A Modified Extrema Pattern with Multilinear Matrix Decomposition based RLC scheme for Efficient Serial Remote Sensing Images Mining

10.21203/rs.3.rs-850113/v2 ◽

2021 ◽

Author(s):

Angelin Preethi R ◽

G. Anandharaj

Keyword(s):

Remote Sensing ◽

Pattern Mining ◽

Matrix Decomposition ◽

Transformation Process ◽

Sequential Patterns ◽

Remote Sensing Images ◽

Scale Invariant ◽

Efficient Manner ◽

Group Matrix ◽

The Matrix

Abstract The growth of serial remote sensing images (SRSI) offers abundant information for determining sequential spatial patterns in several fields like vegetation cover, urban development, and agricultural monitoring. Or else, traditional sequential pattern-mining algorithms cannot be applied efficiently or directly to remote sensing images. Here a new technique is proposed for enhancing the mining efficacy of spatial sequential patterns from raster serial remote sensing images (SRSI) based on pixel grouping approach. The modified extrema pattern is employed to offering grey-scale invariant transform of intensity values unlike previously employed local ternary pattern. The pattern features are computed by transformation process from which the multilinear matrix decomposition of the image is made by computing the covariance estimation on recognizing their orthogonal component. The matrix decomposition is then attained based on run length encoding process (RLC). The two rows of RLC vectors are intersected to attain pixel group matrix. Finally, the compressed image is attained in an efficient manner with effective mining time. The performance outcome reveals that the technique offered in this paper is capable of extracting spatial sequential patterns from SRSI effectively. The proposed system ensures that the entire patterns are extracted at a lower time consumption.

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Research on Remote Sensing Image Matching with Special Texture Background

Symmetry ◽

10.3390/sym13081380 ◽

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.

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

Remote Sensing ◽

10.3390/rs13173425 ◽

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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Uniform Robust Scale-Invariant Feature Matching for Optical Remote Sensing Images

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2011.2144607 ◽

2011 ◽

Vol 49 (11) ◽

pp. 4516-4527 ◽

Cited By ~ 159

Author(s):

Amin Sedaghat ◽

Mehdi Mokhtarzade ◽

Hamid Ebadi

Keyword(s):

Remote Sensing ◽

Feature Matching ◽

Optical Remote Sensing ◽

Remote Sensing Images ◽

Scale Invariant ◽

Invariant Feature ◽

Scale Invariant Feature

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An efficient pixel clustering-based method for mining spatial sequential patterns from serial remote sensing images

Computers & Geosciences ◽

10.1016/j.cageo.2019.01.005 ◽

2019 ◽

Vol 124 ◽

pp. 128-139 ◽

Cited By ~ 4

Author(s):

Xiaozhu Wu ◽

Ximei Zhang

Keyword(s):

Remote Sensing ◽

Sequential Patterns ◽

Remote Sensing Images

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

Multimedia Tools and Applications ◽

10.1007/s11042-016-3857-5 ◽

2016 ◽

Vol 76 (12) ◽

pp. 14461-14483 ◽

Cited By ~ 5

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

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Research on the Zero-Bit Watermarking Algorithms Based on the Matrix Decomposition for Digital Images

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.738-739.674 ◽

2015 ◽

Vol 738-739 ◽

pp. 674-677

Author(s):

Li Jun Fan

Keyword(s):

Digital Watermarking ◽

Digital Images ◽

Matrix Decomposition ◽

Special Technique ◽

Remote Sensing Images ◽

The Matrix ◽

The Many ◽

Tracking Object ◽

Reversible Digital Watermarking ◽

The Military

Due to the problem that the many digital images can be modified more and more easily, watermarking algorithms are proposed. There are many types of digital watermarking algorithms, such as robust digital watermarking algorithms, watermarking schemes for authentication, and reversible digital watermarking algorithms. However, there are some special images, such as the digital medical images for remote treatment, the military images for tracking object, and the remote sensing images, and they are so sensitive to any modification that we should authenticate the copyright by special technique. And this technique is zero-bit watermarking algorithms. In this paper, about zero-bit watermarking algorithms are surveyed. The basic procedures of these classical algorithms are analyzed, and some suggestion about the zero-bit algorithms is given.

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