Abstract
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.
Highlights
Mapping land cover distribution and monitoring its dynamics have been identified as an important goal in environmental studies [1,2,3,4,5]
Obtaining accurate and timely land cover maps is a difficult problem in remote sensing
The STS classification method provided the anticipated classification results in this study, the computation required for the Laplacian Eigenmaps (LE)-dynamic time warping (DTW) method in the STS system increases geometrically with the spatial dimensions of the image
Summary
Mapping land cover distribution and monitoring its dynamics have been identified as an important goal in environmental studies [1,2,3,4,5]. It is necessary to reduce the dimension of multi-spectral time series prior to land cover classification, for methods that use full-band satellite image time series as input [15,39,40]. We investigated the potential for extracting spectral-temporal and spatial features from satellite time-series data to reliably classify different land cover categories. 2018, 10, 383 problem of multi-spectral time series using all the available data, and to determine how to extract and combine the spatial characteristics of time-series land cover classifications. We developed a new automated time-series land cover classification method based on extracting spectral-temporal and spatial features using all the available data This methodology is applicable to all of satellite time series but is illustrated using Landsat time series data in this study. A classification system based on spatial regularization is established to generate land cover map using spectral-temporal and spatial features
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