demonstrate the validity and generality of the proposed detection algorithm.
关键词： Deep convolutional neural networks,polarimetric synthetic aperture radar (PolSAR),ship detection
更新于2025-09-23 15:21:21
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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 - Multitask Classification of Remote Sensing Scenes Using Deep Neural Networks
摘要： The problem of scene classification in remote sensing (RS) images has attracted a lot of attention recently. Many datasets have been presented in the literature for this purpose with each claiming to be the benchmark dataset. In this paper, we propose a different approach to the RS community. Instead of putting our effort in building larger and large scene datasets, we argue that it is better to build a machine learning framework that can learn from all available datasets. We formulate this as a multitask learning problem where each dataset represents a task. Then, we present a deep learning solution that can perform multitask learning. We test the proposed multitask network on three popular scene datasets, namely UC Merced, KSA, and AID datasets. Preliminary results show the promising capabilities of this solution at sharing information between tasks and improving the classification accuracy.
关键词： Deep learning,Scene classification,Multitask classification,Convolutional Neural Network
更新于2025-09-23 15:21:21
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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 - Time-Scale Transferring Deep Convolutional Neural Network for Mapping Early Rice
摘要： In recent years, the use of deep learning in remote sensing domain has made it possible to automate mapping in large-scale. In this paper, we propose a transfer learning method which pre-train a convolutional neural network (CNN) with middle-resolution remote sensing data in 2016, and fine-tune it in following years with a spot of high-resolution remote sensing data in 2017. We used the fine-tuned model to mapping the early-rice in 25 countries which cost only 21 minutes, and yielded an overall accuracy of 81.68%. The result demonstrate that the convolutional neural network model can transfer in different time period with little adjustment in a very high accuracy.
关键词： middle-resolution data,convolutional neural network,time-scale,transfer learning
更新于2025-09-23 15:21:21
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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 - Rotated Region Based Fully Convolutional Network for Ship Detection
摘要： Ship detection from high-resolution optical remote sensing images has been a prevalent domain in recent years. Unlike objects in natural images, ships of interest can be anywhere in optical remote sensing images with multi-scale and multi-oriented which makes it more difficult to be detected. In this paper, we propose a novel method based on the fully convolutional network to detect ships. Our method has three important components: 1) we design a network merging different levels of feature map to fuse multi-scale information. Determining the existence of large ship require features from deep layers in the network, while predicting rotated bounding box enclosing small ships needs shallow layers information; 2) The network can be trained end-to-end to generate score maps which indicates the confidence score for the ship region of interest in pixel-wise level through all locations and scale of an image; 3) We design a rotated bounding box regression model to localize the ships. The experimental results on our dataset collected from Google Earth has demonstrated our proposed method achieves promising performance on ship detection in terms of both efficiency and accuracy in high-resolution optical remote sensing images.
关键词： Ship detection,Rotated region,Fully Convolutional network
更新于2025-09-23 15:21:21
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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 - Oil-Palm Tree Detection in Aerial Images Combining Deep Learning Classifiers
摘要： Palm oil is the largest vegetable oil in the world in terms of produced volume, and 75% of global production is used for food and cooking purposes. Sustainable management of the producing areas calls for the frequent assessment of field conditions. In this paper, we investigate an automatic algorithm based on deep learning that is capable to build an inventory of individual oil-palm trees using aerial color images collected by unmanned aerial vehicles. The idea consists of combining the outputs of two independent convolutional neural networks, trained on partially distinct subsets of samples and different spatial scales to capture coarse and fine details of image patches. The estimated posterior probabilities are combined by simple averaging as to improve detection accuracy and estimate the confidence for each individual detection. Non-maxima suppression removes weak detections. Experiments at three commercial oil-palm tree plantations sites aged two, four, and 16 years in Northern Brazil revealed overall detection accuracies in the range 91.2–98.8% using orthomosaics of decimeter spatial resolution. The proposed approach can be a useful component of a forest monitoring system based on remote sensing.
关键词： convolutional neural networks,classification,Tree counting,remote sensing,forest inventory
更新于2025-09-23 15:21:21
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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 - Introducing Eurosat: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
摘要： In this paper, we address the challenge of land use and land cover classification using Sentinel-2 satellite images. The key contributions are as follows. We present a novel dataset based on Sentinel-2 satellite images covering 13 different spectral bands and consisting of 10 classes with in total 27,000 labeled images. We evaluate state-of-the-art deep Convolutional Neural Networks (CNNs) on this novel dataset with its different spectral bands. We also evaluate deep CNNs on existing remote sensing datasets and compare the obtained results. With the proposed novel dataset, we achieved an overall classification accuracy of 98.57%. The classification system resulting from the proposed research opens a gate towards various Earth observation applications. We demonstrate how the classification system can assist in improving geographical maps.
关键词： Deep Learning,Land Use Classification,Earth Observation,Convolutional Neural Network,Machine Learning,Dataset,Land Cover Classification
更新于2025-09-23 15:21:21
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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 - Fully Convolutional Semi-Supervised Gan for Polsar Classification
摘要： We propose a novel semi-supervised fully convolutional network for Polarimetric synthetic aperture radar (PolSAR) terrain classification. First, by designing a fully convolutional structure, we can perform pixel-based classification tasks. Then, by applying semi-supervised generative adversarial networks (GANs), we utilize both labeled and unlabeled samples and aim to obtain higher classification accuracy. Through a mini-max two-player game, GAN has better performance than other “single-player” classifiers. Finally, we combine the fully convolutional structure with the semi-supervised GAN. Our fully convolutional semi-supervised GAN (FC-SGAN) has excellent spatial feature learning ability and can perform end-to-end pixel-based classification tasks. Experimental results show that compared with existing works, the proposed method has better performances. Even when the training set gets smaller, our method keeps high accuracy.
关键词： terrain classification,fully convolutional network,generative adversarial network,semi-supervised learning
更新于2025-09-23 15:21:21
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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 - Inshore Ship Detection in Sar Images Based on Deep Neural Networks
摘要： Inshore ship detection in SAR image faces difficulties on correctly identifying near-shore ships and onshore objects. This article proposes a multi-scale full convolutional network (MS-FCN) based sea-land segmentation method and applies a rotatable bounding box based object detection method (DR-Box) to solve the inshore ship detection problem. The sea region and land region are separated by MS-FCN then DR-Box is applied on sea region. The proposed method combines global information and local information of SAR image to achieve high accuracy. The networks are trained with Chinese Gaofen-3 satellite images. Experiments on the testing image show most inshore ships are successfully located by the proposed method.
关键词： object detection networks,full convolutional networks,deep learning,inshore ship detection,Synthetic aperture radar
更新于2025-09-23 15:21:21