Abstract
Change detection (CD) is essential for accurate understanding of land surface changes with multitemporal Earth observation data. Due to the great advantages in spatial information modeling, Morphological Attribute Profiles (MAPs) are becoming increasingly popular for improving the recognition ability in CD applications. However, most of the MAPs-based CD methods are implemented by setting the scale parameters of Attribute Profiles (APs) manually and ignoring the uncertainty of change information from different sources. To address these issues, a novel method for CD in high-resolution remote sensing (HRRS) images based on morphological attribute profiles and decision fusion is proposed in this study. By establishing the objective function based on the minimum of average interscale correlation, a morphological attribute profile with adaptive scale parameters (ASP-MAPs) is presented to exploit the spatial structure information. On this basis, a multifeature decision fusion framework based on the Dempster–Shafer (D-S) theory is constructed for obtaining the CD map. Experiments of multitemporal HRRS images from different sensors have shown that the proposed method outperforms the other advanced comparison CD methods, and the overall accuracy (OA) can reach more than 83.9%.
Highlights
With the development of remote sensing system, change detection (CD) has attracted widespread interest as one of the most important applications in remote sensing [1]. e accurate processing and understanding of the changes of land covers is a significant issue in different applications pertaining human activities, such as dynamic monitoring of land use, vegetation health, and environment [2,3,4]. e wild use of the new generation of high-resolution sensors (e.g., IKONOS, QuickBird, and GF2) has further broadened the applications of CD technology [5]
The overall accuracy (OA) of the proposed method reached more than 83.9%, and the fluctuation range was less than 1.5%, which were significantly better than that of the comparison methods. erefore, among the challenges brought by the different data sources, the proposed method possessed advantages of high accuracy and stability
Among three change vector analysis (CVA)-based CD methods, Methods 1 and 2 only used spectral difference as the basis of CD and had weak ability in identifying false changes that were produced by insignificant detail changes; the false positive (FP) rate and false negative (FN) rates were over 30% and 20%, respectively
Summary
With the development of remote sensing system, change detection (CD) has attracted widespread interest as one of the most important applications in remote sensing [1]. e accurate processing and understanding of the changes of land covers is a significant issue in different applications pertaining human activities, such as dynamic monitoring of land use, vegetation health, and environment [2,3,4]. e wild use of the new generation of high-resolution sensors (e.g., IKONOS, QuickBird, and GF2) has further broadened the applications of CD technology [5]. With the development of remote sensing system, change detection (CD) has attracted widespread interest as one of the most important applications in remote sensing [1]. E accurate processing and understanding of the changes of land covers is a significant issue in different applications pertaining human activities, such as dynamic monitoring of land use, vegetation health, and environment [2,3,4]. Compared with mediumand low-resolution remote sensing images, a greater amount of spatial and thematic information of land covers is contained in high-resolution remote sensing (HRRS) images, which makes it feasible to recognize different types of complex structures within a scene [6]. In the current literature, supervised machine learning methods are most widely used for feature
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