Multi-season crop automatic recognition method based on time sequential remote sensing image

Abstract

The invention relates to the technical field of remote sensing image information processing and discloses a multi-season crop automatic recognition method based on a time sequential remote sensing image. The method is based on a time sequential remote sensing image, continuous wavelet transform is utilized, a wavelet coefficient spectrum of a time domain and frequency domain is obtained, a characteristic spectrum is generated from the wavelet coefficient spectrum, characteristics lines used for distinguishing vegetation relative preference and deviation period are extracted, and through tracking the value domain distribution of relative preference point variables in an indication growth period in different scaling intervals, the multi-season crop information is effectively extracted. According to the method, the vegetation dynamic change characteristic can be effectively extracted from dimensions of time and frequency, the vegetation change characteristic in low frequency in a relative preference period is extracted for automatic recognition of a multi-season crop, and the method is independent of prior knowledge basically and has the characteristics of good robustness, high classification accuracy, high degree of automation and strong anti-interference ability.

Description

technical field [0001] The invention relates to the technical field of remote sensing image information processing, in particular to an automatic recognition method for multi-season crops based on time-series remote sensing images. Background technique [0002] Agriculture is the foundation of the national economy, and crops, as the foundation of agriculture, play a pivotal role in ensuring food security and social stability. The current situation facing my country's grain production is: on the one hand, according to the characteristics of China's large population and small cultivated land, increasing the multiple cropping index according to local conditions is an effective way to expand the sown area of ​​crops, tap the potential of cultivated land utilization and increase the total yield of crops; on the other hand , under the current background that the rural labor force continues to transfer to the city and farmers are not motivated to grow grain, increasing the multi-cro...

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Problems solved by technology

However, its shortcomings are also obvious, which are reflected in the following aspects: (1) The vegetation index datasets based on MODIS or SPOT remote sensing platforms are affected by noise to a certain extent, resulting in very low peaks and valleys detected by such methods. It may not be caused by the growth or withering of vegetation; (2) Due to the growth characteristics of different crops and the influence of other non-agricultural crops such as winter weeds, the small growth peaks are not in one-to-one correspondence with crops, such as winter wheat. The two growth peaks of "big" and southern China are relatively warm and humid, and grass is generally grown even in winter slack; (3) The research method is relatively simple and not robust enough, and it is easily disturbed by non-crop growth peaks caused by the above reasons The effect of local minima or local maxima caused by

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