Crop-Planting Area Prediction from Multi-Source Gaofen Satellite Images Using a Novel Deep Learning Model: A Case Study of Yangling District

Neural network models play an important role in crop extraction based on remote sensing data. However, when dealing with high-dimensional remote sensing data, these models are susceptible to performance degradation. In order to address the challenges associated with multi-source Gaofen satellite dat...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Remote sensing (Basel, Switzerland) Ročník 15; číslo 15; s. 3792
Hlavní autoři:	Kuang, Xiaofei, Guo, Jiao, Bai, Jingyuan, Geng, Hongsuo, Wang, Hui
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 01.08.2023
Témata:	Accuracy Agriculture Artificial intelligence Artificial neural networks Calibration case studies characteristic dimensionality reduction Classification Comparative analysis corn crop-planting structure acquisition Crops Datasets decision support systems Decision trees Deep learning Distribution Effectiveness Greenhouses hyperspectral imagery Hyperspectral imaging Identification and classification image analysis Image classification Machine learning multi-source remote sensing multispectral Neural networks Performance degradation Planting precision agriculture prediction Reduction Remote sensing Satellite imagery Satellite imaging Satellites Support vector machines Technology application Wheat China
ISSN:	2072-4292, 2072-4292
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Buďte první, kdo okomentuje tento záznam!