scholarly journals SPMK AND GRABCUT BASED TARGET EXTRACTION FROM HIGH RESOLUTION REMOTE SENSING IMAGES

2016 ◽  
Vol XLI-B7 ◽  
pp. 195-203
Author(s):  
Weihong Cui ◽  
Guofeng Wang ◽  
Chenyi Feng ◽  
Yiwei Zheng ◽  
Jonathan Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Target Detection ◽  
Image Interpretation ◽  
Cluster Method ◽  
Svm Classifier ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Scale Invariant ◽  
Target Extraction

Target detection and extraction from high resolution remote sensing images is a basic and wide needed application. In this paper, to improve the efficiency of image interpretation, we propose a detection and segmentation combined method to realize semi-automatic target extraction. We introduce the dense transform color scale invariant feature transform (TC-SIFT) descriptor and the histogram of oriented gradients (HOG) & HSV descriptor to characterize the spatial structure and color information of the targets. With the k-means cluster method, we get the bag of visual words, and then, we adopt three levels’ spatial pyramid (SP) to represent the target patch. After gathering lots of different kinds of target image patches from many high resolution UAV images, and using the TC-SIFT-SP and the multi-scale HOG & HSV feature, we constructed the SVM classifier to detect the target. In this paper, we take buildings as the targets. Experiment results show that the target detection accuracy of buildings can reach to above 90%. Based on the detection results which are a series of rectangle regions of the targets. We select the rectangle regions as candidates for foreground and adopt the GrabCut based and boundary regularized semi-auto interactive segmentation algorithm to get the accurate boundary of the target. Experiment results show its accuracy and efficiency. It can be an effective way for some special targets extraction.

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.

Rotation and scale invariant target detection in optical remote sensing images based on pose-consistency voting

2016 ◽  
Vol 76 (12) ◽  
pp. 14461-14483 ◽  
Author(s):  
Yudong Lin ◽  
Hongjie He ◽  
Heng-Ming Tai ◽  
Fan Chen ◽  
Zhongke Yin
Keyword(s):  
Remote Sensing ◽  
Target Detection ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Scale Invariant

scholarly journals A Partition-Based Detection of Urban Villages Using High-Resolution Remote Sensing Imagery in Guangzhou, China

2020 ◽  
Vol 12 (14) ◽  
pp. 2334
Author(s):  
Lu Zhao ◽  
Hongyan Ren ◽  
Cheng Cui ◽  
Yaohuan Huang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Uv Detection ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Detection Model ◽  
Model Object ◽  
Object Based ◽  
Similar Area ◽  
Urban Villages

High-resolution remotely sensed imageries have been widely employed to detect urban villages (UVs) in highly urbanized regions, especially in developing countries. However, the understanding of the potential impacts of spatially and temporally differentiated urban internal development on UV detection is still limited. In this study, a partition-strategy-based framework integrating the random forest (RF) model, object-based image analysis (OBIA) method, and high-resolution remote sensing images was proposed for the UV-detection model. In the core regions of Guangzhou, four original districts were re-divided into five new zones for the subsequent object-based RF-detection of UVs with a series features, according to the different proportion of construction lands. The results show that the proposed framework has a good performance on UV detection with an average overall accuracy of 90.23% and a kappa coefficient of 0.8. It also shows the possibility of transferring samples and models into a similar area. In summary, the partition strategy is a potential solution for the improvement of the UV-detection accuracy through high-resolution remote sensing images in Guangzhou. We suggest that the spatiotemporal process of urban construction land expansion should be comprehensively understood so as to ensure an efficient UV-detection in highly urbanized regions. This study can provide some meaningful clues for city managers identifying the UVs efficiently before devising and implementing their urban planning in the future.

scholarly journals Explore the application of high-resolution nighttime light remote sensing images in nighttime marine ship detection: A case study of LJ1-01 data

2020 ◽  
Vol 12 (1) ◽  
pp. 1169-1184
Author(s):  
Liang Zhong ◽  
Xiaosheng Liu ◽  
Peng Yang ◽  
Rizhi Lin
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Infrared Imaging ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Constant False Alarm Rate ◽  
Remote Sensing Images ◽  
Ship Detection ◽  
Application Potential ◽  
Nighttime Light

AbstractNighttime light remote sensing images show significant application potential in marine ship monitoring, but in areas where ships are densely distributed, the detection accuracy of the current methods is still limited. This article considered the LJ1-01 data as an example, compared with the National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) data, and explored the application of high-resolution nighttime light images in marine ship detection. The radiation values of the aforementioned two images were corrected to achieve consistency, and the interference light sources of the ship light were filtered. Then, when the threshold segmentation and two-parameter constant false alarm rate methods are combined, the ships’ location information was with obtained, and the reliability of the results was analyzed. The results show that the LJ1-01 data can not only record more potential ship light but also distinguish the ship light and background noise in the data. The detection accuracy of the LJ1-01 data in both ship detection methods is significantly higher than that of the NPP/VIIRS data. This study analyzes the characteristics, performance, and application potential of the high-resolution nighttime light data in the detection of marine vessels. The relevant results can provide a reference for the high-precision monitoring of nighttime marine ships.

scholarly journals REMOTE SENSING IMAGE CLASSIFICATION APPLIED TO THE FIRST NATIONAL GEOGRAPHICAL INFORMATION CENSUS OF CHINA

2016 ◽  
Vol XLI-B7 ◽  
pp. 419-422
Author(s):  
Xin Yu ◽  
Zongyong Wen ◽  
Zhaorong Zhu ◽  
Qiang Xia ◽  
Lan Shun
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing ◽  
High Resolution ◽  
Bayesian Networks ◽  
Image Classification ◽  
Image Interpretation ◽  
Remote Sensing Image ◽  
Geographical Information ◽  
Remote Sensing Images ◽  
Remote Sensing Image Classification

Image classification will still be a long way in the future, although it has gone almost half a century. In fact, researchers have gained many fruits in the image classification domain, but there is still a long distance between theory and practice. However, some new methods in the artificial intelligence domain will be absorbed into the image classification domain and draw on the strength of each to offset the weakness of the other, which will open up a new prospect. Usually, networks play the role of a high-level language, as is seen in Artificial Intelligence and statistics, because networks are used to build complex model from simple components. These years, Bayesian Networks, one of probabilistic networks, are a powerful data mining technique for handling uncertainty in complex domains. In this paper, we apply Tree Augmented Naive Bayesian Networks (TAN) to texture classification of High-resolution remote sensing images and put up a new method to construct the network topology structure in terms of training accuracy based on the training samples. Since 2013, China government has started the first national geographical information census project, which mainly interprets geographical information based on high-resolution remote sensing images. Therefore, this paper tries to apply Bayesian network to remote sensing image classification, in order to improve image interpretation in the first national geographical information census project. In the experiment, we choose some remote sensing images in Beijing. Experimental results demonstrate TAN outperform than Naive Bayesian Classifier (NBC) and Maximum Likelihood Classification Method (MLC) in the overall classification accuracy. In addition, the proposed method can reduce the workload of field workers and improve the work efficiency. Although it is time consuming, it will be an attractive and effective method for assisting office operation of image interpretation.

scholarly journals A New Method of Different Neural Network Depth and Feature Map Size on Remote Sensing Small Target Detection

2021 ◽  
Vol 24 (68) ◽  
pp. 21-32
Author(s):  
Yaming Cao ◽  
ZHEN YANG ◽  
CHEN GAO
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Target Detection ◽  
Detection Efficiency ◽  
Detection Accuracy ◽  
Small Target ◽  
Remote Sensing Images ◽  
Feature Maps ◽  
Small Target Detection

Convolutional neural networks (CNNs) have shown strong learning capabilities in computer vision tasks such as classification and detection. Especially with the introduction of excellent detection models such as YOLO (V1, V2 and V3) and Faster R-CNN, CNNs have greatly improved detection efficiency and accuracy. However, due to the special angle of view, small size, few features, and complicated background, CNNs that performs well in the ground perspective dataset, fails to reach a good detection accuracy in the remote sensing image dataset. To this end, based on the YOLO V3 model, we used feature maps of different depths as detection outputs to explore the reasons for the poor detection rate of small targets in remote sensing images by deep neural networks. We also analyzed the effect of neural network depth on small target detection, and found that the excessive deep semantic information of neural network has little effect on small target detection. Finally, the verification on the VEDAI dataset shows, that the fusion of shallow feature maps with precise location information and deep feature maps with rich semantics in the CNNs can effectively improve the accuracy of small target detection in remote sensing images.

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