Abstract
Through the continued development of technology, applying deep learning to remote sensing scene classification tasks is quite mature. The keys to effective deep learning model training are model architecture, training strategies, and image quality. From previous studies of the author using explainable artificial intelligence (XAI), image cases that have been incorrectly classified can be improved when the model has adequate capacity to correct the classification after manual image quality correction; however, the manual image quality correction process takes a significant amount of time. Therefore, this research integrates technologies such as noise reduction, sharpening, partial color area equalization, and color channel adjustment to evaluate a set of automated strategies for enhancing image quality. These methods can enhance details, light and shadow, color, and other image features, which are beneficial for extracting image features from the deep learning model to further improve the classification efficiency. In this study, we demonstrate that the proposed image quality enhancement strategy and deep learning techniques can effectively improve the scene classification performance of remote sensing images and outperform previous state-of-the-art approaches.
Highlights
Published: 8 December 2021With the continuous development of science and technology, the image quality achieved by air cameras, mobile phones, and other camera equipment has progressively improved
When remote sensing images are used for deep learning model training, the proper categorization of scenes is an important step in understanding the classification of remote sensing images
With 32 GB random access memory, and the NVIDIA GeForce GTX3060 12G graphics card is used for the deep learning model training
Summary
Published: 8 December 2021With the continuous development of science and technology, the image quality achieved by air cameras, mobile phones, and other camera equipment has progressively improved. When remote sensing images are used for deep learning model training, the proper categorization of scenes is an important step in understanding the classification of remote sensing images. Remote sensing image scene classification tasks can be applied in various fields [1,2] such as disaster prevention and relief, smart city planning, land covering, and land change detection. For the improvement of the image data, the use of various methods of enhancement processing may provide a higher level of image characteristics and assist in reinforcing the image features of the neural network model in the subsequent phase. Most of the existing methods are to directly crop the original image into the model training. It is difficult to achieve good results with the scene classification task.
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