By Topic

Development and Demonstration of an Artificial Immune Algorithm for Mangrove Mapping Using Landsat TM

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

5 Author(s)
Yanmin Luo ; Coll. of Comput. Sci. & Technol., Huaqiao Univ., Xiamen, China ; Minghong Liao ; Jing Yan ; Caiyun Zhang
more authors

Mangroves are valuable contributors to coastal ecosystems; knowledge of the dynamics of mangrove ecosystems is important in the context of global change. To obtain this knowledge, remote sensing is an indispensable means, yet it poses challenges since the accuracy is sometimes unsatisfactory in distinguishing mangroves from other land cover types with traditional classification methods. In this letter, we proposed a modified artificial immune algorithm (AIA), in which the antibodies represent the candidate solutions and the antigens are expressed by the fitness function. Multiclass coevolution was combined with the concept of clonal selection to ensure computation of an optimal clustering center in parallel for each land cover type. A cluster-center-oriented decimal encoding method for antibodies was adopted, and the inner class variance and the between-class difference together were used to formulate the fitness function. Furthermore, a design of the antibody solubility-based selection operator and nonuniform mutation operator was undertaken. Applying this modified AIA to a Landsat Thematic Mapper multispectral remote sensing imagery in the Zhangjiang estuary in southeastern China, we found that the AIA substantially improved classification accuracy over traditional methods, showing an overall accuracy of 90% (kappa coefficient = 0.88) and was capable to discern mangrove well (commission of 10% and omission of 22%).

Published in:

Geoscience and Remote Sensing Letters, IEEE  (Volume:10 ,  Issue: 4 )