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基于多尺度质心轮廓距离的二维形状傅里叶描述子及其在遥感图像目标识别中的应用。

A Fourier Descriptor of 2D Shapes Based on Multiscale Centroid Contour Distances Used in Object Recognition in Remote Sensing Images.

机构信息

Institute of Intelligent Control and Image Engineering, Xidian University, Taibai Road, Xi'an 710071, China.

出版信息

Sensors (Basel). 2019 Jan 24;19(3):486. doi: 10.3390/s19030486.

Abstract

A shape descriptor is an effective tool for describing the shape feature of an object in remote sensing images. Researchers have put forward a lot of excellent descriptors. The discriminability of some descriptors is very strong in the experiments, but usually their computational cost is large, which makes them unsuitable to be used in practical applications. This paper proposes a new descriptor-FMSCCD (Fourier descriptor based on multiscale centroid contour distance)-which is a frequency domain descriptor based on the CCD (centroid contour distance) method, multiscale description, and Fourier transform. The principle of FMSCCD is simple, and the computational cost is very low. What is commendable is that its discriminability is still strong, and its compatibility with other features is also great. Experiments on three databases demonstrate its strong discriminability and operational efficiency.

摘要

形状描述符是一种用于描述遥感图像中目标形状特征的有效工具。研究人员已经提出了许多优秀的描述符。在实验中,一些描述符的可辨别性非常强,但通常它们的计算成本很大,这使得它们不适合实际应用。本文提出了一种新的描述符——FMSCCD(基于多尺度质心轮廓距离的傅里叶描述符),它是一种基于 CCD(质心轮廓距离)方法、多尺度描述和傅里叶变换的频域描述符。FMSCCD 的原理简单,计算成本非常低。值得称赞的是,它的可辨别性仍然很强,与其他特征的兼容性也很好。在三个数据库上的实验证明了它具有很强的可辨别性和运算效率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/427e/6387127/f973ac09a80f/sensors-19-00486-g001.jpg

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