DeepUNet: A Deep Fully Convolutional Network for Pixel-level Sea-Land Segmentation (1709.00201v1)

Abstract: Semantic segmentation is a fundamental research in remote sensing image processing. Because of the complex maritime environment, the sea-land segmentation is a challenging task. Although the neural network has achieved excellent performance in semantic segmentation in the last years, there are a few of works using CNN for sea-land segmentation and the results could be further improved. This paper proposes a novel deep convolution neural network named DeepUNet. Like the U-Net, its structure has a contracting path and an expansive path to get high resolution output. But differently, the DeepUNet uses DownBlocks instead of convolution layers in the contracting path and uses UpBlock in the expansive path. The two novel blocks bring two new connections that are U-connection and Plus connection. They are promoted to get more precise segmentation results. To verify our network architecture, we made a new challenging sea-land dataset and compare the DeepUNet on it with the SegNet and the U-Net. Experimental results show that DeepUNet achieved good performance compared with other architectures, especially in high-resolution remote sensing imagery.

• The paper presents DeepUNet, a novel network architecture designed to enhance pixel-level sea-land segmentation in remote sensing imagery.
• It introduces innovative DownBlocks, UpBlocks, U-connections, and Plus connections that effectively integrate high-resolution features for improved accuracy.
• Empirical results show DeepUNet outperforms U-Net and SegNet, achieving precision metrics above 98% on a dedicated high-resolution sea-land dataset.

DeepUNet: Advancements in Sea-Land Segmentation for Remote Sensing

The paper presents a novel architecture named DeepUNet, a deep fully convolutional network designed explicitly for pixel-level segmentation in sea-land satellite imagery. Sea-land segmentation is pivotal for numerous applications in remote sensing, including maritime safety and ship detection. Given the intricacies and semantic diversity associated with maritime environments, existing methods have faced challenges in achieving precise segmentation. The proposed DeepUNet architecture intends to improve upon current models by offering a more robust structure designed to handle high-resolution images with intricate details.

Technical Contributions and Architecture

DeepUNet builds upon the foundational U-Net structure but introduces significant enhancements tailored for high-resolution remote sensing imagery. The architecture incorporates two innovative components: DownBlocks and UpBlocks, substituting traditional convolutional layers. These blocks are interconnected by two novel types of connections named U-connections and Plus connections, which facilitate the seamless combination of high-resolution features from contracting paths with expansive path outputs. This design aims to enhance the segmentation precision by enabling deeper learning, thus addressing the loss of detail that occurs in existing convolutional networks during downsampling.

In developing DeepUNet, the authors created a novel dataset specifically for testing sea-land segmentation algorithms. This dataset encompasses high-resolution imagery captured from various coastal and wharf locations, providing a rigorous testing ground for proposed segmentation models.

Comparative Analysis and Results

The empirical evaluation involves a comparative analysis of DeepUNet against preceding architectures such as SegNet and U-Net on the newly compiled dataset. DeepUNet demonstrates superior performance, particularly in handling high-resolution imagery, by observing precision, recall, and F1-measure as key performance indicators. Notably, DeepUNet achieves a land precision (LP) of 98.58%, a land recall (LR) of 98.91%, an overall precision (OP) of 99.04%, and an F1-measure of 98.74%, outperforming both U-Net and SegNet. These results highlight its capability to accurately distinguish intricate boundaries and small geographical features, which often pose segmentation challenges in remote sensing applications.

Theoretical and Practical Implications

The development of DeepUNet represents a significant step forward in the application of deep learning to remote sensing image processing. The network's advanced architecture can considerably reduce misclassification rates, especially in images containing complex semantic content. From a theoretical standpoint, the introduction of novel connection types—U-connection and Plus connection—demonstrates innovative ways to address the vanishing gradient problem in deep neural networks, allowing them to better capture and integrate both local and global features.

Practically, the enhanced accuracy and reliability of DeepUNet offer valuable implications for maritime monitoring and management, including improving the accuracy of ship detection and classification. Furthermore, its efficient architecture allows for the potential overlay of additional tasks, paving the way for further integration of multi-task learning approaches in future research.

Future Directions

The paper suggests further exploration into integrating multi-task learning techniques with DeepUNet's architecture to augment its segmentation accuracy and versatility. This integration could potentially enhance performance across various remote sensing tasks, providing a holistic solution for complex environmental imagery.

Overall, the DeepUNet model advances the field of semantic segmentation in remote sensing images by resolving challenges associated with high-resolution image processing, offering significant improvements over traditional convolutional network architectures. This paper contributes a substantial foundation upon which more sophisticated and comprehensive segmentation models can be developed.