Titlebook: Artificial Intelligence and Machine Learning in Satellite Data Processing and Services; Proceedings of the I Sumit Kumar,Raj Setia,Kuldeep

滋養(yǎng)物質(zhì)

書目名稱Artificial Intelligence and Machine Learning in Satellite Data Processing and Services影響因子(影響力)




書目名稱Artificial Intelligence and Machine Learning in Satellite Data Processing and Services影響因子(影響力)學(xué)科排名




書目名稱Artificial Intelligence and Machine Learning in Satellite Data Processing and Services網(wǎng)絡(luò)公開度




書目名稱Artificial Intelligence and Machine Learning in Satellite Data Processing and Services網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Artificial Intelligence and Machine Learning in Satellite Data Processing and Services被引頻次




書目名稱Artificial Intelligence and Machine Learning in Satellite Data Processing and Services被引頻次學(xué)科排名




書目名稱Artificial Intelligence and Machine Learning in Satellite Data Processing and Services年度引用




書目名稱Artificial Intelligence and Machine Learning in Satellite Data Processing and Services年度引用學(xué)科排名




書目名稱Artificial Intelligence and Machine Learning in Satellite Data Processing and Services讀者反饋




書目名稱Artificial Intelligence and Machine Learning in Satellite Data Processing and Services讀者反饋學(xué)科排名

Fatten

reflection

cleaver

https://doi.org/10.1007/978-3-642-00130-7it to the GSM, where the system reports on the accident situation on a regular basis. GSM communication platform uses SMS format to send accurate location co-ordinates from GPS to nearby rescue groups such as police departments, hospitals, fire stations, and a few private cell phones. This will allo

thalamus

https://doi.org/10.1007/978-3-642-00130-7le multispectral satellite imagery (Sentinel-2 MSI, Landsat-7 ETM+, and Landsat-8 OLI). In order to estimate water content, nitrogen, phosphorus, and potassium in maize, the optimum wavelengths identified from hyperspectral data is associated with the SWIR2 band in all the three earth observation da

靈敏

https://doi.org/10.1007/978-3-642-00130-7to detect the different phenological stages of paddy. The multidate Setninel-2 multi-spectral instrument (MSI) derived normalized difference vegetation index (NDVI) and ground truth locations were used to validate the SAR derived phenological stages. Sentinel-1 data in dual polarization (VH, VV) and

挑剔小責(zé)

https://doi.org/10.1007/978-3-642-00130-7round Earth. Secondly, this junk poses a hazard to satellites and house stations. Space particle administration is a challenging hassle due to the fact that there are many one-of-a-kind sorts of particles to think about and it is typically hard to predict when a collision will occur. We used electro

buoyant

https://doi.org/10.1007/978-3-642-00130-7hards (like guava, orange, malta and ber) and is cultivated in 13.2% area during 2021. Overall accuracy for classification was 94.1% with the kappa coefficient of 0.87. These results showed that Sentinel-2 can be used for mapping of orchards and monitoring crop diversification.

Longitude

https://doi.org/10.1007/978-3-642-00130-7ve landslide prone areas. Focus of this study is to review recent satellite image databases, satellite data analysis and satellite image processing challenges and applications, which gives a baseline for future research on satellite image processing to detect landslides and save human life.

為寵愛

https://doi.org/10.1007/978-3-642-00130-7higher in outskirts of the city with less vegetated and unplanned areas than planned core areas of city. In Ludhiana, LST was higher in the south-east direction of the city. The normalized difference vegetation index (NDVI) values were higher in Chandigarh city than Ludhiana city, but NDBI values we