scholarly journals OPTICAL REMOTE SENSING IMAGE OPTIMIZED DEHAZING ALGORITHM BASED ON HOT

2016 ◽  
Vol XLI-B3 ◽  
pp. 797-803
Author(s):  
Zhou Yang ◽  
Xu Qing ◽  
Xu Jiwei ◽  
Jin Guowang
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Satellite Images ◽  
Space Environment ◽  
Optical Remote Sensing ◽  
Near Earth Space ◽  
Texture Information ◽  
Optical Remote Sensing Image ◽  
Dark Pixel ◽  
Application Fields

Due to the significantly effect of clouds in the near-earth space environment to remote sensing satellite images, some satellite images can not be utilized normally, resulting in large limitation of their application fields. For the background suppressed haze thickness index (BSHTI) and improvement background suppressed haze thickness index (IBSHTI) have the characteristics of thoroughly cloud correction and badly deficiency of the tone and texture information, we propose to first adopt IBSHTI to calculate the cloud thickness image of different bands, then the dark-pixel images are obtained by down sampling, and the texture is eliminated by introducing Texture and edge information (TEI). Experiment results show that our method can well retain the ground tone and texture information while removing the effect of clouds, especially in urban areas.

scholarly journals OPTICAL REMOTE SENSING IMAGE OPTIMIZED DEHAZING ALGORITHM BASED ON HOT

2016 ◽  
Vol XLI-B3 ◽  
pp. 797-803
Author(s):  
Zhou Yang ◽  
Xu Qing ◽  
Xu Jiwei ◽  
Jin Guowang
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Satellite Images ◽  
Space Environment ◽  
Optical Remote Sensing ◽  
Near Earth Space ◽  
Texture Information ◽  
Optical Remote Sensing Image ◽  
Dark Pixel ◽  
Application Fields

Due to the significantly effect of clouds in the near-earth space environment to remote sensing satellite images, some satellite images can not be utilized normally, resulting in large limitation of their application fields. For the background suppressed haze thickness index (BSHTI) and improvement background suppressed haze thickness index (IBSHTI) have the characteristics of thoroughly cloud correction and badly deficiency of the tone and texture information, we propose to first adopt IBSHTI to calculate the cloud thickness image of different bands, then the dark-pixel images are obtained by down sampling, and the texture is eliminated by introducing Texture and edge information (TEI). Experiment results show that our method can well retain the ground tone and texture information while removing the effect of clouds, especially in urban areas.

scholarly journals Mathematical models for information classification and recognition of multi-target optical remote sensing images

Open Physics ◽  
2020 ◽  
Vol 18 (1) ◽  
pp. 951-960
Author(s):  
Haiqing Zhang ◽  
Jun Han
Keyword(s):  
Remote Sensing ◽  
Recognition Rate ◽  
Three Dimensional ◽  
Remote Sensing Image ◽  
Optical Remote Sensing ◽  
Specific Class ◽  
Three Dimensional Model ◽  
Image Information ◽  
Information Classification ◽  
Optical Remote Sensing Image

Abstract Traditionally, three-dimensional model is used to classify and recognize multi-target optical remote sensing image information, which can only identify a specific class of targets, and has certain limitations. A mathematical model of multi-target optical remote sensing image information classification and recognition is designed, and a local adaptive threshold segmentation algorithm is used to segment multi-target optical remote sensing image to reduce the gray level between images and improve the accuracy of feature extraction. Remote sensing image information is multi-feature, and multi-target optical remote sensing image information is identified by chaotic time series analysis method. The experimental results show that the proposed model can effectively classify and recognize multi-target optical remote sensing image information. The average recognition rate is more than 95%, the maximum robustness is 0.45, the recognition speed is 98%, and the maximum time-consuming average is only 14.30 s. It has high recognition rate, robustness, and recognition efficiency.

scholarly journals Optical Remote Sensing

2021 ◽  
pp. 315-344
Author(s):  
Man Sing Wong ◽  
Xiaolin Zhu ◽  
Sawaid Abbas ◽  
Coco Yin Tung Kwok ◽  
Meilian Wang
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Information Extraction ◽  
Urban Areas ◽  
Satellite Image ◽  
Background Information ◽  
Optical Remote Sensing ◽  
Basic Operation ◽  
City Development ◽  
Satellite Image Processing

AbstractApplications of Earth-observational remote sensing are rapidly increasing over urban areas. The latest regime shift from conventional urban development to smart-city development has triggered a rise in smart innovative technologies to complement spatial and temporal information in new urban design models. Remote sensing-based Earth-observations provide critical information to close the gaps between real and virtual models of urban developments. Remote sensing, itself, has rapidly evolved since the launch of the first Earth-observation satellite, Landsat, in 1972. Technological advancements over the years have gradually improved the ground resolution of satellite images, from 80 m in the 1970s to 0.3 m in the 2020s. Apart from the ground resolution, improvements have been made in many other aspects of satellite remote sensing. Also, the method and techniques of information extraction have advanced. However, to understand the latest developments and scope of information extraction, it is important to understand background information and major techniques of image processing. This chapter briefly describes the history of optical remote sensing, the basic operation of satellite image processing, advanced methods of object extraction for modern urban designs, various applications of remote sensing in urban or peri-urban settings, and future satellite missions and directions of urban remote sensing.

Detection of small ship targets from an optical remote sensing image

2018 ◽  
Vol 11 (3) ◽  
pp. 275-284 ◽  
Author(s):  
Mingzhu Song ◽  
Hongsong Qu ◽  
Guixiang Zhang ◽  
Guang Jin
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Optical Remote Sensing ◽  
Optical Remote Sensing Image ◽  
Small Ship

Image Retrieval Using Intensity Gradients and Texture Chromatic Pattern

2021 ◽  
Vol 17 (1) ◽  
pp. 57-73
Author(s):  
I.Jeena Jacob ◽  
Betty Paulraj ◽  
P. Ebby Darney ◽  
Hoang Viet Long ◽  
Tran Manh Tuan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Satellite Images ◽  
Local Binary Pattern ◽  
Texture Feature ◽  
Query Image ◽  
Shape Information ◽  
Histogram Of Oriented Gradient ◽  
Texture Pattern ◽  
Texture Information

Methods to retrieve images involve retrieving images from the database by using features of it. They are colour, shape, and texture. These features are used to find the similarity for the query image with that of images in the database. The images are sorted in the order with this similarity. The article uses intra- and inter-texture chrominance and its intensity. Here inter-chromatic texture feature is extracted by LOCTP (local oppugnant colored texture pattern). Local binary pattern (LBP) gives the intra-texture information. Histogram of oriented gradient (HoG) is used to get the shape information from the satellite images. The performance analysis is land-cover remote sensing database, NWPU-VHR-10 dataset, and satellite optical land cover database gives better results than the previous works.

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