Abstract
Fusing hyperspectral and panchromatic remote sensing images can obtain the images with high resolution in both spectral and spatial domains. In addition, it can complement the deficiency of high-resolution hyperspectral and panchromatic remote sensing images. In this paper, a spectral–spatial residual network (SSRN) model is established for the intelligent fusion of hyperspectral and panchromatic remote sensing images. Firstly, the spectral–spatial deep feature branches are built to extract the representative spectral and spatial deep features, respectively. Secondly, an enhanced multi-scale residual network is established for the spatial deep feature branch. In addition, an enhanced residual network is established for the spectral deep feature branch This operation is adopted to enhance the spectral and spatial deep features. Finally, this method establishes the spectral–spatial deep feature simultaneity to circumvent the independence of spectral and spatial deep features. The proposed model was evaluated on three groups of real-world hyperspectral and panchromatic image datasets which are collected with a ZY-1E sensor and are located at Baiyangdian, Chaohu and Dianchi, respectively. The experimental results and quality evaluation values, including RMSE, SAM, SCC, spectral curve comparison, PSNR, SSIM ERGAS and Q metric, confirm the superior performance of the proposed model compared with the state-of-the-art methods, including AWLP, CNMF, GIHS, MTF_GLP, HPF and SFIM methods.
Highlights
With the successful launch of a variety of remote sensing satellites, remote sensing images with different spatial and spectral resolutions from multiple sources have been acquired [1]
The convolutional neural network (CNN) [43], which is an issue of the deep learning method, is adopted to establish a deep network fusion model for the intelligent fusion of hyperspectral and panchromatic images
Description of the hyperspectral, panchromatic and ground truth datasets used to investigate the effectiveness of the proposed spectral– spatial residual network (SSRN) method
Summary
With the successful launch of a variety of remote sensing satellites, remote sensing images with different spatial and spectral resolutions from multiple sources have been acquired [1]. Remote sensing image fusion aims at obtaining more accurate and richer information than any single image data. It generates composite image data with new spatial, spectral and temporal features from the complementary multi-source remote sensing image data in space, time and spectrum [3]. Due to the limited energy acquired by remote sensing image sensors, for the sake of maintaining high spectral resolution, the spatial resolution of hyperspectral remote sensing images is usually low. This type of data fusion process inputs and outputs raw data Data fusion at this level is conducted immediately after the data are gathered from sensors and based on signal methods. Data fusion at this level processes original pixels in raw image data collected from image sensors
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