Abstract
Microwave radiometer data is affected by many factors during the imaging process, including the antenna pattern, system noise, and the curvature of the Earth. Existing deconvolution methods such as Wiener filtering handle this degradation problem in the Fourier domain. However, under complex degradation conditions, the Wiener filtering results are not accurate. In this paper, a convolutional neural network (CNN) model is proposed to solve the degradation problem. The deconvolution procedure is defined as a regression problem in the spatial domain that can be solved with deep learning. For the real inverse process of microwave radiometer data, the CNN model has a more powerful reconstruction ability than Wiener filtering due to the multi-layer structure of the CNN, which enables the multiple feature transform of the data. Additionally, the complex degradation factor during the imaging process of a microwave radiometer can be solved with general framework-based learning. Experimental results demonstrated that the CNN model gains about 5 dB at the peak signal-to-noise ratio compared to the Wiener filtering deconvolution method, and can better distinguish the measured data.
Highlights
Compared to visible light and infrared remote sensing, the advantages of microwave remote sensing technology include the ability to capture images regardless of weather and light conditions [1], the ability to penetrate clouds and vegetation, being not affected by meteorological conditions or the level of sunshine, and the ability to detect ground targets
This study presents the convolutional neural network (CNN) model for directly learning end-to-end mapping between the low- and high-resolution images
Deep learning was proposed to implement deconvolution techniques for radiometer features, whereas increasing the kernel size means increasing the number of parameters in the model
Summary
Compared to visible light and infrared remote sensing, the advantages of microwave remote sensing technology include the ability to capture images regardless of weather and light conditions [1], the ability to penetrate clouds and vegetation, being not affected by meteorological conditions or the level of sunshine, and the ability to detect ground targets. Microwave image data can provide information outside the infrared and the visible light image range. It plays an important role in weather monitoring and disaster prediction. Due to the limitations on antenna size, system noise, and scanning mode, the image data obtained by microwave radiometers have low spatial resolution. Improving the resolution of microwave radiometers using the deconvolution method is important. In Dong et al [24], deep learning was proposed for image super-resolution using convolutional neural networks. This study presents the CNN model for directly learning end-to-end mapping between the low- and high-resolution images. The image super-resolution method based on CNN has been studied [25,26]. CNN have achieved good results in image denoising [28,29]
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