Cloud detection of high-resolution remote sensing image based on improved U-Net

Yin, MeiJie; Wang, Peng; Hao, Weilong; Ni, Chuanfa

doi:10.1007/s11042-023-14655-z

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…Recently, there has been a lot of study conducted on the development of computerbased automatic cloud segmentation techniques as a prerequisite for picture analysis [6][7][8]. Existing cloud detection methods can be divided into two categories: empirical-rule algorithms based on physical features, and machine-learning algorithms [9].…”

Section: Introductionmentioning

confidence: 99%

Effect of Bit Depth on Cloud Segmentation of Remote-Sensing Images

Liao

Liu

2023

Remote Sensing

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Due to the cloud coverage of remote-sensing images, the ground object information will be attenuated or even lost, and the texture and spectral information of the image will be changed at the same time. Accurately detecting clouds from remote-sensing images is of great significance to the field of remote sensing. Cloud detection utilizes semantic segmentation to classify remote-sensing images at the pixel level. However, previous studies have focused on the improvement of algorithm performance, and little attention has been paid to the impact of bit depth of remote-sensing images on cloud detection. In this paper, the deep semantic segmentation algorithm UNet is taken as an example, and a set of widely used cloud labeling dataset “L8 Biome” is used as the verification data to explore the relationship between bit depth and segmentation accuracy on different surface landscapes when the algorithm is used for cloud detection. The research results show that when the image is normalized, the effect of cloud detection with a 16-bit remote-sensing image is slightly better than that of an 8-bit remote sensing image; when the image is not normalized, the gap will be widened. However, using 16-bit remote-sensing images for training will take longer. This means data selection and classification do not always need to follow the highest possible bit depth when doing cloud detection but should consider the balance of efficiency and accuracy.

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Section: Introductionmentioning

confidence: 99%

Effect of Bit Depth on Cloud Segmentation of Remote-Sensing Images

Liao

Liu

2023

Remote Sensing

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“…Compared to object detection that provides bounding box information, semantic segmentation generates denser predictions and richer information, such as accurate boundary information of objects in the image. Therefore, semantic segmentation has great potential for applications in fields such as autonomous driving [2], medical image segmentation [3][4][5] and remote sensing image analysis [6][7][8], where fine-grained information about the scene is needed to enable computers to understand the environment.…”

Section: Introductionmentioning

confidence: 99%

Mix-net: A Dual-Branch Lightweight Semantic Segmentation Network Based on MRF

Tan

Zhao

Cai

et al. 2023

Preprint

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To enable image segmentation tasks to go beyond microcomputers and similar devices and better adapt to the limited computing and storage capabilities of mobile devices, we have proposed a lightweight semantic segmentation network called Mix-net. Firstly, we designed a Mix Convolution (MC) module for feature extraction and recovery, which significantly reduces the number of parameters in the model while maintaining accuracy. Secondly, to enhance the edge details of various semantics in the images, we simultaneously train two network models. One model focuses on learning the main body details of the images, while the other model is specifically designed for segmentation tasks and is more dedicated to capturing the edges of different semantics. Thirdly, we designed a novel activation function called Mix Rectified Function (MRF), which effectively enhances the accuracy of segmentation tasks and is more suitable for lightweight image segmentation tasks. Additionally, we have employed Group Normalization (GN) to complement MRF for normalization and activation operations, enabling higher accuracy for small-batch segmentation tasks. Finally, our proposed network retains skip-connection structures and incorporates channel shuffling and attention modules to enhance accuracy. We have conducted comparative experiments on the Cityscapes dataset, where Mix-net achieved an Mean Intersection over Union (MIoU) of 81.1% and a smaller model size, utilizing only 1.47M Parameters and 9.93G Floating Point Operations (FLOPs) on a single NVIDIA GeForce RTX3060 graphics card.

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