Multifeature Based Satellite Image Segmentation of High Spatial Resolution Remote Sensing Images
- DOI
- 10.2991/978-94-6463-196-8_41How to use a DOI?
- Keywords
- High Spatial Resolution; Satellite Image Segmentation; Remote Sensing
- Abstract
With the continuous development of remote sensing technology, the spatial resolution of the image is getting higher and higher and the characteristic information contained in the image is more abundant. High spatial resolution data provides detailed information about the ground for various applications. Methods of Image segmentation become more and more important in the field of remote sensing image analysis. The structural features and texture information are more obvious. The traditional segmentation method based on a single feature of the image can no longer meet the high requirements. In this work, an efficient algorithm is introduced for evaluating segmentation quality. Multifeature based Satellite image segmentation algorithm is proposed to segment the HSR satellite images into different regions based on the properties of multiple features such as color and texture present in the image. The combination of Multifeature information helps to improve an accuracy of segmentation.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Sujata Gaikwad AU - Vijaya Musande PY - 2023 DA - 2023/08/10 TI - Multifeature Based Satellite Image Segmentation of High Spatial Resolution Remote Sensing Images BT - Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022) PB - Atlantis Press SP - 538 EP - 545 SN - 1951-6851 UR - https://doi.org/10.2991/978-94-6463-196-8_41 DO - 10.2991/978-94-6463-196-8_41 ID - Gaikwad2023 ER -