Middle-season rice information decision tree classification method based on multi-temporal data feature extraction

Abstract

According to a middle-season rice information decision tree classification method based on multi-temporal data feature extraction of the invention, selected GF-1 image data has the advantages of high spatial resolution and high temporal resolution. On the basis, a variety of characteristic parameters for rice distribution extraction based on a single-temporal image are used, the advantages of timing analysis based on a multi-temporal image are utilized, multiple parameters and multiple temporal phases are combined organically, and the distribution of middle-season rice is extracted by means of knowledge decision tree classification. Through use of a variety of characteristic parameters, non-target surface features can be eliminated better. Multi-temporal analysis is conducive to the elimination of wrongly-classified surface features caused by 'different surface features, same spectrum' and the extraction of target surface features. Decision tree classification has the characteristics of being flexible, visual, efficient, and the like. Therefore, by integrating all the advantages, the precision of middle-season rice extraction is further improved. The method is of positive significance both to the food security system of a country and to the commercial application of remote sensing in agriculture.

Description

technical field [0001] The invention relates to the field of surveying and mapping remote sensing, in particular to a middle rice information decision tree classification method based on feature extraction from multi-temporal data. Background technique [0002] Rice is one of the world's three major food crops, and its sown area accounts for 15% of the world's total arable land. my country is the world's largest rice producer, with a vast area of ​​rice cultivation, ranging from Hainan Province in the south, to Heilongjiang Province in the north, to Taiwan Province in the east, and to the Xinjiang Uygur Autonomous Region in the west. Different regions have different climates, and the varieties of rice planted are also different. According to the planting time, it can be divided into three categories: early rice, middle rice, and late rice. Different regions have different growth periods for early rice, middle rice, and late rice. Taking Hubei Province as an example, double...

AI Technical Summary

Problems solved by technology

However, the revisit cycle of TM image data is long, and it is difficult to obtain TM time-series images of rice in different periods, especially cloudy and rainy weather can easily have a negative impact on image quality

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