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Open AccessArticle

Land Cover Classification Using Integrated Spectral, Temporal, and Spatial Features Derived from Remotely Sensed Images

by Yongguang Zhai 1,2, Zhongyi Qu 1,* and Lei Hao 3
1
College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
2
Insitute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences, Beijing 100101, China
3
College of Forestry, Inner Mongolia Agricultural University, Hohhot 010018, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(3), 383; https://doi.org/10.3390/rs10030383
Received: 28 January 2018 / Revised: 25 February 2018 / Accepted: 27 February 2018 / Published: 1 March 2018
Obtaining accurate and timely land cover information is an important topic in many remote sensing applications. Using satellite image time series data should achieve high-accuracy land cover classification. However, most satellite image time-series classification methods do not fully exploit the available data for mining the effective features to identify different land cover types. Therefore, a classification method that can take full advantage of the rich information provided by time-series data to improve the accuracy of land cover classification is needed. In this paper, a novel method for time-series land cover classification using spectral, temporal, and spatial information at an annual scale was introduced. Based on all the available data from time-series remote sensing images, a refined nonlinear dimensionality reduction method was used to extract the spectral and temporal features, and a modified graph segmentation method was used to extract the spatial features. The proposed classification method was applied in three study areas with land cover complexity, including Illinois, South Dakota, and Texas. All the Landsat time series data in 2014 were used, and different study areas have different amounts of invalid data. A series of comparative experiments were conducted on the annual time-series images using training data generated from Cropland Data Layer. The results demonstrated higher overall and per-class classification accuracies and kappa index values using the proposed spectral-temporal-spatial method compared to spectral-temporal classification methods. We also discuss the implications of this study and possibilities for future applications and developments of the method. View Full-Text
Keywords: land cover; Landsat; multi-temporal; nonlinear dimensionality reduction; segmentation; spectral-temporal-spatial classification; validation land cover; Landsat; multi-temporal; nonlinear dimensionality reduction; segmentation; spectral-temporal-spatial classification; validation
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MDPI and ACS Style

Zhai, Y.; Qu, Z.; Hao, L. Land Cover Classification Using Integrated Spectral, Temporal, and Spatial Features Derived from Remotely Sensed Images. Remote Sens. 2018, 10, 383.

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