Fast uniform content-based satellite image registration using the scale-invariant feature transform descriptor

Hamed BOZORGI; Ali JAFARI

doi:10.1631/FITEE.1500295

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Fast uniform content-based satellite image registration using the scale-invariant feature transform descriptor

Regular Papers | Updated：2022-05-19

- Fast uniform content-based satellite image registration using the scale-invariant feature transform descriptor
- 一种快速均匀的采用尺度不变特征变换描述符进行基于内容的卫星图像配准方法
- Frontiers of Information Technology & Electronic Engineering Vol. 18, Issue 8, Pages: 1108-1116(2017)
- Affiliations：
  
  Electrical Engineering Department, University of Guilan, Rasht 41635-3756, Iran
  School of Electrical & Electronic Engineering, Malek Ashtar University of Technology, Tehran 15875-1774, Iran
- Author bio：
  
  Ali JAFARI, E-mail: iustuser@aut.ac.ir
- Funds：
- DOI：10.1631/FITEE.1500295
  CLC： TP391
- Received：05 September 2015，
  
  Revised：2017-;8-;1，
  
  Published：2017-08
- Accepted：
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Hamed BOZORGI, Ali JAFARI. Fast uniform content-based satellite image registration using the scale-invariant feature transform descriptor[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(8): 1108-1116.
DOI：

Hamed BOZORGI, Ali JAFARI. Fast uniform content-based satellite image registration using the scale-invariant feature transform descriptor[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(8): 1108-1116. DOI： 10.1631/FITEE.1500295.

摘要

基于内容的卫星图像配准是在遥感和图像处理领域的一大难题。受照度、旋转、来源差异的影响，该问题在多源遥感图像匹配中更为突出。尺度不变特征变换（scale-invariant feature transform

SIFT）算法是一种成功应用于卫星图像配准的算法。本地SIFT描述符被许多研究者应用于改进图像检索流程。尽管SIFT算法具有良好的稳定性，它在提取多源遥感中本地特征点的质量和数量上仍然具有一定的劣势。另外，SIFT算法提取的本地特征具有较高维度，导致计算过程耗时过长以及对保存相关信息的储存空间要求过高，而这两点也是在基于内容图像检索（content-based image retrieval

CBIR）的相关应用中的重要因素。本文介绍了一种在多源遥感中将本地SIFT特征转变为全局特征的新方法。通过在预处理阶段对图像进行对比度均衡化来提升SIFT本地特征点质量和数量。将参考数据库中每副图像的本地特征单独分为一类后，采用线性判别分析（linear discriminant analysis

LDA）方法将本地SIFT特征转变为全局特征，同时不为降低特征空间的维度。该方法可以显著减少计算时间和所需存储空间。将核函数应用于检定数据并映射，所测试特征点的检索率高达91.67%。

Abstract

Content-based satellite image registration is a difficult issue in the fields of remote sensing and image processing. The difficulty is more significant in the case of matching multisource remote sensing images which suffer from illumination

rotation

and source differences. The scale-invariant feature transform (SIFT) algorithm has been used successfully in satellite image registration problems. Also

many researchers have applied a local SIFT descriptor to improve the image retrieval process. Despite its robustness

this algorithm has some difficulties with the quality and quantity of the extracted local feature points in multisource remote sensing. Furthermore

high dimensionality of the local features extracted by SIFT results in time-consuming computational processes alongside high storage requirements for saving the relevant information

which are important factors in content-based image retrieval (CBIR) applications. In this paper

a novel method is introduced to transform the local SIFT features to global features for multisource remote sensing. The quality and quantity of SIFT local features have been enhanced by applying contrast equalization on images in a pre-processing stage. Considering the local features of each image in the reference database as a separate class

linear discriminant analysis (LDA) is used to transform the local features to global features while reducing dimensionality of the feature space. This will also significantly reduce the computational time and storage required. Applying the trained kernel on verification data and mapping them showed a successful retrieval rate of 91.67% for test feature points.

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