Feature Analysis and Extraction of Complex Motion Target Based on Coherent Laser Remote Sensing Detection

2012 ◽  
Vol 588-589 ◽  
pp. 1076-1080
Author(s):  
Zheng Li ◽  
Yi Hua Hu ◽  
Fei Yan
Keyword(s):  
Remote Sensing ◽  
Vibration Signal ◽  
Complex Motion ◽  
Hilbert Huang Transform ◽  
High Measurement ◽  
Inherent Frequency ◽  
Feature Based ◽  
Civil Values ◽  
Destructive Measurement ◽  
Laser Remote Sensing

Vibration features of moving targets can reflect their detailed characteristics, which have important military and civil values. Coherent lidar is the preponderant method of target detection, because of its advantages such as high measurement accuracy and ability of long-distant non-destructive measurement, which is appropriate for detecting the vibration information of the target through remote sensing. Traditional analysis of moving target’s vibration always contained only one motion mode, which could not reflect the real complex motion of the target. This paper proposed a novel model of complex moving target’s vibration for coherent laser remote sensing detection. Considering the vibration signal is non-linear and non-stationary, Hilbert-Huang transform (HHT) was applied to the decomposition of the signal. After decomposition, the energy of the vibrating signal in some inherent frequency band was selected as the feature of the signal. Simulations and experiments were carried out to verify the divisibility of the feature, which could support the identification of target vibration feature based on coherent laser remote sensing detection.

Laser Remote Sensing of Atmospheric Pollutants

1985 ◽  
Author(s):  
Dennis K. Killinger ◽  
Norman Menyuk ◽  
Aram Mooradian
Keyword(s):  
Remote Sensing ◽  
Atmospheric Pollutants ◽  
Laser Remote Sensing

scholarly journals Remote Sensing of Ocean Fronts in Marine Ecology and Fisheries

2021 ◽  
Vol 13 (5) ◽  
pp. 883
Author(s):  
Igor M. Belkin
Keyword(s):  
Remote Sensing ◽  
Marine Ecology ◽  
Popular Front ◽  
Ocean Fronts ◽  
Detection Algorithms ◽  
Concise Review ◽  
Feature Based ◽  
Front Detection

This paper provides a concise review of the remote sensing of ocean fronts in marine ecology and fisheries, with a particular focus on the most popular front detection algorithms and techniques, including those proposed by Canny, Cayula and Cornillon, Miller, Shimada et al., Belkin and O’Reilly, and Nieto et al.. A case is made for a feature-based approach that emphasizes fronts as major structural and circulation features of the ocean realm that play key roles in various aspects of marine ecology.

scholarly journals Performance Evaluation of Single-Label and Multi-Label Remote Sensing Image Retrieval Using a Dense Labeling Dataset

2018 ◽  
Vol 10 (6) ◽  
pp. 964 ◽  
Author(s):  
Zhenfeng Shao ◽  
Ke Yang ◽  
Weixun Zhou
Keyword(s):  
Remote Sensing ◽  
Performance Evaluation ◽  
Deep Learning ◽  
Image Retrieval ◽  
Semantic Segmentation ◽  
Semantic Content ◽  
Remote Sensing Image ◽  
Remote Sensing Images ◽  
Benchmark Datasets ◽  
Feature Based

Benchmark datasets are essential for developing and evaluating remote sensing image retrieval (RSIR) approaches. However, most of the existing datasets are single-labeled, with each image in these datasets being annotated by a single label representing the most significant semantic content of the image. This is sufficient for simple problems, such as distinguishing between a building and a beach, but multiple labels and sometimes even dense (pixel) labels are required for more complex problems, such as RSIR and semantic segmentation.We therefore extended the existing multi-labeled dataset collected for multi-label RSIR and presented a dense labeling remote sensing dataset termed "DLRSD". DLRSD contained a total of 17 classes, and the pixels of each image were assigned with 17 pre-defined labels. We used DLRSD to evaluate the performance of RSIR methods ranging from traditional handcrafted feature-based methods to deep learning-based ones. More specifically, we evaluated the performances of RSIR methods from both single-label and multi-label perspectives. These results demonstrated the advantages of multiple labels over single labels for interpreting complex remote sensing images. DLRSD provided the literature a benchmark for RSIR and other pixel-based problems such as semantic segmentation.

LASER REMOTE SENSING FOR ENVIRONMENTAL APPLICATIONS

2013 ◽  
pp. 175-205
Author(s):  
Antonella Boselli ◽  
Gianluca Pisani ◽  
N. Spinelli ◽  
Xuan Wang
Keyword(s):  
Remote Sensing ◽  
Environmental Applications ◽  
Laser Remote Sensing

scholarly journals An Image Registration Method Using Deep Residual Network Features for Multisource High-Resolution Remote Sensing Images

2021 ◽  
Vol 13 (17) ◽  
pp. 3425
Author(s):  
Xin Zhao ◽  
Hui Li ◽  
Ping Wang ◽  
Linhai Jing
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Registration ◽  
Feature Descriptor ◽  
Remote Sensing Images ◽  
Residual Network ◽  
Scale Invariant ◽  
Registration Method ◽  
Sensing Applications ◽  
Feature Based

Accurate registration for multisource high-resolution remote sensing images is an essential step for various remote sensing applications. Due to the complexity of the feature and texture information of high-resolution remote sensing images, especially for images covering earthquake disasters, feature-based image registration methods need a more helpful feature descriptor to improve the accuracy. However, traditional image registration methods that only use local features at low levels have difficulty representing the features of the matching points. To improve the accuracy of matching features for multisource high-resolution remote sensing images, an image registration method based on a deep residual network (ResNet) and scale-invariant feature transform (SIFT) was proposed. It used the fusion of SIFT features and ResNet features on the basis of the traditional algorithm to achieve image registration. The proposed method consists of two parts: model construction and training and image registration using a combination of SIFT and ResNet34 features. First, a registration sample set constructed from high-resolution satellite remote sensing images was used to fine-tune the network to obtain the ResNet model. Then, for the image to be registered, the Shi_Tomas algorithm and the combination of SIFT and ResNet features were used for feature extraction to complete the image registration. Considering the difference in image sizes and scenes, five pairs of images were used to conduct experiments to verify the effectiveness of the method in different practical applications. The experimental results showed that the proposed method can achieve higher accuracies and more tie points than traditional feature-based methods.

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