Characteristic image time series synthetic method and apparatus based on multisource moderate resolution

Abstract

The invention discloses a characteristic image time series synthetic method based on multisource moderate resolution. The method comprises the following steps: acquiring remote sensing images by using GF1-WFV, HJ1-A / B and Landsat separately to form a multisource moderate resolution remote sensing image; preprocessing the multisource moderate resolution remote sensing image and outputting a radiation-normalized time series characteristic image; extracting a cloud cover area in the time series characteristic image, dividing a cloudless time series characteristic image, outputting a characteristic image time series empty tile set; extracting single crop growth curves of a plurality of kinds of crops from the time series characteristic image acquired by MODIS to generate a multi-crop growth curve library; adding the time series empty tile set and the single crop growth curves to the radiation-normalized time series characteristic image to perform time-phase normalization, and constructing a characteristic image time series tile se. The method completes the construction of a characteristic image time series with a large range, a long time series, and a large data volume.

Description

technical field [0001] The present invention relates to the technical field of remote sensing image processing, and more specifically, the present invention relates to a method and device for synthesizing time series of characteristic images based on multi-source middle division. Background technique [0002] Crop planting area is an important basis for formulating grain production policies and determining the quantity of grain trade, and is the basis for the adjustment of agricultural planting structure. The growth of crops has its own laws, and the same crop has a relatively stable growth and development law in the same area. According to the differences in time and biomass in the development process of different crops, combined with some phenological information, time can be used. Sequence remote sensing images classify and identify different crops, so as to achieve the purpose of extracting crop planting information. In this context, the time series data of remote sensi...

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Problems solved by technology

Such a time interval will cause the characteristic index to fail to reflect the crop growth status in the same period, and generally cause significant differences in crop growth in different regions of the study area.

In addition, since the resolution of feature images has been significantly improved compared with traditional time series data, the increase in data volume is also huge, especially large-scale (provincial) time series data will inevitably cause a sharp increase in data volume. Taking Jiangxi Province as an example, using HJ satellite data to cover the entire area in a single phase requires 6.5G of data for 5 scenes, and the crop growth period is set as three months. At least 9 periods of data are required to construct a ten-day time series during the growth period. When synthesizing according to the traditional band combination method, at least 100G or more characteristic image time-phase sequence data will be generated. Ordinary stand-alone machines cannot perform follow-up analysis and calculation on such a huge amount of data.

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