Intelligent Remote Sensing Classification Based on Cuckoo Search Algorithm

doi:10.13474/j.cnki.11-2246.2017.0014

Abstract

Abstract: A new, intelligent approach to classify remote-sensing images based on Cuckoo search algorithm is presented. Cuckoo search algorithm, a new bio-inspired intelligence algorithm, is widely used to solve optimization problems. Cuckoo search algorithm to search for the optimal upper and lower threshold values on each band of remote-sensing image is applied. The classification rules are constructed by the links between the optimal split values and classification type in the explicit formation of ‘if-then’, and each link corresponds to the optimal solution of one Cuckoo, nest or egg. By taking an example of ALOS image in the north shore of the Yangtze River estuary, the proposed classification method based on CS algorithm is implemented and tested against See5.0 decision-tree method. The overall classification accuracy and Kappa coefficient of CS-based method are higher than the See5.0 decision-tree one. The results demonstrate that the practicability of applying CS algorithm to classify the remote-sensing images.

Key words: cuckoo search algorithm (CS), swarm intelligence caculation, remote sensing image, classification

CLC Number:

P237

SHEN Quanfei, CAO Min, SHI Zhaoliang, XU Ruqi. Intelligent Remote Sensing Classification Based on Cuckoo Search Algorithm[J]. 测绘通报, 2017, 0(1): 65-68,73.

References

[1] 杜培军.遥感原理与应用[M].徐州:中国矿业大学出版社,2006.
[2] 贾永红.人工神经网络在多源遥感影像分类中的应用[J].测绘通报,2000(7):7-8.
[3] 申文明,王文杰,罗海江,等.基于决策树分类技术的遥感影像分类方法研究[J].遥感技术与应用,2007,22(3):336-337.
[4] 杨红磊,彭军还. 基于马尔可夫随机场的模糊c-均值遥感影像分类[J].测绘学报,2012,41(2):213-218.
[5] 谭琨,杜培军. 基于再生核Hilbert空间小波核函数支持向量机的高光谱遥感影像分类[J]. 测绘学报, 2011,40(2):143-147.
[6] 陈云浩,冯通,史培军,等.基于面向对象和规则的遥感影像分类研究[J].武汉大学学报(自然科学版), 2006, 31(4):316-320.
[7] STATHAKIS D,VASILAKOS A. Comparisons of Computational Intelligence Based Classification Techniques for Remotely Sensed Optical Image Classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(8):2305-2318.
[8] SIPPER M. Machine Nature:The Coming Age of Bio-inspired Computing[M]. New York:McGraw-Hill, 2002.
[9] 刘小平,黎夏,何晋强,等.基于蚁群智能的遥感影像分类新方法[J].遥感学报,2008, 12(2):253-262.
[10] LIU Xiaoping, LI Xia, LIU Liu, et al. An Innovative Method to Classify Remote-Sensing Images Using Ant Colony Optimization[J].IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(12):4198-4208.
[11] 刘小平,黎夏,彭晓鹃,等.一种基于生物群集智能优化的遥感分类方法[J].中国科学(D辑:地球科学), 2007, 37(10):1400-1408.
[12] 曹敏,史照良,阳建逸. 蜂群智能算法的遥感影像分类方法[J].测绘学报,2013, 42(5):745-751.
[13] YANG X S, DEB S.Cuckoo Search via Lévy Flights, Proceedings of World Congress on Nature and Biologically Inspired Computing (NaBIC 2009)[M].[S.l.]:IEEE Publications, 2009:210-214.
[14] YANG X S. Swarm Intelligence Based Algorithms:A Critical Analysis[J]. Evolutionary Intelligence, 2014, 7:17-28.
[15] YANG X S, DEB S. Cuckoo Search:Recent Advances and Applications[J]. Neural Computing and Applications, 2014, 24:169-174.
[16] MANIKANDAN P, SELVARAJAN S. Data Clustering Using Cuckoo Search Algorithm (CSA)[J]. Advances in Intelligent Systems and Computing, 2014, 236:1275-1283.
[17] LIU Xinni. Application of Cckoo Search Agorithm in Multi-threshold Image Segmentation[J]. Computer Engineering, 2013, 39(7):274-278.