Cyanobacterial bloom remote sensing monitoring method based on planktonic algae indexes and deep learning

Abstract

The invention discloses a cyanobacterial bloom remote sensing monitoring method based on a planktonic algae index and deep learning. The method comprises the steps: (1) extracting and recognizing cyanobacterial bloom in a research region through employing Landsat satellite data and a planktonic algae index (FAI); (2) utilizing a series of data enhancement methods such as rotation, translation, zooming and color transformation to operate cyanobacterial bloom data extracted by a floating algae index (FAI); (3) performing operations such as cutting, resampling and the like on the cyanobacterial bloom data after data enhancement to make a data set suitable for deep learning; (4) adopting a Deeplab deep learning model, and taking cyanobacterial bloom data as a training set for training; and (5)outputting a classification result taking land, a water area and cyanobacterial bloom as main categories. By adopting the method, the cyanobacterial bloom can be accurately and efficiently extractedand identified from the satellite image.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a remote sensing monitoring method for cyanobacteria blooms based on planktonic algae index and deep learning. Background technique [0002] Eutrophication of water body has always been one of the important factors that pollute the water environment of lakes, which will cause a large number of algae such as blue-green algae to grow on the surface of the water body. In recent years, with the development of social economy and population growth, the impact of human activities on lakes has been increasing, resulting in the continuous decline of water quality in lakes everywhere, and the trend of eutrophication of water bodies is obvious, especially in lakes near urban areas. This phenomenon is very serious. Algae blooms of different degrees have occurred in lakes all over the country, such as Taihu Lake and Dianchi Lake. Therefore, it is particularly important t...

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Problems solved by technology

[0003] At present, there have been many studies on the monitoring of cyanobacterial blooms through remote sensing technology, but there are still various problems such as time-consuming and labor-intensive, heavy workload, insufficient accuracy, low resolution, and poor effectiveness.

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