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[null,"1.","Record Nr.",null,"UNINA9910639984703321"]
[null,null,"Autore",null,"Jeon Gwanggil"]
[null,null,"Titolo",null,"Artificial Intelligence-Based Learning Approaches for Remote Sensing"]
[null,null,"Pubbl/distr/stampa",null,"Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022"]
[null,null,"ISBN",null,"3-0365-6084-X"]
[null,null,"Descrizione fisica",null,"1 electronic resource (382 p.)"]
[null,null,"Soggetti",null,"Technology: general issues"]
[{"$nulls":4},"History of engineering & technology"]
[{"$nulls":4},"Environmental science, engineering & technology"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Sommario/riassunto","The reprint focuses on artificial intelligence-based learning approaches and their applications in remote sensing fields. The explosive development of machine learning, deep learning approaches and its wide applications in signal processing have been witnessed in remote sensing. The new developments in remote sensing have led to a high resolution monitoring of ground on a global scale, giving a huge amount of ground observation data. Thus, artificial intelligence-based deep learning approaches and its applied signal processing are required for remote sensing. These approaches can be universal or specific tools of artificial intelligence, including well known neural networks, regression methods, decision trees, etc. It is worth compiling the various cutting-edge techniques and reporting on their promising applications."]