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Spatial-temporal classification enhancement via 3-D iterative filtering for multi-temporal Very-High-Resolution satellite images

Li, Mao, Li
http://rave.ohiolink.edu/etdc/view?acc_num=osu1514939565470669

Abstract Details

2018, Master of Science, Ohio State University, Electrical and Computer Engineering.
It has been widely studied utilizing spatial-temporal remote sensing images to interpret ground objects. Due to the spectral ambiguities caused by inevitable factors like meteorological conditions, sunlight illumination, sensor radiation performance and earth objects reflectance, the interpretation accuracy of multi-class classification using a single temporal image is unsatisfactory. Under the hypothesis that earth objects have the temporal consistency, this thesis proposes a classification accuracy enhancement approach that utilizes 3-D temporal very-high-resolution images, where the digital surface model is generated through stereo dense matching. In the first place, the probability distribution of images’ coverage areas is derived from the supervised Random Forest Classifier. Then, the proposed method iteratively filters the probability maps with a 3-D bilateral filter which is built upon the domain of spectrum, spatial and height information of surface. Compared with single filtering enhancement studied before, continuously message passing from data in different dates can be achieved by iteratively filtering until the probability converge. It is conducted that each of the three experiments on 8 temporal consistent images presents convincing different types of city layout in Port-au-Prince, the capital of Haiti, including open grounds, dense residential and educational areas. After classification enhancement, the overall classification accuracy is increased by 2%~6%. The presenting results illustrate that although the study areas experienced a devastating earthquake leading to significant changes in the city landscape, the constraint on surface height effectively eliminates pre-enhancing classification errors. Furthermore, although the first filtering contributes the most on classification accuracy enhancement, this approach is manifested to consistently enhance the classification performance for similar earth objects like road and ground, permanent shelters and buildings through further iterations.
Rongjun Qin, Dr. (Advisor)
Desheng Liu, Dr. (Committee Co-Chair)
90 p.
Computer Engineering; Computer Science; Electrical Engineering; Geographic Information Science; Geography; Remote Sensing
Image Enhance; Spatiotemporal probability bilateral filter; Random Forest, Classification

Recommended Citations

Citations

  • Li, Li, M. (2018). Spatial-temporal classification enhancement via 3-D iterative filtering for multi-temporal Very-High-Resolution satellite images [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1514939565470669

    APA Style (7th edition)

  • Li, Li, Mao. Spatial-temporal classification enhancement via 3-D iterative filtering for multi-temporal Very-High-Resolution satellite images. 2018. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1514939565470669.

    MLA Style (8th edition)

  • Li, Li, Mao. "Spatial-temporal classification enhancement via 3-D iterative filtering for multi-temporal Very-High-Resolution satellite images." Master's thesis, Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1514939565470669

    Chicago Manual of Style (17th edition)

osu1514939565470669
248
© 2018, some rights reserved.Creative Commons License Spatial-temporal classification enhancement via 3-D iterative filtering for multi-temporal Very-High-Resolution satellite images by Mao Li Li is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by The Ohio State University and OhioLINK.
Release 3.2.12