Abstract
Land cover change detection plays an important role in natural disaster monitoring, tracking urban expansion, and many social benefit areas. The spectral-based direct comparison (SDC) methods are commonly used for change detection, but such methods are vulnerable to the influence of external factors. In general, land changes among different land cover types have different characters of change magnitude. The class probability-based direct comparison (CPDC) methods consider land type information and reduce the influence of external factors, but these methods are strongly dependent on the training samples. To address the above problems, we proposed a novel change detection method that integrates spectral values and class probabilities (SVCP). First, a new change magnitude map based on spectral values and class probabilities is constructed by using the maximum interclass variance and Gaussian mixture model (GMM), which greatly enhances the differentiation between changed and unchanged areas. Second, the Kapur threshold selection method is improved by using the variance of the changed and unchanged areas as well as the class probabilities for adaptive thresholding. The SVCP approach was assessed by two case studies from Landsat 8 Operational Land Imager (OLI) images. The “change/no-change” detection and “from-to” change types were evaluated. The experimental results indicated that the SVCP method is more accurate in the change detection, with lower false and missed detection rates than the traditional methods.
Highlights
Multi-temporal remote sensing-based change detection extracts land change information from remote sensing images of the same study area at different times [1]–[3]
The detection results are evaluated by four evaluation indexes of overall accuracy (OA), kappa coefficient (κ), omission rate (OR), and commission rate (CR)
The main contribution of our work is that we combine spectral values and class probabilities in change magnitude calculation and change threshold selection
Summary
Multi-temporal remote sensing-based change detection extracts land change information (i.e., change area and change type) from remote sensing images of the same study area at different times [1]–[3]. During the last three decades, a number of change detection methods have been proposed for addressing different land cover change requirements, and new techniques continue to be developed for improving change detection results [7], [8]. The deep learning-based change detection methods learn change features from remote sensing images and use the change features to segment the images so as to obtain the change detection results, which could provide a superior performance in various change detection tasks [11]. Most of these methods require very large training datasets, longer training time and accurate manual annotation [12]. The traditional change detection methods still receive
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