Cultivated land change detection method and system under hierarchical multiple judgment rules

Abstract

This application proposes such a method and system for detecting changes in cultivated land under hierarchical multiple judgment rules, which may include: extracting the change mask image of the current remote sensing image, and performing raster vectorization processing on the change mask image to obtain the change vector Spots: Using the slow feature analysis change detection method, the current remote sensing images, historical remote sensing images and cultivated map spots are used to extract the global change information, and obtain the change probability mask image, in which the historical remote sensing images and cultivated map spots are existing inputs. data; and counting the pixel mean value of the corresponding position of the change vector map spot in the change probability mask image, using the mean value as the change probability value of the change vector map spot, so as to obtain the change vector map spot with the change probability value. This application solves the problems of high missed detection rate, low recall rate, and difficult detection of small targets in the prior art; it improves the credibility of the results; at the same time, it provides methods for the non-agricultural detection of cultivated land and the investigation of land change support.

Description

technical field [0001] The present application relates to the technical field of cultivated land change detection, in particular to a method and system for detecting cultivated land change under hierarchical multiple determination rules. Background technique [0002] In recent years, remote sensing technology has continued to improve, making the spatial resolution, time resolution and spectral resolution of the acquired images continue to increase, which can clearly reflect the detailed information of the ground objects and express the spatial structure characteristics of the target. Handling creates good conditions. However, more detailed information not only means greater processing capacity, but also makes the interval between the changed area and the unchanged area smaller, and it is more difficult to obtain the change detection result map; secondly, to obtain The data may be disturbed by light, man-made, sensors, etc., and traditional change detection methods require f...

AI Technical Summary

Problems solved by technology

However, there are problems such as high missed detection rate, low recall rate, and difficult detection of small targets. The algorithm is still in academia, and there is still a gap between the extraction results and production requirements. There is a lack of research on the detection of cultivated land changes.

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