CFP last date
20 June 2024
Reseach Article

Land Cover Classification of Satellite Imagery using Deep Learning

by Vismaya Prakasan, Romita Pawar, Aditee Pachpande
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 28
Year of Publication: 2022
Authors: Vismaya Prakasan, Romita Pawar, Aditee Pachpande
10.5120/ijca2022922357

Vismaya Prakasan, Romita Pawar, Aditee Pachpande . Land Cover Classification of Satellite Imagery using Deep Learning. International Journal of Computer Applications. 184, 28 ( Sep 2022), 1-7. DOI=10.5120/ijca2022922357

@article{ 10.5120/ijca2022922357,
author = { Vismaya Prakasan, Romita Pawar, Aditee Pachpande },
title = { Land Cover Classification of Satellite Imagery using Deep Learning },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2022 },
volume = { 184 },
number = { 28 },
month = { Sep },
year = { 2022 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number28/32490-2022922357/ },
doi = { 10.5120/ijca2022922357 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:22:37.494039+05:30
%A Vismaya Prakasan
%A Romita Pawar
%A Aditee Pachpande
%T Land Cover Classification of Satellite Imagery using Deep Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 28
%P 1-7
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the assessment of remotely sensed imagery, hyper-spectral (HSI) image classifications are commonly employed. The hyper-spectral image includes various image bands. The Convolutional Neural Network (CNN) is an extensively useful deep learning algorithm for data visualization and processing. Both the Spatial along with Spectral information are important for HSI classes to be effective. Due to the higher computing complexity, only a few approaches have used 3D CNN. Hybrid Spectral Convolutional 2D-3D Network (HybridSN) is instituted for HSI classing in this paper. HybridSN involves a spatial and spectral 3D-CNN which is then trailed by a spatial 2D. A study of more abstract level spatial representation will continue with 2D-CNN over 3D-CNN. Furthermore, when compared to conventional CNNs, the employment of hybrid CNNs lessens the model

References
  1. Yanhui Guo, Xijie Yin, Xuechen Zhao, Dongxin Yang, and Yu Bai. Hyperspectral image classification with svm and guided filter. EURASIP Journal on Wireless Communications and Networking, 2019(1):1–9, 2019.
  2. M Shambulinga and G Sadashivappa. Hyperspectral image classification using support vector machine with guided image filter. IJACSA, 10:271–276, 2019.
  3. Alou Diakite, Gui Jiangsheng, and Fu Xiaping. Hyperspectral image classification using 3d 2d cnn. IET Image Processing, 15(5):1083–1092, 2021.
  4. Junru Yin, Changsheng Qi, Qiqiang Chen, and Jiantao Qu. Spatial-spectral network for hyperspectral image classification: A 3-d cnn and bi-lstm framework. Remote Sensing, 13(12):2353, 2021.
  5. Douglas Omwenga Nyabuga, Jinling Song, Guohua Liu, and Michael Adjeisah. A 3d-2d convolutional neural network and transfer learning for hyperspectral image classification. Computational Intelligence and Neuroscience, 2021, 2021.
  6. Xudong Kang, Shutao Li, and Jon Atli Benediktsson. Spectral–spatial hyperspectral image classification with edgepreserving filtering. IEEE transactions on geoscience and remote sensing, 52(5):2666–2677, 2013.
  7. Xinyu Lei, Hongguang Pan, and Xiangdong Huang. A dilated cnn model for image classification. IEEE Access, 7:124087– 124095, 2019.
  8. D Jeevalakshmi, S Narayana Reddy, and B Manikiam. Land cover classification based on ndvi using landsat8 time series: a case study tirupati region. In 2016 International Conference on Communication and Signal Processing (ICCSP), pages 1332–1335. IEEE, 2016.
  9. Lei Ma, Tengyu Fu, Thomas Blaschke, Manchun Li, Dirk Tiede, Zhenjin Zhou, Xiaoxue Ma, and Deliang Chen. Evaluation of feature selection methods for object-based land cover mapping of unmanned aerial vehicle imagery using random forest and support vector machine classifiers. ISPRS International Journal of Geo-Information, 6(2):51, 2017.
  10. Bing Liu, Anzhu Yu, Xibing Zuo, Zhixiang Xue, Kuiliang Gao, and Wenyue Guo. Spatial-spectral feature classification of hyperspectral image using a pretrained deep convolutional neural network. European Journal of Remote Sensing, 54(1):385–397, 2021.
  11. Miae Kim, Junghee Lee, Daehyeon Han, Minso Shin, Jungho Im, Junghye Lee, Lindi J Quackenbush, and Zhu Gu. Convolutional neural network-based land cover classification using 2-d spectral reflectance curve graphs with multitemporal satellite imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(12):4604–4617, 2018.
  12. Fuding Xie, Fangfei Li, Cunkuan Lei, and Lina Ke. Representative band selection for hyperspectral image classification. ISPRS International Journal of Geo- Information, 7(9):338, 2018.
  13. Hayder Hasan, Helmi ZM Shafri, and Mohammed Habshi. A comparison between support vector machine (svm) and convolutional neural network (cnn) models for hyperspectral image classification. In IOP Conference Series: Earth and Environmental Science, volume 357, page 012035. IOP Publishing, 2019.
  14. Alexander FH Goetz. Three decades of hyperspectral remote sensing of the earth: A personal view. Remote Sensing of Environment, 113:S5–S16, 2009.
  15. SAROJ KUMAR. ” Jnana Sangama” Belgaum-590018. PhD thesis, Visvesvaraya Technological University, 2012.
  16. Bing Lu, Phuong D Dao, Jiangui Liu, Yuhong He, and Jiali Shang. Recent advances of hyperspectral imaging technology and applications in agriculture. Remote Sensing, 12(16):2659, 2020.
  17. Yushi Chen, Hanlu Jiang, Chunyang Li, Xiuping Jia, and Pedram Ghamisi. Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Transactions on Geoscience and Remote Sensing, 54(10):6232–6251, 2016.
  18. Ava Vali, Sara Comai, and Matteo Matteucci. Deep learning for land use and land cover classification based on hyperspectral and multispectral earth observation data: A review. Remote Sensing, 12(15):2495, 2020.
  19. Yanan Luo, Jie Zou, Chengfei Yao, Xiaosong Zhao, Tao Li, and Gang Bai. Hsi-cnn: a novel convolution neural network for hyperspectral image. In 2018 International Conference on Audio, Language and Image Processing (ICALIP), pages 464–469. IEEE, 2018.
  20. Gavneet Singh Chadha, Jan Niclas Reimann, and Andreas Schwung. Generalized dilation structures in convolutional neural networks. In ICPRAM, pages 79–88, 2021.
  21. Ying Li, Haokui Zhang, Xizhe Xue, Yenan Jiang, and Qiang Shen. Deep learning for remote sensing image classification: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(6):e1264, 2018.
Index Terms

Computer Science
Information Sciences

Keywords

Hyper-spectral Image Remote Sensing Overall Accuracy Hybrid Spectral Network Principle Component Analysis Confusion Matrix