Pixel-level remote sensing image cloud region detection method based on coarse-grained label guided deep learning

Abstract

The invention is based on a practical problem that a cloud area detection method based on deep learning needs to spend a large annotation cost and discloses a pixel-level remote sensing image cloud region detection method based on coarse-grained label guided deep learning. The method comprises the following steps of firstly, training a deep network model with good robustness and generating a cloudactivation graph by utilizing an image block-level label which is relatively easy to obtain and combining a local pooling layer pruning strategy and a global convolution pooling layer under the constraint of a remote sensing image block data set of a coarse-grained image block-level label, and then obtaining a final cloud mask graph through threshold segmentation. The method is advantaged in thatthe labeling work can be greatly reduced, pixel-level accurate cloud region detection of the remote sensing image is realized, and efficiency and performance of cloud region detection of the remote sensing image can be effectively improved.

Description

technical field [0001] The invention belongs to the technical field of remote sensing and artificial intelligence, and relates to a remote sensing image cloud area detection method based on deep learning, in particular to a pixel-level remote sensing image cloud area detection method guided by coarse-grained labels and deep learning. Background technique [0002] Cloud detection is a key issue in remote sensing image interpretation and application. A large number of cloud cover will affect the availability of remote sensing image data and increase the difficulty of remote sensing image interpretation. Cloud area detection uses various methods in the field of remote sensing and computer vision to detect cloudy areas in remote sensing images. At the level of on-board applications, by not sending images with more clouds, it saves transmission bandwidth and storage space, and reduces resource consumption. Waste; at the level of ground applications, it provides data preparation f...

AI Technical Summary

Problems solved by technology

However, the superior performance of deep learning relies on a large number of accurate pixel-level labels, and the labeling work is quite time-consuming and labor-intensive

Considering that different types of satellites often have great differences in spectrum and spatial resolution, for each remote sensing satellite image, the method based on deep learning requires a corresponding pixel-level annotation data set to operate, which in turn requires a lot of manpower to label

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