Next Article in Journal
Automatic Kernel Size Determination for Deep Neural Networks Based Hyperspectral Image Classification
Next Article in Special Issue
Detecting Vegetation Change in Response to Confining Elephants in Forests Using MODIS Time-Series and BFAST
Previous Article in Journal
Comparison of Machine Learning Techniques in Inferring Phytoplankton Size Classes
Previous Article in Special Issue
Assessing Different Feature Sets’ Effects on Land Cover Classification in Complex Surface-Mined Landscapes by ZiYuan-3 Satellite Imagery
Open AccessArticle

Improvement of Moderate Resolution Land Use and Land Cover Classification by Introducing Adjacent Region Features

by Longlong Yu 1,2,*, Jinhe Su 1,2, Chun Li 1,2, Le Wang 1,2, Ze Luo 1 and Baoping Yan 1
1
Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
2
University of Chinese Academy of Sciences, Beijing 100190, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(3), 414; https://doi.org/10.3390/rs10030414
Received: 22 December 2017 / Revised: 12 February 2018 / Accepted: 7 March 2018 / Published: 8 March 2018
(This article belongs to the Special Issue GIS and Remote Sensing advances in Land Change Science)
Landsat-like moderate resolution remote sensing images are widely used in land use and land cover (LULC) classification. Limited by coarser resolutions, most of the traditional LULC classifications that are based on moderate resolution remote sensing images focus on the spectral features of a single pixel. Inspired by the spatial evaluation methods in landscape ecology, this study proposed a new method to extract neighborhood characteristics around a pixel for moderate resolution images. 3 landscape-metric-like indexes, i.e., mean index, standard deviation index, and distance weighted value index, were defined as adjacent region features to include the surrounding environmental characteristics. The effects of the adjacent region features and the different feature set configurations on improving the LULC classification were evaluated by a series of well-controlled LULC classification experiments using K nearest neighbor (KNN) and support vector machine (SVM) classifiers on a Landsat 8 Operational Land Imager (OLI) image. When the adjacent region features were added, the overall accuracies of both the classifiers were higher than when only spectral features were used. For the KNN and SVM classifiers that used only spectral features, the overall accuracies of the LULC classification were 85.45% and 88.87%, respectively, and the accuracies were improved to 94.52% and 96.97%. The classification accuracies of all the LULC types improved. Highly heterogeneous LULC types that are easily misclassified achieved greater improvements. As comparisons, the grey-level co-occurrence matrix (GLCM) and convolutional neural network (CNN) approaches were also implemented on the same dataset. The results revealed that the new method outperformed GLCM and CNN approaches and can significantly improve the classification performance that is based on moderate resolution data. View Full-Text
Keywords: land use and land cover; classification; scale; adjacent region feature; remote sensing; landscape ecology land use and land cover; classification; scale; adjacent region feature; remote sensing; landscape ecology
Show Figures

Graphical abstract

MDPI and ACS Style

Yu, L.; Su, J.; Li, C.; Wang, L.; Luo, Z.; Yan, B. Improvement of Moderate Resolution Land Use and Land Cover Classification by Introducing Adjacent Region Features. Remote Sens. 2018, 10, 414.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop