Application of Deep Learning in Image Processing

Abstract

Deep learning has revolutionized computer vision, achieving remarkable success in complex visual tasks such as image classification, segmentation, and object detection. This thesis explores advanced deep learning techniques for image processing, with a particular focus on remote sensing imagery. Specifically, this research addresses two critical challenges: (1) achieving high segmentation accuracy in scenarios with limited labeled data and (2) integrating multi-modality data into model training.

To tackle these challenges, this work proposes innovative solutions leveraging semi-supervised learning and multi-modality learning. By employing consistency learning and advanced data augmentation techniques, the proposed approaches effectively utilize unlabeled data, significantly boosting segmentation accuracy.

Furthermore, integrating complementary data modalities, such as spectral and spatial information, enhances model robustness and overall performance. Experimental results on benchmark datasets validate the effectiveness of these methods, demonstrating their potential for real-world applications, including environmental monitoring, urban planning, and disaster management.

The primary contributions of this thesis include advancing the theoretical understanding of semi-supervised and multi-modality learning in remote sensing segmentation, developing novel methodologies to address data scarcity, and providing practical frameworks that are applicable across various domains. However, limitations related to scalability and generalizability highlight avenues for future research, such as exploring dynamic augmentation strategies, advanced fusion mechanisms, and extensions to other fields like medical imaging.

This research provides a comprehensive framework for overcoming segmentation challenges in remote sensing, delivering significant advancements in deep learning-based image analysis.