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

Frontiers of Information Technology & Electronic Engineering 2017年第8期18卷 1108-1116页

作者： hamed bozorgi Ali JAFARI Electrical Engineering Department University of Guilan Rasht 41635-3756 Iran School of Electrical & Electronic Engineering Malek Ashtar University of Technology Tehran 15875-1774 Iran

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 di- mensionality 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.

关键词： Content-based image retrieval Feature point distribution Image registration Linear discriminant analysis Remotesensing Scale-invariant feature transform

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