- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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A Novel Neural Network for Remote Sensing Image Matching
摘要： Rapid development of remote sensing (RS) imaging technology makes the acquired images have larger size, higher resolution, and more complex structure, which goes beyond the reach of classical hand-crafted feature-based matching. In this paper, we propose a feature learning approach based on two-branch networks to transform the image matching task into a two-class classification problem. To match two key points, two image patches centered at the key points are entered into the proposed network. The network aims to learn discriminative feature representations for patch matching, so that more matching pairs can be obtained on the premise of maintaining higher subpixel matching accuracy. The proposed network adopts a two-stage training mode to deal with the complex characteristics of RS images. An adaptive sample selection strategy is proposed to determine the size of each patch by the scale of its central key point. Thus, each patch can preserve the texture structure around its key point rather than all patches have a predetermined size. In the matching prediction stage, two strategies, namely, superpixel-based sample graded strategy and superpixel-based ordered spatial matching, are designed to improve the matching efficiency and matching accuracy, respectively. The experimental results and theoretical analysis demonstrate the feasibility, robustness, and effectiveness of the proposed method.
关键词： neural network,image matching,remote sensing (RS) image,Deep learning (DL)
更新于2025-09-23 15:22:29
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Integrating Handcrafted and Deep Features for Optical Coherence Tomography Based Retinal Disease Classification
摘要： Deep Neural Networks (DNNs) have been widely applied to automatic analysis of medical images for disease diagnosis, and to help human experts by efficiently processing immense amounts of images. While handcrafted feature has been used for eye disease detection or classification since the 1990s, DNN was recently adopted in this area and showed very promising performance. Since handcrafted and deep feature can extract complementary information, we propose in this paper three different integration frameworks to combine handcrafted and deep feature for optical coherence tomography (OCT) image based eye disease classification. In addition, to integrate the handcrafted feature at Input and Fully Connection (FC) layers using existing networks like VGG, DenseNet and Xception, a novel ribcage network (RC Net) is also proposed for feature integration at middle layers. For RC Net, two “rib” channels are designed to independently process deep and handcrafted features, and another so called “spine” channel is designed for the integration. While dense blocks are the main components of the three channels, sum operation is proposed for the feature map integration. Our experimental results showed that the deep networks achieved better classification accuracy after integration of the handcrafted features e.g. SIFT and Gabor. The RC Net showed the best performance among all proposed feature integration methods.
关键词： feature integration,deep learning,Artificial intelligence,optical coherence tomography
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 - Deep Semantic Segmentation of Aerial Imagery Based on Multi-Modal Data
摘要： In this paper, we focus on the use of multi-modal data to achieve a semantic segmentation of aerial imagery. Thereby, the multi-modal data is composed of a true orthophoto, the Digital Surface Model (DSM) and further representations derived from these. Taking data of different modalities separately and in combination as input to a Residual Shuffling Convolutional Neural Network (RSCNN), we analyze their value for the classification task given with a benchmark dataset. The derived results reveal an improvement if different types of geometric features extracted from the DSM are used in addition to the true orthophoto.
关键词： multi-modal data,aerial imagery,Shuffling-CNN,deep learning,Semantic segmentation
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 - Comparative Study of Feature Extraction Approaches for Ship Classification in Moderate-Resolution SAR Imagery
摘要： This paper presents a comparative study of existing feature extraction approaches for ship classification in moderate-resolution synthetic aperture radar (SAR) images. Ship classification is a key functionality in many maritime surveillance applications. For efficient ship classification, appropriate feature extraction is crucial. Most of existing studies have used high-resolution images. For maritime surveillance, however, wide-area coverage is essential whereas it inevitably reduces the spatial resolution. In this paper, we evaluate the applicability of representative methods to moderate-resolution images. The evaluated methods are hand-crafted feature extraction (HCF), principal component analysis (PCA) and autoencoder (AE) based on neural-network. The evaluation is done on the basis of accuracy for two-class ship classification into tanker and cargo. The experiments demonstrate that AE outperforms HCF and PCA in classification accuracy by 7.4% and 2.6%, respectively. Furthermore, AE performs best even in classification of challenging cases such as small ships.
关键词： feature extraction,ship classification,moderate resolution,deep learning,SAR,maritime
更新于2025-09-23 15:22:29
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An Efficient and Robust Iris Segmentation Algorithm Using Deep Learning
摘要： Iris segmentation is a critical step in the entire iris recognition procedure. Most of the state-of-the-art iris segmentation algorithms are based on edge information. However, a large number of noisy edge points detected by a normal edge-based detector in an image with specular reflection or other obstacles will mislead the pupillary boundary and limbus boundary localization. In this paper, we present a combination method of learning-based and edge-based algorithms for iris segmentation. A well-designed Faster R-CNN with only six layers is built to locate and classify the eye. With the bounding box found by Faster R-CNN, the pupillary region is located using a Gaussian mixture model. Then, the circular boundary of the pupillary region is fit according to five key boundary points. A boundary point selection algorithm is used to find the boundary points of the limbus, and the circular boundary of the limbus is constructed using these boundary points. Experimental results showed that the proposed iris segmentation method achieved 95.49% accuracy on the challenging CASIA-Iris-Thousand database.
关键词： Iris segmentation,Faster R-CNN,Gaussian mixture model,Boundary point selection,Deep learning
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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Deep Belief Network for Spectral–Spatial Classification of Hyperspectral Remote Sensor Data
摘要： With the development of high-resolution optical sensors, the classification of ground objects combined with multivariate optical sensors is a hot topic at present. Deep learning methods, such as convolutional neural networks, are applied to feature extraction and classification. In this work, a novel deep belief network (DBN) hyperspectral image classification method based on multivariate optical sensors and stacked by restricted Boltzmann machines is proposed. We introduced the DBN framework to classify spatial hyperspectral sensor data on the basis of DBN. Then, the improved method (combination of spectral and spatial information) was verified. After unsupervised pretraining and supervised fine-tuning, the DBN model could successfully learn features. Additionally, we added a logistic regression layer that could classify the hyperspectral images. Moreover, the proposed training method, which fuses spectral and spatial information, was tested over the Indian Pines and Pavia University datasets. The advantages of this method over traditional methods are as follows: (1) the network has deep structure and the ability of feature extraction is stronger than traditional classifiers; (2) experimental results indicate that our method outperforms traditional classification and other deep learning approaches.
关键词： classification,feature extraction,multi-sensor fusion,remote sensors,deep learning,hyperspectral image
更新于2025-09-23 15:22:29
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[IEEE 2018 28th International Telecommunication Networks and Applications Conference (ITNAC) - Sydney, Australia (2018.11.21-2018.11.23)] 2018 28th International Telecommunication Networks and Applications Conference (ITNAC) - CamThings: IoT Camera with Energy-Efficient Communication by Edge Computing based on Deep Learning
摘要： In recent years, the demand for IoT cameras has increased due to the high demand for image data. However, the image sensor is unsuitable as an energy-constrained edge device for IoT due to its high-power consumption. Therefore, periodic on–off scheduling of IoT cameras is a promising approach since video recording using image sensors is energy-intensive. Due to the constrained computing performance of edge devices, IoT is still based on cloud computing with energy leaks by transmitting all the data of edge devices to cloud. In this paper, we proposed energy-efficient communication via edge computing based on deep learning, which reduces power consumption by transmitting only images of interest classified using edge computing. We also designed and implemented CamThings, which is an energy-efficient IoT camera with periodic on–off scheduling and the proposed energy-efficient communication. To analyze and evaluate the efficiency of the proposed communication scheme, we implemented a power consumption model for CamThings. In an environment with a low interest ratio, the proposed CamThings is superior to the baseline method with only periodic on–off scheduling in terms of power consumption and lifetime. When the scheduling period T is 5s and the interest ratio is 0.1, the proposed method consumed 41% less power than the baseline method. As a result, CamThings has a lifetime of more than one month.
关键词： Wireless Communication,Energy Efficiency,Edge Computing,IoT Camera,Deep Learning
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) - Comparative study of visual saliency maps in the problem of classification of architectural images with Deep CNNs
摘要： Incorporating Human Visual System (HVS) models into building of classifiers has become an intensively researched field in visual content mining. In the variety of models of HVS we are interested in so-called visual saliency maps. Contrarily to scan-paths they model instantaneous attention assigning the degree of interestingness/saliency for humans to each pixel in the image plane. In various tasks of visual content understanding, these maps proved to be efficient stressing contribution of the areas of interest in image plane to classifiers models. In previous works saliency layers have been introduced in Deep CNNs, showing that they allow reducing training time getting similar accuracy and loss values in optimal models. In case of large image collections efficient building of saliency maps is based on predictive models of visual attention. They are generally bottom-up and are not adapted to specific visual tasks. Unless they are built for specific content, such as 'urban images'-targeted saliency maps we also compare in this paper. In present research we propose a 'bootstrap' strategy of building visual saliency maps for particular tasks of visual data mining. A small collection of images relevant to the visual understanding problem is annotated with gaze fixations. Then the propagation to a large training dataset is ensured and compared with the classical GBVS model and a recent method of saliency for urban image content. The classification results within Deep CNN framework are promising compared to the purely automatic visual saliency prediction.
关键词： Mexican Culture,Saliency Maps,Deep 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, 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