By Topic

Hierarchical Remote Sensing Image Analysis via Graph Laplacian Energy

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

7 Author(s)
Zhang Huigang ; Sch. of Comput. Sci. & Eng., Beihang Univ., Beijing, China ; Bai Xiao ; Zheng Huaxin ; Zhao Huijie
more authors

Segmentation and classification are important tasks in remote sensing image analysis. Recent research shows that images can be described in hierarchical structure or regions. Such hierarchies can produce the state-of-the-art segmentations and can be used in the classification. However, they often contain more levels and regions than required for an efficient image description, which may cause increased computational complexity. In this letter, we propose a new hierarchical segmentation method that applies graph Laplacian energy as a generic measure for segmentation. It reduces the redundancy in the hierarchy by an order of magnitude with little or no loss of performance. In the classification stage, we apply local self-similarity feature to capture the internal geometric layouts of regions in an image. By incorporating advantages from both semantic hierarchical segmentation and local geometric region description, we have achieved better performance than those from the methods being compared. In the experimental section, we validate the effectiveness of our method by showing results on QuickBird and GeoEye-1 image data sets.

Published in:

Geoscience and Remote Sensing Letters, IEEE  (Volume:10 ,  Issue: 2 )