Improving Land Use/Land Cover Classification by Integrating Pixel Unmixing and Decision Tree Methods

Chao Yang,Tiezhu Shi,Guofeng Wu,Jinliang Wang,Qingquan Li,Kai Ding

doi:10.3390/rs9121222

Chao Yang, Tiezhu Shi + Show 4 more

Open Access

https://doi.org/10.3390/rs9121222

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Journal: Remote Sensing	Publication Date: Nov 27, 2017
Citations: 55	License type: CC BY 4.0

Affiliation: Shenzhen University, Yunnan Normal University

Abstract

Decision tree classification is one of the most efficient methods for obtaining land use/land cover (LULC) information from remotely sensed imageries. However, traditional decision tree classification methods cannot effectively eliminate the influence of mixed pixels. This study aimed to integrate pixel unmixing and decision tree to improve LULC classification by removing mixed pixel influence. The abundance and minimum noise fraction (MNF) results that were obtained from mixed pixel decomposition were added to decision tree multi-features using a three-dimensional (3D) Terrain model, which was created using an image fusion digital elevation model (DEM), to select training samples (ROIs), and improve ROI separability. A Landsat-8 OLI image of the Yunlong Reservoir Basin in Kunming was used to test this proposed method. Study results showed that the Kappa coefficient and the overall accuracy of integrated pixel unmixing and decision tree method increased by 0.093% and 10%, respectively, as compared with the original decision tree method. This proposed method could effectively eliminate the influence of mixed pixels and improve the accuracy in complex LULC classifications.

Highlights

Land use/land cover (LULC) information is urgently required for policy making for it provides vital inputs for various developmental, environmental and resource planning applications, as well as regional and global scale process modeling [1,2]
Artificial Neural Network (ANN) [4,5,6], Support Vector Machine (SVM) [7,8], and Fuzzy classification methods [9,10,11], which are based on image spectral characteristics, cannot take multi-features (such as Digital Elevation Model (DEM), spectral information, Iterative Self-organizing Data Analysis Technique (ISODATA) result, Minimum Noise Fraction (MNF) result, and abundance) into account, and their complex algorithms may lead to low classification efficiency
We proposed a new training sample selection method using a 3D terrain that was created by OLI image fusion with digital elevation model (DEM) to select ROIs, which departs from the traditional method based on a two-dimensional image

Summary

Introduction

Land use/land cover (LULC) information is urgently required for policy making for it provides vital inputs for various developmental, environmental and resource planning applications, as well as regional and global scale process modeling [1,2]. Remote sensing classification is an important way to extract LULC information, and the selection of classification methods is a key factor influencing its accuracy. Artificial Neural Network (ANN) [4,5,6], Support Vector Machine (SVM) [7,8], and Fuzzy classification methods [9,10,11], which are based on image spectral characteristics, cannot take multi-features (such as Digital Elevation Model (DEM), spectral information, Iterative Self-organizing Data Analysis Technique (ISODATA) result, Minimum Noise Fraction (MNF) result, and abundance) into account, and their complex algorithms may lead to low classification efficiency. Object-oriented classification delineates objects from remote sensing images by obtaining a variety of additional spatial and textural information, which is important for improving the accuracy of remote sensing classification [12,13]; for low resolution imagery or fragmented landscapes and complex terrain, its classification accuracy is much lower [14]

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