文章基本信息

标题：CaneSat dataset to leverage convolutional neural networks for sugarcane classification from Sentinel-2
本地全文：下载
作者：Shyamal S. Virnodkar ; Vinod K. Pachghare ; V.C. Patil 等
期刊名称：Journal of King Saud University @?C Computer and Information Sciences
印刷版ISSN：1319-1578
出版年度：2022
卷号：34
期号：6
页码：3343-3355
DOI：10.1016/j.jksuci.2020.09.005
语种：English
出版社：Elsevier
摘要：The ubiquitous deep learning (DL) in remote sensing (RS) motivates the most challenging problem of crop classification. To perpetrate such an exigent task, an attempt is made to prepare a novel dataset, the CaneSat dataset, in two formats: RGB color space and geo-tiff images, covering the region of four talukas in Karnataka, India. This research aims to build a model for sugarcane classification using two-dimensional convolutional neural network (CNN or ConvNet) applying RS time series data. Further, the study intents to evaluate competency of four state-of-the-art deep CNNs namely AlexNet, GoogLeNet, ResNet50 and DenseNet201 using fine tuning and deep CNNs as feature extractors to classify sugarcane and non-sugarcane areas from Sentinel-2 data. The results of the research are expressive on CaneSat dataset. It shows that the CNN model performs significantly good producing 88.46% accuracy, whereas all deep networks exhibit more than 73.00% overall accuracy. When used as feature extractors, ResNet50 and DenseNet201 outperform all other models with precision of 85.65% and 87.70%, respectively. Noticeably, the results indicate that 2D CNN model and features extracted using CNNs with SVM classifier are efficient methods for sugarcane classification from Sentinel-2 time series data in peninsular zone of India.
关键词：Multi-temporal remote sensing images;Sugarcane classification;Convolutional neural network;Deep network