Feature enhancement method for bare land in remote sensing images

Computer Engineering and Applications ›› 2012, Vol. 48 ›› Issue (32): 196-199.

Previous Articles Next Articles

Feature enhancement method for bare land in remote sensing images

QIU Yonghong1, TAN Yongzhong2, ZHOU Guohua1

1.College of Resources and Environment Science, Hunan Normal University, Changsha 410081, China
2.Department of Land Management, College of Public Administration, Zhejiang University, Hangzhou 310027, China

Online:2012-11-11 Published:2012-11-20

一种遥感影像裸土地特征增强方法

邱永红1，谭永忠2，周国华1

1.湖南师范大学资源与环境科学学院，长沙 410081
2.浙江大学公共管理学院土地管理系，杭州 310027

Abstract

Abstract: It’s difficult for feature enhancement algorithm available to highlight bare land in a given band. It presents a new feature enhancement method for bare land in multi-spectral remote sensing images. The image is partitioned coarsely by spectral distance, and bare land and non-bare-land pixel samples are obtained. The image is transformed with the whitening matrix form non-bare-land pixel samples. The whitened image is rotated with the principal components transformation matrix from its bare land pixel samples. One procedure for doing this is discussed, to validate the method, an example is presented.

Key words: remote sensing, bare land, feature extraction, whitening, threshold segmentation

摘要： 针对常用特征增强算法难以将裸土地在特定波段突出出来的问题，提出了一种适用于多光谱遥感影像的裸土地特征增强算法。基本思路是采用根据光谱距离进行预分割，估计裸土地和非裸土地样本，针对非裸土地样本进行白化处理，基于白化数据中的裸土地样本进行主分量变换，裸土地信息将被集中并突出于变换结果的第一分量。对算法的具体流程进行了探讨，并通过一个实例进行了验证。

关键词: 遥感, 裸土地, 特征提取, 白化处理, 阈值分割

QIU Yonghong1, TAN Yongzhong2, ZHOU Guohua1. Feature enhancement method for bare land in remote sensing images[J]. Computer Engineering and Applications, 2012, 48(32): 196-199.

邱永红1，谭永忠2，周国华1. 一种遥感影像裸土地特征增强方法[J]. 计算机工程与应用, 2012, 48(32): 196-199.

[1]	BAO Zhiqiang, XING Yu, LYU Shaoqing, HUANG Qiongdan. Improved YOLO V2 6D Object Pose Estimation Algorithm [J]. Computer Engineering and Applications, 2021, 57(9): 148-153.
[2]	ZHANG Zhentong, SHAN Yugang, YUAN Jie. Remote Sensing Image Detection Algorithm Combining Multi-scale and Attention Mechanism [J]. Computer Engineering and Applications, 2021, 57(9): 212-216.
[3]	XU Degang, WANG Lu, LI Fan. Review of Typical Object Detection Algorithms for Deep Learning [J]. Computer Engineering and Applications, 2021, 57(8): 10-25.
[4]	HU Wentao, CHEN Xiuhong. Low-Rank Projection Learning Based on Neighbor Graph [J]. Computer Engineering and Applications, 2021, 57(7): 209-214.
[5]	ZHANG Xiaoli, ZHANG Kuixing, JIANG Mei, WEI Benzheng, CONG Jinyu. Review of Image Classification Technology for Lymphoma [J]. Computer Engineering and Applications, 2021, 57(6): 1-9.
[6]	WEI Wei, YANG Ru, ZHU Ye. Target Detection of Improved CenterNet to Remote Sensing Images [J]. Computer Engineering and Applications, 2021, 57(6): 191-199.
[7]	SHE Hailong, XIE Shanjuan, ZOU Jingjie. 3D-CNN with Standard Score Dimensionality Reduction for Hyperspectral Remote Sensing Images Classification [J]. Computer Engineering and Applications, 2021, 57(4): 169-175.
[8]	HU Jie, ZHANG Ying, XIE Shiyi. Summary of Research Progress on Application of Domestic Remote Sensing Image Classification Technology [J]. Computer Engineering and Applications, 2021, 57(3): 1-13.
[9]	XIONG Jian, QIN Renchao, HE Mengyi, LIU Jianlan, TANG Fengyang. Application of Improved Random Forest Algorithm in Android Malware Detection [J]. Computer Engineering and Applications, 2021, 57(3): 130-136.
[10]	LI Daidong, HE Xiaohui, LI Panle, TIAN Zhihui, ZHOU Guangsheng. Road Extraction Network of Remote Sensing Image Based on SPUD-ResNet [J]. Computer Engineering and Applications, 2021, 57(23): 240-247.
[11]	XIE Junzhang, PENG Hui, TANG Jianfeng, HOU Yichen, ZENG Qingxi. Improved YOLOv4 for Dense Remote Sensing Target Detection [J]. Computer Engineering and Applications, 2021, 57(22): 247-256.
[12]	QIN Weiwei, SONG Tainian, LIU Jieyu, WANG Hongwei, LIANG Zhuo. Remote Sensing Military Target Detection Algorithm Based on Lightweight YOLOv3 [J]. Computer Engineering and Applications, 2021, 57(21): 263-269.
[13]	WANG Jianjun, WEI Jiang, MEI Shaohui, WANG Jian. Improved YOLOv3 for Small Object Detection in Remote Sensing Images [J]. Computer Engineering and Applications, 2021, 57(20): 133-141.
[14]	LI Longlong, HE Dongjian, WANG Meili. Study of Plant Leaf Image Recognition Based on Improved Local Binary Pattern Algorithm [J]. Computer Engineering and Applications, 2021, 57(19): 228-234.
[15]	LI Jie, LI Miao, YUAN Xiguo. Detection Algorithm?of Pathogenic Microbes from Next-Generation Sequencing Data [J]. Computer Engineering and Applications, 2021, 57(19): 282-289.

Feature enhancement method for bare land in remote sensing images

一种遥感影像裸土地特征增强方法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics