@inproceedings{2d5a56d884fd43d2bba440aa271b3e34,
title = "Satellite image scene classification using spatial information",
abstract = "In order to enhance the local feature's describing capacity and improve the classification performance of high-resolution (HR) satellite images, we present an HR satellite image scene classification method that make use of spatial information of local feature. First, the spatial pyramid matching model (SPMM) is adopted to encode spatial information of local feature. Then, images are represented by the local feature descriptors and encoding information. Finally, the support vector machine (SVM) classifier is employed to classify image scenes. The experiment results on a real satellite image dataset show that our method can classify the scene classes with an 82.6% accuracy, which indicates that the method can work well on describing HR satellite images and classifying different scenes.",
keywords = "Satellite image, spatial information, spatial pyramid model, support vector machine",
author = "Weiwei Song and Dunwei Wen and Ke Wang and Tong Liu and Mujun Zang",
note = "Publisher Copyright: {\textcopyright} 2015 SPIE.; 6th International Conference on Graphic and Image Processing, ICGIP 2014 ; Conference date: 24-10-2014 Through 26-10-2014",
year = "2015",
doi = "10.1117/12.2178739",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
editor = "David Zhang and Yulin Wang and Xudong Jiang",
booktitle = "Sixth International Conference on Graphic and Image Processing, ICGIP 2014",
}