Pansharpening multispectral remote-sensing images withguided filter formonitoring impact of human behavior on environment

摘要

Human behavior would lead to a significant impact on the environment. By monitoring the environment, we can indirectly monitor human behavior. Remote sensing (RS) technology provides a large number of multispectral (MS) images. When combining the Internet of things (IoT) technology, those images can be used for human behavioral monitoring. However, due to the limitation of the optical sensors embedded in satellites, the spatial resolution of MS image is relatively low, which poses a huge problem for further understanding these images. Pansharpening, also known as multisensor image fusion, aims to sharp an MS image to a high-resolution multisensor image (HMS) by integrating a corresponding high-resolution panchromatic (PAN) image. By doing so, the redundancy among big data can be effectively reduced. Traditional Intensity-Hue-Saturation (IHS)-based methods often suffer from spectral distortion. To address this problem, a novel pansharpening method is proposed in this paper. Different from those traditional IHS methods, the proposed method first decomposes MS and PAN into high-frequency-component (HFC) and low-frequency-component (LFC), respectively. Then, the guided filter (GF) is utilized to enhance the spectral information on the detail map. Furthermore, the detail map is refined according to the adaptive coefficients for each band of MS. By performing experiments, we demonstrate the proposed method can obtain satisfying results in both visual quality and object assessment among existing methods.