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Proceedings Paper

Framland parcels extraction from high-resolution remote sensing images based on the two-stage image classification
Author(s): Guoying Liu; Xu Song; Jing Lv
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Paper Abstract

It is difficult and boring for people to artificially extract farmland parcels from high resolution remote sensing images. Therefore, automatic methods are in the urgent need to release image interpreters from such a work as well as achieve accurate results. In the past years, although many researchers have made attempts to solve this problem by using different techniques and also produced some good results, they still cannot meet the demand of practical applications. In this paper, a farmland extraction method is proposed based on a new technique of two-stage image classification. The first stage aims at producing a map of farmland area by using the supervised iterative conditional mode (ICM), where a novel mixture posterior is proposed based on the tree-structured interpretation of certain complex landscapes, e.g., farmland and building area, and the Markov random field model (MRF) is also used to make use of spatial information between neighboring pixels. The second stage extracts the farmland parcels by using the Meanshift algorithm (MS) based on the hybrid of the original image and the texture image produced by the local binary pattern (LBP) method. We applied our method to a piece of aerial image in the urban area of Taizhou, China. The results show that the proposed method has an ability to produce more accurate results than the MS method.

Paper Details

Date Published: 14 December 2015
PDF: 6 pages
Proc. SPIE 9812, MIPPR 2015: Automatic Target Recognition and Navigation, 981211 (14 December 2015); doi: 10.1117/12.2209212
Show Author Affiliations
Guoying Liu, Anyang Normal Univ. (China)
Xu Song, Anyang Normal Univ. (China)
Jing Lv, Anyang Normal Univ. (China)

Published in SPIE Proceedings Vol. 9812:
MIPPR 2015: Automatic Target Recognition and Navigation
Nong Sang; Xinjian Chen, Editor(s)

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