Arctic travel has become a significant way for commercial or scientific researches. Sea-ice is always serious threat for Arctic navigation. In this paper, a classification method of aerialimages is proposed to recogn...
详细信息
ISBN:
(纸本)9781538671504
Arctic travel has become a significant way for commercial or scientific researches. Sea-ice is always serious threat for Arctic navigation. In this paper, a classification method of aerialimages is proposed to recognize different types of sea-ice scenes, which can be further used to analyze the threat of each scene in Arctic Ocean. However, due to the diverse types and distribution of sea-ice in various scenes, the categories of sea-ice scenes are indistinct. So a grouping way is primarily introduced to define different sea-ice scenes. These scenes are grouped into six typical categories according to the distribution of sea-ice. Then a transfer learning strategy is used to fine-tune a pre-trained deep convolution neural network model. Finally, using that trained network to classify new sea ice scenes. Experimental results show that an acceptable classification accuracy can be obtained.
The automatic classification of ships from aerialimages is a considerable challenge. Previous works have usually applied image processing and computer vision techniques to extract meaningful features from visible spe...
详细信息
The automatic classification of ships from aerialimages is a considerable challenge. Previous works have usually applied image processing and computer vision techniques to extract meaningful features from visible spectrum images in order to use them as the input for traditional supervised classifiers. We present a method for determining if an aerialimage of visible spectrum contains a ship or not. The proposed architecture is based on Convolutional Neural Networks (CNN), and it combines neural codes extracted from a CNN with a k-Nearest Neighbor method so as to improve performance. The kNN results are compared to those obtained with the CNN Softmax output. Several CNN models have been configured and evaluated in order to seek the best hyperparameters, and the most suitable setting for this task was found by using transfer learning at different levels. A new dataset (named MASATI) composed of aerialimagery with more than 6000 samples has also been created to train and evaluate our architecture. The experimentation shows a success rate of over 99% for our approach, in contrast with the 79% obtained with traditional methods in classification of ship images, also outperforming other methods based on CNNs. A dataset of images (MWPU VHR-10) used in previous works was additionally used to evaluate the proposed approach. Our best setup achieves a success ratio of 86% with these data, significantly outperforming previous state-of-the-art ship classification methods.
Overhead transmission line corridor detection is important to ensure the safety of power facilities. Frequent and uncertain changes in the transmission line corridor environment requires an efficient and autonomous UA...
详细信息
Overhead transmission line corridor detection is important to ensure the safety of power facilities. Frequent and uncertain changes in the transmission line corridor environment requires an efficient and autonomous UAV inspection system, whereas the existing UAV-based inspection systems has some shortcomings in control model and ground clearance measurement. For one thing, the existing manual control model has the risk of striking power lines because it is difficult for manipulators to judge the distance between the UAV fuselage and power lines accurately. For another, the ground clearance methods based on UAV usually depend on LiDAR (Light Detection and Ranging) or single-view visual repeat scanning, with which it is difficult to balance efficiency and accuracy. Aiming at addressing the challenging issues above, a novel UAV inspection system is developed, which can sense 3D information of transmission line corridor by the cooperation of the dual-view stereovision module and an advanced embedded NVIDIA platform. In addition, a series of advanced algorithms are embedded in the system to realize autonomous control of UAVs and ground clearance measurement. Firstly, an edge-assisted power line detection method is proposed to locate the power line accurately. Then, 3D reconstruction of the power line is achieved based on binocular vision, and the target flight points are generated in the world coordinate system one-by-one to guide the UAVs movement along power lines autonomously. In order to correctly detect whether the ground clearances are in the range of safety, we propose an aerial image classification based on a light-weight semantic segmentation network to provide auxiliary information categories of ground objects. Then, the 3D points of ground objects are reconstructed according to the matching points set obtained by an efficient feature matching method, and concatenated with 3D points of power lines. Finally, the ground clearance can be measured and detected acco
In the study of identifying homogeneous regions in remote sensing images,fuzzy clustering is one of the most frequently used *** used method of fuzzy cluster analysis is the fuzzy C-means algorithm(FCM),which easily t...
详细信息
ISBN:
(纸本)9781479951499
In the study of identifying homogeneous regions in remote sensing images,fuzzy clustering is one of the most frequently used *** used method of fuzzy cluster analysis is the fuzzy C-means algorithm(FCM),which easily traps into local optimal *** algorithm combining FCM with genetic algorithms is introduced for aerial remote sensing image fuzzy clustering *** input image features are extracted based on a new descriptor which combines Gabor descriptor with Gist *** dimension reduction of the extracted feature vector is processed through principal component *** the extracted features from in-house aerialimages dataset are clustered with proposed *** shows that this method can get a good clustering effect.
Deep learning-based algorithms have shown significant state-of-the-art accuracy in aerial image classification. Besides, the nature of these algorithms is a black box, which puts the question of why a particular outpu...
详细信息
Deep learning-based algorithms have shown significant state-of-the-art accuracy in aerial image classification. Besides, the nature of these algorithms is a black box, which puts the question of why a particular output is produced. Therefore explainability is one kind of solution to improve the transparency of DNN network's decision. In this paper, we have designed a lightweight and explainable convolutional neural network (CNN) architecture for emergency monitoring from aerialimagery. We interpret the outcomes of the proposed architecture with a newly designed ex-plainable algorithm which is the improved version of the model-agnostic methods such as Shap-ley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME). We have used a dedicated dataset named as aerialimage Database for Emergency Response (AIDER) for the experiments and explained the decisions of the proposed CNN classifier to ensure reliability. The proposed classifier achieves 96% accuracy with minimal memory requirements on a benchmark set with known ground truth and explains their outcomes with the newly pro-posed explainable algorithm.
暂无评论