Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Display Omitted Obtain positive wavelet isolines from wavelet spectra.Derive CI from number of strong bright centers and skeleton width.Handle complex intra-class variability through time and frequency.Guarantee great generality, comparability and consistency.Applicable to VI time series from either coarse or high images. Timely and accurate monitoring of cropping intensity (CI) is essential to help us understand changes in food production.  aims to develop an automatic Cropping Intensity extraction method based on the Isolines of Wavelet Spectra (CIIWS) with consideration of intra-class variability. The CIIWS method involves the following procedures: (1) characterizing vegetation dynamics from time-frequency dimensions through a continuous wavelet transform performed on vegetation index temporal profiles; (2) deriving three main features, the skeleton width, maximum number of strong brightness centers and the intersection of their scale intervals, through computing a series of wavelet isolines from the wavelet spectra; and (3) developing an automatic cropping intensity classifier based on these three features. The proposed CIIWS method improves the understanding in the spectral-temporal properties of vegetation dynamic processes. To test its efficiency, the CIIWS method is applied to China's Henan province using 250m 8days composite Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time series datasets. An overall accuracy of 88.9% is achieved when compared with in-situ observation data. The mapping result is also evaluated with 30m Chinese Environmental Disaster Reduction Satellite (HJ-1)-derived data and an overall accuracy of 86.7% is obtained. At county level, the MODIS-derived sown areas and agricultural statistical data are ll correlated (r2=0.85). The merit and uniqueness of the CIIWS method is the ability to cope with the complex intra-class variability through continuous wavelet transform and efficient feature extraction based on wavelet isolines. As an objective and meaningful algorithm, it guarantees easy applications and greatly contributes to satellite observations of vegetation dynamics and food security efforts.
['Bingwen Qiu', 'Zhuangzhuang Wang', 'Zhenghong Tang', 'Chongcheng Chen', 'Zhanling Fan', 'Weijiao Li']
I am writing a research paper on a idea Automated cropping intensity extraction from isolines of wavelet spectra
[{"from": "human", "value": "I am writing a research paper on a idea Automated cropping intensity extraction from isolines of wavelet spectra"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Display Omitted Obtain positive wavelet isolines from wavelet spectra.Derive CI from number of strong bright centers and skeleton width.Handle complex intra-class variability through time and frequency.Guarantee great generality, comparability and consistency.Applicable to VI time series from either coarse or high images. Timely and accurate monitoring of cropping intensity (CI) is essential to help us understand changes in food production.  aims to develop an automatic Cropping Intensity extraction method based on the Isolines of Wavelet Spectra (CIIWS) with consideration of intra-class variability. The CIIWS method involves the following procedures: (1) characterizing vegetation dynamics from time-frequency dimensions through a continuous wavelet transform performed on vegetation index temporal profiles; (2) deriving three main features, the skeleton width, maximum number of strong brightness centers and the intersection of their scale intervals, through computing a series of wavelet isolines from the wavelet spectra; and (3) developing an automatic cropping intensity classifier based on these three features. The proposed CIIWS method improves the understanding in the spectral-temporal properties of vegetation dynamic processes. To test its efficiency, the CIIWS method is applied to China's Henan province using 250m 8days composite Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time series datasets. An overall accuracy of 88.9% is achieved when compared with in-situ observation data. The mapping result is also evaluated with 30m Chinese Environmental Disaster Reduction Satellite (HJ-1)-derived data and an overall accuracy of 86.7% is obtained. At county level, the MODIS-derived sown areas and agricultural statistical data are ll correlated (r2=0.85). The merit and uniqueness of the CIIWS method is the ability to cope with the complex intra-class variability through continuous wavelet transform and efficient feature extraction based on wavelet isolines. As an objective and meaningful algorithm, it guarantees easy applications and greatly contributes to satellite observations of vegetation dynamics and food security efforts."}]