UNSUPERVISED LAND-COVER CLASSIFICATION THROUGH HYPER-HEURISTIC-BASED HARMONY SEARCH

被引：0
|
作者：
Papa, J. [1 ]
Papa, L. [2 ]
Pisani, R. [3 ]
Pereira, D. [3 ]
机构：
[1] Sao Paulo State Univ, Dept Comp, Bauru, SP, Brazil
[2] Sao Paulo State Southwest Coll, Dept Hlth, Avare, SP, Brazil
[3] Univ Western Sao Paulo, Dept Comp, Presidente Prudente, SP, Brazil
来源：
2015 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2015年
关键词：
Clustering; Land-cover classification; Machine Learning; Genetic Programming;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Unsupervised land-cover classification aims at learning intrinsic properties of spectral and spatial features for the task of area coverage in urban and rural areas. In this paper, we propose to model the problem of optimizing the well-known k means algorithm by combining different variations of the Harmony Search technique using Genetic Programming (GP). We have shown GP can improve the recognition rates when using one optimization technique only, but it still deserves a deeper study when we have a very good individual technique to be combined.
引用
收藏
页码：69 / 72
页数：4
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