A novel approach for scene classification from remote sensing images using deep learning methods. (12th March 2021)

• Record Type: Journal Article
• Authors: Xu, Xiaowei; Chen, Yinrong; Zhang, Junfeng; Chen, Yu; Anandhan, Prathik; Manickam, Adhiyaman
• Is Part Of: European journal of remote sensing. Volume 54(2021)Supplement 2
• Page Start: 383
• Page End: 395
• Publication Date: 2021-03-12
• Subjects: Deep learning -- satellite images -- time series -- recurrent neural network -- remote sensing; Remote sensing -- Periodicals; Remote sensing; Electronic journals; Periodicals; 621.3678
• DOI: 10.1080/22797254.2020.1790995
• Languages: English
• ISSNs: 2279-7254

Abstract

ABSTRACT: Deep learning plays a major task in classification of unsupervised data, which utilises network enhanced learning. This technique proves to be powerful in remote sensing (RS) field for spatial data classification. In the existing environment, huge amounts of data from the earth observation satellites known as satellite images time series (SITS) are gathered, which can be utilised for observing the areas related to geography over through time. In this proposed model the time series model utilised is based on geography. There exists a challenge on how these types of information can be analysed in the field of remote sensing. Notable, techniques related to deep learning substantiated in dealing with remote sensing usually for classification of scene . In this paper, we propose an enhanced classification method involving Recurrent Neural Network (RNN) along with Random forest (RF) for land classification using satellite images, which are publicly available for various research purposes. We utilised spatial data gathered from the satellite images (i.e. time series). Our experimental classification is based on pixel and object-based classification. The attained analysis illustrates that the proposed model outperforms the other present day remote sensing classification techniques by producing 87% target accuracy of classification scene from satellite images.