CM-Unet: A Novel Remote Sensing Image Segmentation Method Based on Improved U-Net

Abstract

Semantic segmentation is an active research area for high-resolution (HR) remote sensing image processing. Most existing algorithms are better at segmenting different features. However, for complex scenes, many algorithms have insufficient segmentation accuracy. In this study, we propose a new method CM-Unet based on the U-Net framework to address the problems of holes, omissions, and fuzzy edge segmentation. First, we add the channel attention mechanism in the encoding network and the residual module to transmit information. Second, a multi-feature fusion mechanism is proposed in the decoding network, and an improved sub-pixel convolution method replaces the traditional upsampling operation. We conducted simulation experiments on the Potsdam, Vaihingen and GID datasets. The experimental results show that the proposed CM-Unet required segmentation time is approximately 62 ms/piece, the MIoU is 90.4% and the floating point operations (FLOPs) is 20.95 MFLOPs. Compared with U-Net, CM-Unet only increased the total number of parameters and floating point operations slightly, but achieved the best segmentation effect compared with the other models. CM-Unet can segment remote sensing images efficiently and accurately owing to its lower time consumption and space requirements; the precision of the segmentation results is better than other methods.

A Multi-Modality Fusion and Gated Multi-Filter U-Net for Water Area Segmentation in Remote Sensing

“…U-Net consists of an encoder (down-sampling path) and a decoder (up-sampling path) connected by skip connections. Due to its simple structure, U-Net can achieve high accuracy with limited training samples, which makes it and its series of extensions widely used in various image segmentation tasks, including remote sensing image segmentation [15,16]. DeepLab series [17][18][19] are another series of CNN-based models for image semantic segmentation that have been proposed and iteratively improved by the Google team.…”