Abstract
Stripe noise is considered one of the largest issues in space-borne remote sensing. The features of stripe noise in high-resolution remote sensing images are varied in different spatiotemporal conditions, leading to limited detection capability. In this study, we proposed a new detection algorithm (LSND: a linear stripe noise detection algorithm) considering stripe noise as a typical linear target. A large-scale stripe noise dataset for remote sensing images was created through linear transformations, and the target recognition of stripe noise was performed using deep convolutional neural networks. The experimental results showed that for sub-meter high-resolution remote sensing images such as GF-2 (GaoFen-2), our model achieved a precision of 98.7%, recall of 93.8%, F1-score of 96.1%, AP of 92.1%, and FPS of 35.71 for high resolution remote sensing images. Furthermore, our model exceeded ~40% on the accuracy and ~20% on the speed of the general models. Stripe noise detection would be helpful to detect the qualities of space-borne remote sensing and improve the quality of the images.
Highlights
IntroductionStripe noise seriously degrades image quality and adversely impacts the subsequent extraction and use of the image information
Stripe noise is a phenomenon that widely exists in space-borne imaging
(2) The cropped images randomly selected from each column were detected using the optimal-parameter model based on the LSND algorithm, and we recorded the detection results
Summary
Stripe noise seriously degrades image quality and adversely impacts the subsequent extraction and use of the image information It is caused by the different spectral responses of each charged-coupled device (CCD) in a spectrometer, errors in the calibration of the data system, an inconsistent response function of the sensor to the signal response area, or changes in the sensor’s response to the signal [1,2,3,4]. The brightness of a specific column or row of a remote sensing image is darker or brighter than the adjacent rows or columns of an area This phenomenon leads to blurred image features, and seriously influences data application (see Figure 1). Many researchers have reported the detection of stripe noise in high-resolution remote sensing images and used the detection results in fields such as destriping image-quality enhancement [5,6,7,8,9]
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