基于蚁群算法的光谱分解方法剔除植被干扰信息

夏浩东; 薛云; 邓会娟; 刘凤姣

基于蚁群算法的光谱分解方法剔除植被干扰信息

1.
国土资源部实物地质资料中心, 河北三河 065201
2.
湖南城市学院城市建设系, 湖南益阳 413000

详细信息

作者简介:
夏浩东(1968-), 男, 高级工程师, 主要从事地质矿产勘查与实物地质资料管理工作。E-mail:xiahaodong68@163.com

中图分类号: P627
计量
- 文章访问数: 146
- HTML全文浏览量: 69
- PDF下载量: 4
- 被引次数: 0
出版历程
- 收稿日期: 2011-10-09
- 刊出日期: 2012-03-01

ELIMINATING THE DISTURBANCE OF VEGETATION INFORMATION BY SPECTRAL MIXTURE ANALYSIS BASED ON ANT COLONY ALGORITHM

1.
Cores & Samples Centre of Land & Resources, Sanhe 065201, China
2.
Hunan City University, Yiyang 413000, China

摘要

摘要: 针对遥感图像预处理工作中，光谱分解方法处理速度慢而蚁群算法识别目标速度快的特点，结合蚁群算法和线性光谱混合模型，建立基于蚁群搜索的光谱分解模型，以剔除植被干扰信息。选取青海黄南州吉地地区为研究区，首先确定蚂蚁移动规则，然后建立基于蚁群算法的光谱分解模型，最后根据模型重构不含有植被信息的新的多波段图像，通过残差图分析以及原图与剔除植被后影像对比分析，初步验证了基于蚁群算法的光谱分解方法剔除植被干扰信息的可行性。
- 蚁群算法 /
- 线性光谱混合模型 /
- ETM /
- 青海吉地地区
Abstract: In the research of extracting alteration information from remote sensing image, eliminating the disturbance of vegetation from image is important. In this paper, a spectral mixture analysis model is established based on ant colony algorithm, in order to eliminate the disturbance of vegetation. This model is applied to Jidi area in Huangnan City, Qinghai Province. Firstly, the ant-moving rule is defined. Secondly, the model of spectral mixture analysis based on ant colony algorithm is established. In the end, a new image without the disturbance of vegetation is plotted. Comparison of ETM image and result image shows that the disturbance of vegetation can be eliminated by the method.
- ant colony algorithm /
- Linear Spectrum Mixture Model /
- ETM /
- Jidi area in Qinghai

HTML全文

图 1 预处理后影像（ETM7+ETM 4+ETM 1）

Figure 1. The image after pre-processing

下载: 全尺寸图片幻灯片

图 2 散点图

Figure 2. Scatter diagram

下载: 全尺寸图片幻灯片

图 3 剔除植被后影像（band 7+band 4+band 1）

Figure 3. The image after eliminating vegetation

下载: 全尺寸图片幻灯片

图 4 残差图（最大残差为0.103）

蓝色：0~0.025；绿色：0.025~0.050；黄色：0.050~0.075；红色：0.075~0.103

Figure 4. The residual map

下载: 全尺寸图片幻灯片

参考文献(7)

[1]	Adams J B, Smith M O, ohnson P E.Pectral mixture modeling:A new analysis of rock and soil types at the Viking Lander 1 site[J].Journal of Geophysical Research, 1986, 91(B8):8098~8112. doi: 10.1029/JB091iB08p08098
[2]	Smith M O, Ustin S L, Adams J B, et al.Vegetation in deserts:A regional measure of abundance from multispectral images[J].Remote Sensing of Environment, 1990, 31(1):1~26. doi: 10.1016/0034-4257(90)90074-V
[3]	Dorigo M, Caro G D, Gambardella L M.Ant algorithms for discrete optimization[J].Artificial Life, 1999, 5(3):137~172.
[4]	Dorigo M, Bonabeau E, Theraulaz G.Ant algorithms and stigmergy[J].Future Generation Computer System, 2000, 16(6):851~871. http://www.sciencedirect.com/science/article/pii/S0167739X0000042X
[5]	李士勇.蚁群优化算法及其应用研究进展[J].计算机测量与控制, 2003, 11(12):911~913. doi: 10.3321/j.issn:1671-4598.2003.12.001 LI Shi-yong.Progresses in ant colony optimization algorithm with applications[J].Computer Measurement & Control, 2003, 11(12):911~913. doi: 10.3321/j.issn:1671-4598.2003.12.001
[6]	Chialvo D R, Millonas M M.How swarms build cognitive maps[C]//Steels L.The biology and technology of intelligent autonomous agents.NATO ASI Series, 1995:439~450.
[7]	王树根, 杨耘, 林颖, 等.基于人工蚁群优化算法的遥感图像自动分类[J].计算机工程与应用, 2005, 29:77~80. doi: 10.3321/j.issn:1002-8331.2005.01.024 WANG Shu-gen, YANG Yun, LIN Ying, et al.Automatic classification of remotely sensed images based on artificial ant colony algorithm[J].Computer Engineering and Application, 2005, 29:77~80. doi: 10.3321/j.issn:1002-8331.2005.01.024