Multi-modal remote sensing image data detection method and system

Abstract

The invention provides a multi-modal remote sensing image data detection method and system, electronic equipment and a storage medium, and the method comprises the steps: inputting multi-modal remote sensing image data of different time sequences into a multi-modal feature mining network, and outputting the fusion vector features of the multi-modal remote sensing image data of each time sequence, and inputting the plurality of fusion vector features into a change detection network, and identifying whether the multi-modal remote sensing image data with different time sequences have differences or not by the change detection network. The multi-modal feature mining network constructed by the invention is used for mining and fusing the features of the multi-modal remote sensing image data, so that the diversity, multi-time sequence and multi-feature requirements of a training data set are met, and the accuracy of network feature mining can be improved; and the abnormal states of the multi-modal remote sensing images with different time sequences are detected by constructing a multi-modal feature mining network and a change detection network, so that support is provided for researching the development trend of a detected area.

Description

technical field [0001] The present invention relates to the field of remote sensing image processing, and more specifically, to a multimodal remote sensing image data detection method and system. Background technique [0002] Remote sensing change detection technology has been widely used in urban planning, rural land rights confirmation, disaster early warning and other fields. It uses the visual transformation of monitoring bare soil and buildings to reflect the urban development status, and monitors the change of other plots to farmland plots to reflect high-standard farmland. Planning, monitoring changes of forest land parcels to soils in response to landslides. With the development of high-resolution remote sensing images, the existing optical satellite data are no longer sufficient to meet the timeliness of remote sensing ground monitoring. In the face of massive, multi-modal, high-resolution remote sensing datasets, traditional image interpretation techniques perform...

AI Technical Summary

Problems solved by technology

With the development of high-resolution remote sensing images, the existing optical satellite data are no longer sufficient to meet the timeliness of remote sensing ground monitoring

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