Advances in Scene Classification of Remotely Sensed High Resolution Images and the Existing Datasets
Akila G1, Gayathri R2
1Akila G, Research Scholar, Faculty Of Information and Communication Engineering , Anna University, Chennai, Tamil Nadu, India.
2Gayathri R, Associate Professor, Department Of Electronics And Communication Engineering, Sri Venkateswara College of Engineering, Sriperumbudur Tk, Kancheepuram District, Tamil Nadu, India
Manuscript received on 02 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 August 2019 | PP: 1643-1647 | Volume-8 Issue-10, August 2019 | Retrieval Number: J88410881019/2019©BEIESP | DOI: 10.35940/ijitee.J8841.0881019
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Research on Scene classification of remotely sensed images has shown a significant improvement in the recent years as it is used in various applications such as urban planning, urban mapping, management of natural resources, precision agriculture, detecting targets etc. The recent advancement of intelligent earth observation system has led to the generation of high resolution remote sensing images in terms of spatial, spectral and temporal resolutions which in turn helped the researchers to improve the performance of Land Use Land Class (LULC) Classification Techniques to a higher level. With the usage of different deep learning architecture and the availability of various high resolution image datasets, the field of Remote Sensing Scene Classification of high resolution (RSSCHR) images has shown tremendous improvement in the past decade. In this paper we present the different publicly available datasets , various scene classification methods and the future research scope of remotely sensed high resolution images.
Keywords: Deep Learning, Remote Sensing, Scene Classification, Datasets, Convolutional Neural Networks.
Scope of the Article: Classification