Integrating Image Quality Enhancement Methods and Deep Learning Techniques for Remote Sensing Scene Classification

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.

Pansharpening Low-Altitude Multispectral Images of Potato Plants Using a Generative Adversarial Network

“…The multispectral images (7)(8)(9)(10)(11)(12)(13)(14) represent a color composite derived from spectral bands (3)(4)(5)(6), where UN MS ( 7) is derived from the unprocessed spectral bands and the multispectral images (8)(9)(10)(11)(12)(13)(14) are the output of various preprocessed spectral bands. Similarly, the pansharpened images (15)(16)(17)(18)(19)(20)(21)(22) were generated with PanColorGAN by fusing the unprocessed and preprocessed PAN (1) and the multispectral images (7)(8)(9)(10)(11)(12)(13)(14).…”

“…On the other hand, MS WF (8) and MS TV (10) provided a blurry appearance. The PS images (15)(16)(17)(18) obtained the structure of the MS images. The PS images were chromatically more natural, and the structure and edges of the leaves appear sharper.…”

“…The PS images were chromatically more natural, and the structure and edges of the leaves appear sharper. The PS WF ( 16) images visually exhibit more blur than the UN PS (15), while PS USM ( 17) had sharp features which can be seen prominently in the leaf edges. Conversely, the CLAHE-preprocessed MS images (11)(12)(13)(14) suffered from chromatic distortion, resulting in undesirable color artifacts at the image edges.…”

“…Therefore, preprocessing and improving remotely sensed images are some of the essential preliminary tasks for automated plant stress detection. The study of Hung et al (2021) [15] indicates that image enhancement provides a beneficial influence on classification tasks.…”

GA-based weighted ensemble learning for multi-label aerial image classification using convolutional neural networks and vision transformers

“…The scene was originally a combination of multiple objects, environments, and semantics in the image. With technological development, many investigators have used scenes as basic analysis units in recent years, and supervised deep learning models have made considerable progress in remote sensing image scene classification tasks [4,5,[9][10][11]. Xu et al [12] developed a multi-embedding contrastive learning framework for remote sensing image classification.…”