面向地球观测数据分类的开放源码支持向量机图像检索与相关反馈系统

Alexandru-Cosmin Grivei
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引用次数: 0

摘要

对地观测图像采集量的不断增加,需要新的弱监督分类和图像检索算法。在本文中,我们提出了SVMIRE (SVM图像检索与相关反馈)的架构,这是一个灵活的,模块化的,快速的数据挖掘系统,基于相关反馈的方法,提高了支持向量机(SVM)分类器的性能。该系统具有存储和重用所获得的分类模型和结果的能力。SVMIRE系统的功能在两个数据集上进行了测试:一个Landsat 8 OLI/TIRS(作战陆地成像仪/热红外传感器)和一个Sentinel-2 MSI(多光谱仪器)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SVMIRE - An Open Source SVM Image Retrieval with Relevance Feedback System For Earth Observation Data Classification
The continuous increase of Earth Observation image acquisitions requires new weakly supervised algorithms for classification and image retrieval. In this paper, we present the architecture of SVMIRE (SVM Image REtrieval with Relevance Feedback) which is a flexible, modular, and fast data mining system based on a relevance feedback approach that increases the performance of the Support Vector Machine (SVM) classifiers. The proposed system has the capability of storing and reusing the obtained classification model, and results. The functionalities of the SVMIRE system are tested on two datasets: one Landsat 8 OLI/TIRS (Operational Land Imager/Thermal Infrared Sensor) and one Sentinel-2 MSI (Multi-Spectral Instrument).
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