Pansharpening Based on Joint-Guided Detail Extraction

Abstract

Pansharpening is the process of fusing low spatial resolution multispectral (MS) images with high spatial resolution panchromatic (PAN) images, so as to obtain high spatial resolution multispectral (HRMS) images. In this study, a new pansharpening method based on a joint-guided detail extraction is proposed to maintain the spectral and spatial fidelity of a pansharpened image. First, to obtain details that are highly correlated with an MS image, a new PAN image is constructed and guided by the intensity component of the MS image through a variational model. The construction of the new PAN image improves the correlation between the PAN and MS images, and thus reduces the spectral distortion. The variational model is rapidly solved using the least-squares method. Second, to obtain accurate details from the new PAN image, the extraction of the details is guided by each band of the MS image through a regression model, which can further reduce the spatial distortion. The regression model is effectively solved using the gradient descend method. Finally, the details are injected into the up-sampled MS image to obtain a fused image. Numerous experiments on the proposed approach were conducted and the results were compared with previous state-of-the-art pansharpening methods. The experimental results verify that the proposed method can efficiently achieve high-quality HRMS images.

Spatial and Spectral-Channel Attention Network for Denoising on Hyperspectral Remote Sensing Image

“…With the improvements of hardware and convolutional neural networks (CNN) in image processing applications, deep learning (DL)-based methods represent the emerging methods of image processing [36][37][38][39][40][41][42][43][44][45][46][47][48][49]. Denoising methods [36,37] for modeling HSIs by 2D convolutions are effective, but sacrifice the ability of the model to extract GCS knowledge and flexibility with spectral dimensions.…”

Pansharpening scheme using spatial detail injection–based convolutional neural networks

“…A method based on a joint-guided detail extraction can reduce spectral and spatial distortion with a little computational burden. Here, variational model is designed to obtain a new PAN image guided by the MS image and improve the correlation [31].…”

Pansharpening Based on Variational Fractional-Order Geometry Model and Optimized Injection Gains

“…In recent years, CS-and MRA-based methods have generally been defined and realized by injection models. In particular, the CS-based injection model is used extensively owing to its high spatial quality and efficiency [30]. However, it is difficult to obtain satisfactory spectral quality with this model [28].…”

“…However, two deficiencies remain that may affect the fusion quality. The first is that the low correlation between the PAN and MS images may cause spectral distortion [30]. The second is that vegetated and non-vegetated regions are rarely differentiated when determining the injection gains, which will cause spatial and spectral distortion [33].…”