Abstract
Remote sensing change detection (RSCD) is an important yet challenging task in Earth observation. The booming development of convolutional neural networks (CNNs) in computer vision raises new possibilities for RSCD, and many recent RSCD methods have introduced CNNs to achieve promising improvements in performance. In this paper we propose a novel multidirectional fusion and perception network for change detection in bi-temporal very-high-resolution remote sensing images. First, we propose an elaborate feature fusion module consisting of a multidirectional fusion pathway (MFP) and an adaptive weighted fusion (AWF) strategy for RSCD to boost the way that information propagates in the network. The MFP enhances the flexibility and diversity of information paths by creating extra top-down and shortcut-connection paths. The AWF strategy conducts weight recalibration for every fusion node to highlight salient feature maps and overcome semantic gaps between different features. Second, a novel perceptual similarity module is designed to introduce perceptual loss into the RSCD task, which adds perceptual information, such as structure and semantic information, for high-quality change map generation. Extensive experiments on four challenging benchmark datasets demonstrate the superiority of the proposed network compared with eight state-of-the-art methods in terms of F1, Kappa, and visual qualities.
Highlights
We can conclude that the proposed multidirectional fusion and perception network (MFPNet) achieves the best performance against other comparative methods, exhibiting the highest scores on both metrics
Due to the great challenge brought by this dataset, all the algorithms perform poorly for small object change detection, but our proposed MFPNet still outperforms the best methods by a small margin
We proposed a novel deep learning network (MFPNet) for Remote sensing change detection (RSCD)
Summary
Remote sensing change detection (RSCD) aims to identify important changes, such as water-body variations, building developments, and road changes, between images acquired over the same geographical area but taken at distinct times. It has a wide range of applications in natural disaster assessment, urban planning, resource management, deforestation monitoring, etc. With the development of Earth observation technologies, very-high-resolution (VHR) remote sensing images from various sensors (e.g., WorldView, QuickBird, GaoFen, and high-definition imaging devices on airplanes) are increasingly available, which has created new demands on RSCD algorithms [10,11]. To quickly and robustly obtain the required change information from massive VHR images, deep learning methods, especially convolutional neural networks (CNNs) with powerful deep feature representation and image problem modeling abilities, have attracted significant research interest [11,12,13]
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