Paddy rice automatic change detection method integrating pixels and objects

Abstract

The invention relates to a paddy rice automatic change detection method integrating pixels and objects. The method comprises the following steps of establishing an MODIS-based water body index time series data set and MODIS water body index time series curves; calculating the similarity of the MODIS water body index time series curves in the starting and ending years pixel by pixel; judging a suspected change region based on the similarity of the MODIS water body index time series curves; obtaining an image object through image segmentation by integrating multi-phase Landsat images in the starting year; superposing the suspected change region with the image object to obtain a suspected change object of a research region; further subdividing the suspected change object of the research region by integrating multi-phase Landsat images in the ending year; and performing paddy rice charting of the starting and ending years on the subdivided suspected change object of the research region by utilizing Landsat remote sensing image characteristics so as to obtain a paddy rice dynamic change distribution diagram of the research region in the starting and ending years. The method has the characteristics of high automation degree, simplicity, easiness in use, good robustness, high classification precision and the like.

Description

technical field [0001] The invention relates to a rice automatic change detection method integrating pixel and object. Background technique [0002] In the field of remote sensing application research, the early research results mainly focused on the monitoring of land use and its change. Since the 21st century, with the continuous improvement of the temporal and spatial resolution of remote sensing images, the depth and breadth of remote sensing applications have continued to expand. In recent years, research on crop monitoring using remote sensing images has attracted much attention. However, due to the spectral similarity between different crops and the spectral inconsistency caused by various factors such as phenological changes and variety differences of the same crop in different regions, remote sensing monitoring of crops is facing challenges. Conventional remote sensing monitoring methods are difficult to meet the actual needs of real-time and rapid automatic monit...

AI Technical Summary

Problems solved by technology

If the object-oriented classification method is applied to time-series remote sensing images, it will be difficult to deal with the problem

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