首页|New Machine Learning Study Findings Have Been Reported by Researchers at Chinese Academy of Sciences (Multi-Temporal Sentinel-1 and Sentinel-2 Data for Orchards Discrimination in Khairpur District, Pakistan Using Spectral Separability Analy sis ...)
New Machine Learning Study Findings Have Been Reported by Researchers at Chinese Academy of Sciences (Multi-Temporal Sentinel-1 and Sentinel-2 Data for Orchards Discrimination in Khairpur District, Pakistan Using Spectral Separability Analy sis ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on artificial intelligenc e is the subject of a new report. According to news reporting originating from B eijing, People's Republic of China, by NewsRx correspondents, research stated, " Generating orchards spatial distribution maps within a heterogeneous landscape i s challenging and requires fine spatial and temporal resolution images." Financial supporters for this research include National Natural Science Foundati on of China. Our news correspondents obtained a quote from the research from Chinese Academy of Sciences: "This study examines the effectiveness of Sentinel-1 (S1) and Senti nel-2 (S2) satellite data of relatively high spatial and temporal resolutions fo r discriminating major orchards in the Khairpur district of the Sindh province, Pakistan using machine learning methods such as random forest (RF) and a support vector machine. A Multicollinearity test (MCT) was performed among the multi-te mporal S1 and S2 variables to remove those with high correlations. Six different feature combination schemes were tested, with the fusion of multi-temporal S1 a nd S2 (scheme-6) outperforming all other combination schemes. The spectral separ ability between orchards pairs was assessed using Jeffries-Matusita (JM) distanc e, revealing that orchard pairs were completely separable in the multi-temporal fusion of both sensors, especially the indistinguishable pair of dates-mango. Th e performance difference between RF and SVM was not significant, SVM showed a sl ightly higher accuracy, except for scheme-4 where RF performed better. This stud y concludes that multi-temporal fusion of S1 and S2 data, coupled with robust ML methods, offers a reliable approach for orchard classification."
Chinese Academy of SciencesBeijingPe ople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
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