scholarly journals Multi-Level Features Extraction for Discontinuous Target Tracking in Remote Sensing Image Monitoring

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4855 ◽  
Author(s):  
Bin Zhou ◽  
Xuemei Duan ◽  
Dongjun Ye ◽  
Wei Wei ◽  
Marcin Woźniak ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Target Tracking ◽  
Principal Component ◽  
Remote Sensing Image ◽  
Features Extraction ◽  
Reference Image ◽  
Final Decision ◽  
Image Monitoring ◽  
Multi Level ◽  
Adaptive Neighborhood

Many techniques have been developed for computer vision in the past years. Features extraction and matching are the basis of many high-level applications. In this paper, we propose a multi-level features extraction for discontinuous target tracking in remote sensing image monitoring. The features of the reference image are pre-extracted at different levels. The first-level features are used to roughly check the candidate targets and other levels are used for refined matching. With Gaussian weight function introduced, the support of matching features is accumulated to make a final decision. Adaptive neighborhood and principal component analysis are used to improve the description of the feature. Experimental results verify the efficiency and accuracy of the proposed method.

scholarly journals Research on Remote Sensing Image Matching with Special Texture Background

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1380
Author(s):  
Sen Wang ◽  
Xiaoming Sun ◽  
Pengfei Liu ◽  
Kaige Xu ◽  
Weifeng Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Geometric Distortion ◽  
Reference Image ◽  
Remote Sensing Images ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Speeded Up Robust Features ◽  
Adaptive Quantization ◽  
Texture Background

The purpose of image registration is to find the symmetry between the reference image and the image to be registered. In order to improve the registration effect of unmanned aerial vehicle (UAV) remote sensing imagery with a special texture background, this paper proposes an improved scale-invariant feature transform (SIFT) algorithm by combining image color and exposure information based on adaptive quantization strategy (AQCE-SIFT). By using the color and exposure information of the image, this method can enhance the contrast between the textures of the image with a special texture background, which allows easier feature extraction. The algorithm descriptor was constructed through an adaptive quantization strategy, so that remote sensing images with large geometric distortion or affine changes have a higher correct matching rate during registration. The experimental results showed that the AQCE-SIFT algorithm proposed in this paper was more reasonable in the distribution of the extracted feature points compared with the traditional SIFT algorithm. In the case of 0 degree, 30 degree, and 60 degree image geometric distortion, when the remote sensing image had a texture scarcity region, the number of matching points increased by 21.3%, 45.5%, and 28.6%, respectively and the correct matching rate increased by 0%, 6.0%, and 52.4%, respectively. When the remote sensing image had a large number of similar repetitive regions of texture, the number of matching points increased by 30.4%, 30.9%, and −11.1%, respectively and the correct matching rate increased by 1.2%, 0.8%, and 20.8% respectively. When processing remote sensing images with special texture backgrounds, the AQCE-SIFT algorithm also has more advantages than the existing common algorithms such as color SIFT (CSIFT), gradient location and orientation histogram (GLOH), and speeded-up robust features (SURF) in searching for the symmetry of features between images.

scholarly journals Novel Multi-Scale Filter Profile-Based Framework for VHR Remote Sensing Image Classification

2019 ◽  
Vol 11 (18) ◽  
pp. 2153
Author(s):  
Zhiyong Lv ◽  
Guangfei Li ◽  
Yixiang Chen ◽  
Jón Atli Benediktsson
Keyword(s):  
Remote Sensing ◽  
Principal Component ◽  
Remote Sensing Image ◽  
Classification Performance ◽  
Remote Sensing Images ◽  
Multi Scale ◽  
Remote Sensing Image Classification ◽  
Very High Spatial Resolution ◽  
Layer Stacking ◽  
Initial Classification

Filter is a well-known tool for noise reduction of very high spatial resolution (VHR) remote sensing images. However, a single-scale filter usually demonstrates limitations in covering various targets with different sizes and shapes in a given image scene. A novel method called multi-scale filter profile (MFP)-based framework (MFPF) is introduced in this study to improve the classification performance of a remote sensing image of VHR and address the aforementioned problem. First, an adaptive filter is extended with a series of parameters for MFP construction. Then, a layer-stacking technique is used to concatenate the MPFs and all the features into a stacked vector. Afterward, principal component analysis, a classical descending dimension algorithm, is performed on the fused profiles to reduce the redundancy of the stacked vector. Finally, the spatial adaptive region of each filter in the MFPs is used for post-processing of the obtained initial classification map through a supervised classifier. This process aims to revise the initial classification map and generate a final classification map. Experimental results performed on the three real VHR remote sensing images demonstrate the effectiveness of the proposed MFPF in comparison with the state-of-the-art methods. Hard-tuning parameters are unnecessary in the application of the proposed approach. Thus, such a method can be conveniently applied in real applications.

scholarly journals Research On The Classification Of High Resolution Image Based On Object-oriented And Class Rule

2015 ◽  
Vol XL-7/W4 ◽  
pp. 75-80 ◽  
Author(s):  
C. K. Li ◽  
W. Fang ◽  
X. J. Dong
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Classification ◽  
Object Oriented ◽  
Remote Sensing Data ◽  
Image Data ◽  
Remote Sensing Image ◽  
Classification Method ◽  
Multi Level ◽  
Image Object

With the development of remote sensing technology, the spatial resolution, spectral resolution and time resolution of remote sensing data is greatly improved. How to efficiently process and interpret the massive high resolution remote sensing image data for ground objects, which with spatial geometry and texture information, has become the focus and difficulty in the field of remote sensing research. An object oriented and rule of the classification method of remote sensing data has presents in this paper. Through the discovery and mining the rich knowledge of spectrum and spatial characteristics of high-resolution remote sensing image, establish a multi-level network image object segmentation and classification structure of remote sensing image to achieve accurate and fast ground targets classification and accuracy assessment. Based on worldview-2 image data in the Zangnan area as a study object, using the object-oriented image classification method and rules to verify the experiment which is combination of the mean variance method, the maximum area method and the accuracy comparison to analysis, selected three kinds of optimal segmentation scale and established a multi-level image object network hierarchy for image classification experiments. The results show that the objectoriented rules classification method to classify the high resolution images, enabling the high resolution image classification results similar to the visual interpretation of the results and has higher classification accuracy. The overall accuracy and Kappa coefficient of the object-oriented rules classification method were 97.38%, 0.9673; compared with object-oriented SVM method, respectively higher than 6.23%, 0.078; compared with object-oriented KNN method, respectively more than 7.96%, 0.0996. The extraction precision and user accuracy of the building compared with object-oriented SVM method, respectively higher than 18.39%, 3.98%, respectively better than the object-oriented KNN method 21.27%, 14.97%.

scholarly journals A New Method for Geometric Quality Evaluation of Remote Sensing Image Based on Information Entropy

2014 ◽  
Vol XL-2 ◽  
pp. 63-70 ◽  
Author(s):  
W. Jiao ◽  
T. Long ◽  
G. Yang ◽  
G. He
Keyword(s):  
Remote Sensing ◽  
Information Entropy ◽  
Quality Evaluation ◽  
Remote Sensing Image ◽  
Remotely Sensed ◽  
New Method ◽  
Reference Image ◽  
Geometric Accuracy ◽  
Remotely Sensed Imagery ◽  
Geometric Quality

Geometric accuracy of the remote sensing rectified image is usually evaluated by the root-mean-square errors (RMSEs) of the ground control points (GCPs) and check points (CPs). These discrete geometric accuracy index data represent only on a local quality of the image with statistical methods. In addition, the traditional methods only evaluate the difference between the rectified image and reference image, ignoring the degree of the original image distortion. A new method of geometric quality evaluation of remote sensing image based on the information entropy is proposed in this paper. The information entropy, the amount of information and the uncertainty interval of the image before and after rectification are deduced according to the information theory. Four kind of rectification model and seven situations of GCP distribution are applied on the remotely sensed imagery in the experiments. The effective factors of the geometrical accuracy are analysed and the geometric qualities of the image are evaluated in various situations. Results show that the proposed method can be used to evaluate the rectification model, the distribution model of GCPs and the uncertainty of the remotely sensed imagery, and is an effective and objective assessment method.

Research on the Optimal Classification Method for Remote Sensing Image Based on the Gabor-PCA Analysis

2014 ◽  
Vol 989-994 ◽  
pp. 3617-3620
Author(s):  
Jing Hui Yang ◽  
Li Guo Wang ◽  
Jin Xi Qian
Keyword(s):  
Remote Sensing ◽  
Spatial Information ◽  
Gabor Filter ◽  
Principal Component ◽  
Remote Sensing Image ◽  
Classification Method ◽  
Spectral Features ◽  
Classification Methods ◽  
Remote Sensing Image Classification ◽  
Core Idea

According to the problem that the traditional remote sensing image classification methods focus only on analyzing the spectral features and have low utilization of the spatial information, a new spatial-spectral classification method is proposed in this paper, its core idea is to combine the spectral features base on the Principal Component Analysis (PCA) algorithm with the spatial features extracted by the Gabor filter. Experiments show that, compared with the traditional classification methods, the proposed method can improve the classification accuracy and the Kappa coefficient, which means to bring better classification and visual effects.

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