- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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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 - Ship Discrimination with Deep Convolutional Neural Networks in Sar Images
摘要： With the advantages of all-time, all-weather, and wide coverage, synthetic aperture radar (SAR) systems are widely used for ship detection to ensure marine surveillance. However, the azimuth ambiguity and buildings exhibit similar scattering mechanisms of ships, which cause false alarms in the detection of ships. To address this problem, self-designed deep convolutional neural networks with the capability to automatically learn discriminative features is applied in this paper. Two datasets, including one dataset reconstructed from IEEEDataPort SARSHIPDATA and the other constructed from 10 scenes of Sentinel-1 SAR images, are used to evaluate our approach. Experimental results reveal that our model achieves more than 95% classification accuracy on both datasets, demonstrating the effectiveness of our approach.
关键词： ship discrimination,Sentinel-1 images,synthetic aperture radar,deep convolutional neural networks
更新于2025-09-23 15:23:52
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DeeptransMap: a considerably deep transmission estimation network for single image dehazing
摘要： Due to the ill-posed phenomenon of the classical physical model, single image dehazing based on the model has been a challenging vision task. In recent years, applying machine learning techniques to estimate a critical parameter transmission has proven to be an effective solution to this issue. Accordingly, the robustness and accuracy of learning-based transmission estimation model is extremely important, since it does impact on the final dehazing effects. The state-of-the-art dehazing algorithms by this means generally use haze-relevant features as the single input to their transmission estimation models. However, the used haze-relevant features sometimes are not sufficient and reliable in holding real intrinsic information related to haze due to their two shortcomings and ultimately bring about their less effectiveness for some dehazing cases. Based on related efforts on representation learning and deep convolutional neural networks, in this paper, we seek to achieve the robustness and accuracy of transmission estimation model for bolstering the effectiveness of single image dehazing. Specifically, we propose a hybrid model combining unsupervised and supervised learning in a considerably deep neural networks architecture, in order to achieve accurate transmission map from a single image. Experimental results demonstrate that our work performs favorably against several state-of-the-art dehazing methods with the same estimated goal and keeps efficient in terms of the computational complexity of transmission estimation.
关键词： Feature learning,Deep convolutional neural networks (CNNs),Image dehazing,Transmission estimation
更新于2025-09-23 15:23:52
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FusionCNN: a remote sensing image fusion algorithm based on deep convolutional neural networks
摘要： In remote sensing image fusion field, traditional algorithms based on the human-made fusion rules are severely sensitive to the source images. In this paper, we proposed an image fusion algorithm using convolutional neural networks (FusionCNN). The fusion model implicitly represents a fusion rule whose inputs are a pair of source images and the output is a fused image with end-to-end property. As no datasets can be used to train FusionCNN in remote sensing field, we constructed a new dataset from a natural image set to approximate MS and Pan images. In order to obtain higher fusion quality, low frequency information of MS is used to enhance the Pan image in the pre-processing step. The method proposed in this paper overcomes the shortcomings of the traditional fusion methods in which the fusion rules are artificially formulated, because it learns an adaptive strong robust fusion function through a large amount of training data. In this paper, Landsat and Quickbird satellite data are used to verify the effectiveness of the proposed method. Experimental results show that the proposed fusion algorithm is superior to the comparative algorithms in terms of both subjective and objective evaluation.
关键词： Convolutional neural networks,Deep learning,Remote sensing image fusion,Image enhancement
更新于2025-09-23 15:23:52
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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) - High-Quality Virtual View Synthesis for Light Field Cameras Using Multi-Loss Convolutional Neural Networks
摘要： Although light field cameras record both spatial and angular information, their angular and spatial resolutions are limited when capturing light field data. Thus, it is required to synthesize virtual views. In this paper, we propose high-quality virtual view synthesis based on multi-loss convolutional neural networks (CNN). We adopt multi-loss function for view synthesis in both pixel and feature spaces to increase the angular resolution of light field data. We combine three losses of feature loss, edge loss, and mean squared error (MSE) loss into the multi-loss function. We learn the view synthesis function based on simple three layers of CNN. Experimental results show that the proposed method successfully produces virtual views from light field data and outperforms state-of-the-arts in terms of peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM).
关键词： Convolutional neural networks,loss function,virtual view synthesis,multi-loss,light field
更新于2025-09-23 15:23:52
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Ship detection in spaceborne infrared images based on Convolutional Neural Networks and synthetic targets
摘要： Automatic detection of ships in spaceborne infrared images is important for both military and civil applications due to its all-weather detection capability. While deep learning methods have made great success in many image processing fields recently, training deep learning models still relies on large amount of labeled data, which may limit its application performance for infrared images target detection tasks. Considering that, we propose a new infrared ship detection method based on Convolutional Neural Networks (CNN) which is trained only with synthetic targets. For the problem of limited infrared training data, we design a Transfer Network (T-Net) to generate large amount of synthetic infrared-style ship targets from Google Earth images. The experiments are conducted on a near infrared band image (0:845μm s 0:885μm), a short wavelength infrared band image (1:560μm s 1:66μm) and a long wavelength infrared band image (2:1μm s 2:3μm) of Landsat-8 satellite. The results demonstrate the effectiveness of the target generation ability of T-Net. With only synthetic training samples, our detection method achieves a higher accuracy than other classical ship detection methods.
关键词： Convolutional Neural Networks,Spaceborne infrared image,Synthetic targets,Ship detection
更新于2025-09-23 15:23:52
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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) - PAC-Net: Pairwise Aesthetic Comparison Network for Image Aesthetic Assessment
摘要： Image aesthetic assessment is important for finding well taken and appealing photographs but is challenging due to the ambiguity and subjectivity of aesthetic criteria. We develop the pairwise aesthetic comparison network (PAC-Net), which consists of two parts: aesthetic feature extraction and pairwise feature comparison. To alleviate the ambiguity and subjectivity, we train PAC-Net to learn the relative aesthetic ranks of two images by employing a novel loss function, called aesthetic-adaptive cross entropy loss. Then, we develop simple schemes for using PAC-Net in the tasks of aesthetic ranking and aesthetic classification, respectively. Experimental results demonstrate that PAC-Net achieves the state-of-the-art performances in both the ranking and classification applications.
关键词： convolutional neural networks,pairwise comparison,aesthetic ranking,Image aesthetic assessment
更新于2025-09-23 15:23:52
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Learning Deep Conditional Neural Network for Image Segmentation
摘要： Combining Convolutional Neural Networks (CNNs) with Conditional Random Fields (CRFs) achieve great success among recent object segmentation methods. There are two advantages by such usage. First, CNNs can extract low-level features, which are very similar to the extracted features in primates’ primary visual cortex (V1). Second, CRFs can set up the relationship between input features and output labels in a direct way. In this paper, we extend the first advantage by using CNNs for low-level feature extraction and Structured Random Forest (SRF) based border ownership detector for high-level feature extraction, which are similar to the outputs of primates secondary visual cortex (V2). Compared to the CRF model, an improved Conditional Boltzmann Machine (CBM) which has a multi-channel visible layer are proposed to model the relationship between predicted labels, local and global contexts of objects with multi-scale and multilevel features. Besides, our proposed CBM model is extended for object parsing by using multi visible branches instead of a single visible layer of CBM, which can not only segment the whole body but also the parts of the body under. These visible branches use each branch for the segmentation of the whole body or one of the body parts. All the branches share the same hidden layers of CBM and train the branches under an iterative way. By exploiting object parsing, the whole body segmentation performance of object is improved. To refine the segmentation output, two kinds of optimization algorithms are proposed. The superpixel based algorithm can re-label the overlapped regions of multi-kinds of objects. The other curve correction algorithm corrects the edges of segmented object parts by using smooth edges under a curve similarity criterion. Experiments demonstrate that our models yield competitive results for object segmentation on PASCAL VOC 2012 dataset and for object parsing on PennFudan Pedestrian Parsing dataset, Pedestrian Parsing Surveillance Scenes dataset, Horse-Cow parsing dataset, PASCAL Quadrupeds dataset.
关键词： Convolutional Neural Networks,Conditional Boltzmann Machines,Segmentation,object parsing
更新于2025-09-23 15:23:52
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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 - Environmental Monitoring Using Drone Images and Convolutional Neural Networks
摘要： Recently, drone images have been used in a number of applications, mainly for pollution control and surveillance purposes. In this paper, we introduce the well-known Convolutional Neural Networks in the context of environmental monitoring using drone images, and we show their robustness in real-world images obtained from uncontrolled scenarios. We consider a transfer learning-based approach and compare two neural models, i.e., VGG16 and VGG19, to distinguish four classes: 'water', 'deforesting area', 'forest', and 'buildings'. The results are analyzed by experts in the field and considered pretty much reasonable.
关键词： Land-use classification,Convolutional Neural Networks,Drones
更新于2025-09-23 15:23:52
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Semisupervised Scene Classification for Remote Sensing Images: A Method Based on Convolutional Neural Networks and Ensemble Learning
摘要： The scarcity of labeled samples has been the main obstacle to the development of scene classification for remote sensing images. To alleviate this problem, the efforts have been dedicated to semisupervised classification which exploits both labeled and unlabeled samples for training classifiers. In this letter, we propose a novel semisupervised method that utilizes the effective residual convolutional neural network (ResNet) to extract preliminary image features. Moreover, the strategy of ensemble learning (EL) is adopted to establish discriminative image representations by exploring the intrinsic information of all available data. Finally, supervised learning is performed for scene classification. To verify the effectiveness of the proposed method, it is further compared with several state-of-the-art feature representation and semisupervised classification approaches. The experimental results show that by combining ResNet features with EL, the proposed method can obtain more effective image representations and achieve superior results.
关键词： remote sensing (RS) images,Semi-supervised classification,ensemble learning (EL),scene classification,Convolutional neural networks (CNNs)
更新于2025-09-23 15:23:52
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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) - Integrating Multi-Level Convolutional Features for Correlation Filter Tracking
摘要： Discriminative correlation filters (DCFs) have drawn increasing interest in visual tracking. In particular, a few recent works treat DCFs as a special layer and adding it into a Siamese network for visual tracking. However, they adopt shallow networks to learn target representations, which lack robust semantic information in deeper layers and make these works fail to handle significant appearance changes. In this paper, we design a novel network to fuse multi-level convolutional features, each level of which characterize target from different perspectives. Then we integrate our network with the DCF layer to construct an end-to-end deep architecture for visual tracking. The overall architecture is trained end-to-end offline to adaptively learn target representations, which are not only enabled to encode high-level semantic features and low-level spatial detail features, but also closely related to correlation filters. Experiments show that our proposed tracker achieves superior performance against state-of the-art trackers.
关键词： correlation filters,visual tracking,convolutional neural networks
更新于2025-09-23 15:22:29