Joint feature selection and classification of low-resolution satellite images using the SAT-6 dataset

Rajalaxmi Padhy , Sanjit Kumar Dash , Jibitesh Mishra

High-Confidence Computing ›› 2025, Vol. 5 ›› Issue (3) : 100278

PDF (1624KB)
High-Confidence Computing ›› 2025, Vol. 5 ›› Issue (3) : 100278 DOI: 10.1016/j.hcc.2024.100278
Research Articles
research-article

Joint feature selection and classification of low-resolution satellite images using the SAT-6 dataset

Author information +
History +
PDF (1624KB)

Abstract

The modern industries of today demand the classification of satellite images, and to use the information obtained from it for their advantage and growth. The extracted information also plays a crucial role in national security and the mapping of geographical locations. The conventional methods often fail to handle the complexities of this process. So, an effective method is required with high accuracy and stability. In this paper, a new methodology named RankEnsembleFS is proposed that addresses the crucial issues of stability and feature aggregation in the context of the SAT-6 dataset. RankEnsembleFS makes use of a two-step process that consists of ranking the features and then selecting the optimal feature subset from the top-ranked features. RankEnsembleFS achieved comparable accuracy results to state-of-the-art models for the SAT-6 dataset while significantly reducing the feature space. This reduction in feature space is important because it reduces computational complexity and enhances the interpretability of the model. Moreover, the proposed method demonstrated good stability in handling changes in data characteristics, which is critical for reliable performance over time and surpasses existing ML ensemble methods in terms of stability, threshold setting, and feature aggregation. In summary, this paper provides compelling evidence that this RankEnsembleFS methodology presents excellent performance and overcomes key issues in feature selection and image classification for the SAT-6 dataset.

Keywords

Satellite images / Feature selection / Ensemble feature selection / Feature ranking / Feature reduction / Image classification

Cite this article

Download citation ▾
Rajalaxmi Padhy, Sanjit Kumar Dash, Jibitesh Mishra. Joint feature selection and classification of low-resolution satellite images using the SAT-6 dataset. High-Confidence Computing, 2025, 5(3): 100278 DOI:10.1016/j.hcc.2024.100278

登录浏览全文

4963

注册一个新账户 忘记密码

CRediT authorship contribution statement

Rajalaxmi Padhy: Conceptualization, Methodology, Software, Investigation, Resources, Data curation, Writing - original draft. Sanjit Kumar Dash: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Writing - review & editing, Visualization. Jibitesh Mishra: Writing - review & editing, Visualization, Supervision, Project administration.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

[1]

W. Emery, A. Camps, Introduction to Satellite Remote Sensing: Atmosphere, Ocean, Land and Cryosphere Applications, Elsevier, 2017.

[2]

S. Iyer, R. Desai, S. Deore,S. Ahuja, Classification of low-resolution satellite images using image fusion and decorrelation stretch.

[3]

A. Asokan, J. Anitha, M. Ciobanu, A. Gabor, A. Naaji, D.J. Hemanth, Image processing techniques for analysis of satellite images for historical maps classification—An overview, Appl. Sci. 10 (12) (2020) 4207.

[4]

N. Ahmed, S. Saha, M. Shahzad, M.M. Fraz, X.X. Zhu, Progressive unsupervised deep transfer learning for forest mapping in satellite image, in:Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 752-761.

[5]

J. Li, K. Cheng, S. Wang, F. Morstatter, R.P. Trevino, J. Tang, H. Liu, Feature selection: A data perspective, ACM Comput. Surv. ( CSUR) 50 (6) (2017) 1-45.

[6]

M. Xin, Y. Wang, Research on image classification models based on deep convolutional neural networks, EURASIP J. Image Video Process. 2019 (2019) 1-11.

[7]

Rostami. Mehrdad, et al., Review of swarm intelligence-based feature selection methods, Eng. Appl. Artif. Intell. 100 (2021) 104210.

[8]

R. Thakur, P. Panse, ELSET: Design of an ensemble deep learning model for improving satellite image classification efficiency via temporal analysis, Measurement: Sens. 24 (2022) 100437.

[9]

Reena Thakur, Prashant Panse, ELSET: Design of an ensemble deep learning model for improving satellite image classification efficiency via temporal analysis, Measurement: Sens. 24 (2022) 100437.

[10]

Z.M. Hira, D.F. Gillies, A review of feature selection and feature extraction methods applied on microarray data, Adv. Bioinform. 2015 (2015).

[11]

N. Pudjihartono, T. Fadason, A.W. Kempa-Liehr, J.M. O’Sullivan, A review of feature selection methods for machine learning-based disease risk prediction, Front. Bioinform 2 (2022) 927312.

[12]

M.R. Alhamidi, W. Jatmiko, Optimal feature aggregation and combination for two-dimensional ensemble feature selection, Information 11 (1) (2020) 38.

[13]

S. Basu, S. Ganguly, S. Mukhopadhyay, R. DiBiano, M. Karki, R. Nemani, Deepsat: a learning framework for satellite imagery,in:Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2015, pp. 1-10.

[14]

Q. Liu, S. Basu, S. Ganguly, S. Mukhopadhyay, R. DiBiano, M. Karki, R. Nemani,Deepsat v2: feature augmented convolutional neural nets for satellite image classification, Remote Sens. Lett. 11 (2) (2020) 156-165.

[15]

S. Amini, M. Saber, H. Rabiei-Dastjerdi, S. Homayouni, Urban land use and land cover change analysis using random forest classification of landsat time series, Remote Sens. 14 (11) (2022) 2654.

[16]

R. Naushad, T. Kaur, E. Ghaderpour, Deep transfer learning for land use and land cover classification: A comparative study, Sensors 21 (23) (2021) 8083.

[17]

Gonzalo Martínez-Muñoz, Daniel Hernández-Lobato, Alberto Suárez, An analysis of ensemble pruning techniques based on ordered aggregation, IEEE Trans. Pattern Anal. Mach. Intell. 31 (2009) 245-259, http://dx.doi.org/10.1109/TPAMI.2008.78.

[18]

K. Thiagarajan, M. Manapakkam Anandan, A. Stateczny, P. Bidare Divakarachari, H. Kivudujogappa Lingappa, Satellite image classification using a hierarchical ensemble learning and correlation coefficient-based gravitational search algorithm, Remote Sens. 13 (21) (2021) 4351.

[19]

M.J. Horry, S. Chakraborty, B. Pradhan, N. Shulka, M. Almazroui, Two-speed deep-learning ensemble for classification of incremental land-cover satellite image patches, Earth Syst. Environ. 7 (2) (2023) 525-540.

[20]

H. Du, M. Li, Y. Xu, C. Zhou, An ensemble learning approach for land use/land cover classification of arid regions for climate simulation: A case study of Xinjiang, Northwest China, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 16 (2023) 2413-2426.

[21]

R. Padhy, S.S. Swain, S.K. Dash, J. Mishra, Classification of low-resolution satellite images using fractal augmented descriptors, Int. J. Image Graph. 22 (01) (2022) 2250002.

AI Summary AI Mindmap
PDF (1624KB)

505

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/