Abstract
At present, the classification accuracy of high-resolution Remote Sensing Image Scene Classification (RSISC) has reached a quite high level on standard datasets. However, when coming to practical application, the intrinsic noise of satellite sensors and the disturbance of atmospheric environment often degrade real Remote Sensing (RS) images. It introduces defects to them, which affects the performance and reduces the robustness of RSISC methods. Moreover, due to the restriction of memory and power consumption, the methods also need a small number of parameters and fast computing speed to be implemented on small portable systems such as unmanned aerial vehicles. In this paper, a Lightweight Progressive Inpainting Network (LPIN) and a novel combined approach of LPIN and the existing RSISC methods are proposed to improve the robustness of RSISC tasks and satisfy the requirement of methods on portable systems. The defects in real RS images are inpainted by LPIN to provide a purified input for classification. With the combined approach, the classification accuracy on RS images with defects can be improved to the original level of those without defects. The LPIN is designed on the consideration of lightweight model. Measures are adopted to ensure a high gradient transmission efficiency while reducing the number of network parameters. Multiple loss functions are used to get reasonable and realistic inpainting results. Extensive tests of image inpainting of LPIN and classification tests with the combined approach on NWPU-RESISC45, UC Merced Land-Use and AID datasets are carried out which indicate that the LPIN achieves a state-of-the-art inpainting quality with less parameters and a faster inpainting speed. Furthermore, the combined approach keeps the comparable classification accuracy level on RS images with defects as that without defects, which will improve the robustness of high-resolution RSISC tasks.
Highlights
Introduction published maps and institutional affilRemote Sensing (RS) images are widely used in earth science, agriculture, military reconnaissance, disaster rescue and many other fields
Scene classification tests are carried out to verify the robustness improvement of the RS images scene classification (RSISC) tasks with the proposed method
The Ground Truth (GT) images and the corresponding images with defects are sent to six existing RSISC methods and the proposed combined methods respectively to test the classification accuracy
Summary
Remote Sensing (RS) images are widely used in earth science, agriculture, military reconnaissance, disaster rescue and many other fields. To fully understand and utilize the rich information of the earth surface contained in RS images, the task of RS images scene classification (RSISC) has become a research hotspot. Most existing RSISC methods [1] can be roughly divided into two categories according to their approaches to feature designing and extracting. One is the traditional machine learning-based methods with hand-crafted features, such as models based on Bag of Visual Words (BoVW) [2], Randomized Spatial. As deep learning technology has been proved to have excellent performance in computer vision and pattern recognition [6,7], the classification methods based on Convolutional Neural. Network (CNN) [8–19] have been widely investigated for they can learn and extract image features automatically. Hu et al [9] and Du et al [10] used a pretrained CNN to extract iations
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