Medium-low resolution remote sensing image enteromorpha coverage information fine extraction method

Abstract

The invention discloses a medium-low resolution remote sensing image-based enteromorpha coverage information fine extraction method. The method comprises the basic steps of screening and preprocessinga data source; calculating four vegetation indexes of the preprocessed image to obtain a grey-scale map of each vegetation index, and performing local adaptive threshold segmentation on each grey-scale map; calculating an initial enteromorpha coverage range; extracting an enteromorpha distribution rough range and spectral information thereof; constructing and training an enteromorpha prolifera extraction model; and extracting an enteromorpha prolifera coverage fine range. The method provided by the invention is scientific and reasonable, comprehensively considers the accuracy of extracting enteromorpha coverage information by a plurality of vegetation indexes, neural network sample balance and spectral space-time characteristics of enteromorpha pixels in medium-low resolution remote sensing images, and can improve the universality and accuracy of the enteromorpha coverage information extraction method to a certain extent.

Description

technical field [0001] The invention belongs to the technical field of marine remote sensing detection, and in particular relates to a method for finely extracting enteromorpha coverage information of medium and low resolution remote sensing images. Background technique [0002] In recent years, Enteromorpha disasters have occurred frequently in my country's sea areas, which not only leads to marine ecological imbalance, but also causes certain economic losses. However, the spatial distribution of Enteromorpha changes rapidly, and shipborne monitoring is difficult to meet the needs of quickly obtaining the development trend of Enteromorpha. Remote sensing technology has the advantages of wide range and multiple frequencies. Using satellite remote sensing to monitor Enteromorpha can quickly determine the location, time and density of Enteromorpha outbreaks. Low- and medium-resolution remote sensing image data has become the main data source in the application of remote sensi...

AI Technical Summary

Problems solved by technology

[0003] The existing remote sensing monitoring methods for Enteromorpha suitable for medium and low-resolution remote sensing images mainly include threshold method and classification method, etc. The threshold selection of the fixed threshold method is difficult; the single vegetation index of the adaptive threshold method is not suitable for all Enteromorpha distribution situations; The two-dimensional convolutional neural network with medium and low resolution remote sensing images of Enteromorpha as input has the problems of compression of the spectral feature dimension of Enteromorpha, insufficient learning of spatial features and unbalanced samples, which affect the extraction accuracy of Enteromorpha

Images