Remote sensing image solid waste identification method and system based on multi-strategy enhancement

Abstract

The invention provides a remote sensing image solid waste identification method and system based on multi-strategy enhancement, and the method comprises the steps: cutting an original remote sensing image according to the input original remote sensing image and a preset overlapping ratio, and storing the coordinate position information of the left upper corner point of each cut remote sensing image block in the whole remote sensing image; putting the remote sensing image blocks into a deep learning target detection network for remote sensing image solid waste recognition to be recognized, and recognizing a frame of the solid waste, wherein the deep learning target detection network for remote sensing image solid waste recognition adopts a multi-strategy enhanced key point target recognition network guided by position information; screening the detected frame to obtain a final frame identification result, and displaying the final frame identification result in the original remote sensing image. The scheme provided by the invention can be effectively applied to the high-resolution remote sensing image, so that the solid waste in the remote sensing image can be quickly detected.

Description

technical field [0001] The invention belongs to the technical field of remote sensing information processing, and relates to a solid waste detection method, in particular to a multi-strategy enhanced key point recognition network guided by position information, which is used for the recognition method of solid waste in large-scale high-resolution remote sensing images . Background technique [0002] Solid waste is a problem that cannot be ignored in the development of China's urbanization process. Solid waste is a major management issue that poses enormous economic, social and environmental risks. However, due to the large randomness and wide distribution of solid waste distribution points, the management of solid waste has brought great difficulties. Therefore, it is very necessary to realize the rapid identification of solid waste and improve the identification efficiency of solid waste. [0003] There are mainly two methods for existing solid waste, one is based on the...

AI Technical Summary

Problems solved by technology

However, deep learning requires a large amount of data for training, and the amount of solid waste under remote sensing images is very scarce, which makes the construction of solid waste datasets an important issue

At the same time, the characteristics of solid waste under remote sensing images are very diverse. There is no fixed shape and color, and there are only uniform characteristics of messy accumulation, which makes it difficult to build a solid waste identification network.

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