A remote sensing image-based inland water body blue-green algae classification and recognition method

Abstract

The invention discloses a remote sensing image-based inland water body blue-green algae classification and recognition method, which comprises the following steps of 1) obtaining an optical remote sensing data source image, and processing the obtained data source image to obtain a pre-processed optical image capable of being recognized; 2) performing radiation calibration and atmospheric correction on the identifiable pre-processed optical image obtained in the step 1); and 3) establishing a deep neural network for extracting blue-green algae information of the water body by using the image obtained in the step 2) after over-radiation calibration and atmospheric correction, and identifying the blue-green algae information through the deep neural network. According to the method, the remotesensing image is introduced into monitoring of the blue-green algae, so that the problem of high cost, long time consumption and the like due to limited sampling frequency in a traditional method formanually collecting a water sample on site for water quality analysis are solved. The remote sensing image has the characteristics of high spatial coverage and high time resolution, and can well detect the lake blue-green algae bloom.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a remote sensing image-based classification and identification method for inland water cyanobacteria. Background technique [0002] LANDSAT US land series satellites are satellites that mainly transmit optical images. The main tasks are to investigate underground mineral deposits, marine resources and groundwater resources, monitor and assist in the management of agriculture, forestry, animal husbandry and water conservancy. The growth and landform of natural plants, inspect and forecast various serious natural disasters (such as earthquakes) and environmental pollution, take images of various targets, and draw various thematic maps (such as geological maps, landform maps, hydrological maps), etc. [0003] The Sentinel-2A satellite launched by the European Space Agency (ESA, European Space Agency) carries a multispectral imager, which can cover 13 spectral bands with a w...

AI Technical Summary

Problems solved by technology

[0006] The traditional monitoring of cyanobacteria mainly focuses on the use of land water environment monitoring stations. Although this method can monitor the situation of cyanobacteria in lake water, it is limited by manpower. This method cannot accurately monitor lake cyanobacteria in a large area in a timely manner.

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