Abstract
Land Use/ Land Cover change detection is an inspiring, interesting task to be performed worldwide. The real-time satellite data of the earth's surface and its different findings can be studied with the assistance of Remote Sensing and Geographic Information Systems. This work builds the novel Fuzzy Swin Transformer-based LU/LC classification model using the LISS-III satellite data of Yelagiri Hills. In the proposed work, the LU/LC features extracted from fuzzy clustering were used as the input training patches when executing the Swin Transformer model. The training patches assist the transformer in finding the LU/LC classification map with good computational complexity and accuracy. The Simple Random, Cluster, Systematic, and Stratified Random sampling methods were used to validate the accuracy of the acquired LU/LC classification map. The proposed Fuzzy Swin Transformer model attains good results with an average classification accuracy of 98.43% using Simple Random Sampling, 97.45% using Stratified Random Sampling, 97.36% using Systematic Sampling, and 96.97% using Cluster Sampling. The LU/LC change detected in this work was considered an important source of information to support the concerned land resource planners in taking necessary action to preserve the land cover, exclusively for the forest-covered areas of the hill stations.
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References
Adhikari S, Fik T, Dwivedi P (2017) Proximate causes of land-use and land-cover change in bannerghatta National Park: a spatial statistical model. Forests 8(9):342. https://doi.org/10.3390/f8090342
Agilandeeswari L, Meena SD (2023) SWIN transformer based contrastive self-supervised learning for animal detection and classification. Multimed Tools Appl 82:10445–10470. https://doi.org/10.1007/s11042-022-13629-x
Agilandeeswari L, Prabukumar M, Radhesyam V, Phaneendra KL, Farhan A (2022) Crop classification for agricultural applications in hyperspectral remote sensing images. Appl Sci 12(3):1670. https://doi.org/10.3390/app12031670
Ali ASA, Ebrahimi S, Ashiq MM, Alasta MS, Azari B (2022) CNN-bi LSTM neural network for simulating groundwater level. Environ Eng 8(1):1–7. https://doi.org/10.52547/crpase.8.1.2748
Amigo JM, Santos C (2020) Preprocessing of hyperspectral and multispectral images. In: Data handling in science and technology (Vol. 32, pp. 37–53). Elsevier. https://doi.org/10.1016/B978-0-444-63977-6.00003-1
Asubonteng K, Pfeffer K, Ros-Tonen M, Verbesselt J, Baud I (2018) Effects of tree-crop farming on land-cover transitions in a mosaic landscape in the eastern region of Ghana. Environ Manage 62(3):529–547. https://doi.org/10.1007/s00267-018-1060-3
Bazi Y, Bashmal L, Rahhal MMA, Dayil RA, Ajlan NA (2021) Vision transformers for remote sensing image classification. Remote Sensing 13(3):516. https://doi.org/10.3390/rs13030516
Chen X, Qiu C, Guo W, Yu A, Tong X, Schmitt M (2022) Multiscale feature learning by transformer for building extraction from satellite images. IEEE Geosci Remote Sens Lett 19:1–5. https://doi.org/10.1109/LGRS.2022.3142279
Chethan KS, Sinchana GS, Choodarathnakara AL (2020) Classification of Homogeneous Sites Using IRS-P5 Satellite Imagery. In 2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM) (pp. 184–189). IEEE. https://doi.org/10.1109/ICCAKM46823.2020.9051510
Chughtai AH, Abbasi H, Karas IR (2021) A review on change detection method and accuracy assessment for land use land cover. Remote Sens Appl Soc Environ 22:100482. https://doi.org/10.1016/j.rsase.2021.100482
Dewangkoro, H. I., & Arymurthy, A. M. (2021). Land use and land cover classification using CNN, SVM, and channel squeeze & spatial excitation block. In: IOP Conference Series: Earth and Environmental Science 704(1):p. 012048). IOP Publishing. https://doi.org/10.1088/1755-1315/704/1/012048
Dong S, Guo H, Chen Z, Pan Y, Gao B (2022) Spatial stratification method for the sampling design of LULC classification accuracy assessment: a case study in Beijing. China Remote Sensing 14(4):865. https://doi.org/10.3390/rs14040865
Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J (2020) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929. https://doi.org/10.48550/arXiv.2010.11929
Etemadi H, Smoak JM, Karami J (2018) Land use change assessment in coastal mangrove forests of Iran utilizing satellite imagery and CA–MARKOV algorithms to monitor and predict future change. Environ Earth Sci 77(5):1–13. https://doi.org/10.1007/s12665-018-7392-8
Floreano IX, de Moraes LAF (2021) Land use/land cover (LULC) analysis (2009–2019) with Google earth engine and 2030 prediction using markov-CA in the Rondônia state. Brazil Environ Monitor Asses 193(4):1–17. https://doi.org/10.1007/s10661-021-09016-y
Fonji SF, Taff GN (2014) Using satellite data to monitor land-use land-cover change in North-Eastern Latvia. Springerplus 3(1):1–15. https://doi.org/10.1186/2193-1801-3-61
Gaetano R, Ienco D, Ose K, Cresson R (2018) A two-branch CNN architecture for land cover classification of PAN and MS imagery. Remote Sensing 10(11):1746. https://doi.org/10.3390/rs10111746
Ganasri BP, Dwarakish GS (2015) Study of land use/land cover dynamics through classification algorithms for harangi catchment area, Karnataka state, INDIA. Aquatic Procedia 4:1413–1420. https://doi.org/10.1016/j.aqpro.2015.02.183
Garai D, Narayana AC (2018) Land use/land cover changes in the mining area of godavari coal fields of southern India. Egypt J Remote Sens Space Sci 21(3):375–381. https://doi.org/10.1016/j.ejrs.2018.01.002
Gashaw T, Tulu T, Argaw M, Worqlul AW (2017) Evaluation and prediction of land use/land cover changes in the andassa watershed, Blue Nile Basin. Ethiopia Environ Syst Res 6(1):1–15. https://doi.org/10.1186/s40068-017-0094-5
Ghosh A, Mishra NS, Ghosh S (2011) Fuzzy clustering algorithms for unsupervised change detection in remote sensing images. Inf Sci 181(4):699–715. https://doi.org/10.1016/j.ins.2010.10.016
Gong H, Mu T, Li Q, Dai H, Li C, He Z, Wang W, Han F, Tuniyazi A, Li H, Lang X (2022) Swin-transformer-enabled YOLOv5 with attention mechanism for small object detection on satellite images. Remote Sensing 14(12):2861. https://doi.org/10.3390/rs14122861
Goodarzi Mehr S, Ahadnejad V, Abbaspour RA, Hamzeh M (2013) Using the mixture-tuned matched filtering method for lithological mapping with landsat TM5 images. Int J Remote Sens 34(24):8803–8816. https://doi.org/10.1080/01431161.2013.853144
Haq MA (2022) CNN based automated weed detection system using UAV imagery. Comput Syst Sci Eng 42(2):837–849. https://doi.org/10.32604/csse.2022.023016
Haq MA (2022) Planetscope nanosatellites image classification using machine learning. Comput Syst Sci Eng 42(3):1031. https://doi.org/10.32604/csse.2022.023221
Haq MA (2022) CDLSTM: a novel model for climate change forecasting. Comput Mater Contin 71(2):2363. https://doi.org/10.32604/cmc.2022.023059
Haq MA, Rahaman G, Baral P, Ghosh A (2021) Deep learning based supervised image classification using UAV images for forest areas classification. J Indian Soc Remote Sens 49:601–606. https://doi.org/10.1007/s12524-020-01231-3
Haq MA, Hassine SBH, Malebary SJ, Othman HA, Tag-Eldin EM (2023) 3D-cnnhsr: a 3-dimensional convolutional neural network for hyperspectral super-resolution. Comput Syst Sci Eng 47:2689–2705. https://doi.org/10.32604/csse.2023.039904
Haq MA, Ahsan A, Gyani J (2023a) Implementation of CNN for Plant Identification using UAV Imagery. Int J Adv Comput Sci Appl, 14(4). https://doi.org/10.14569/IJACSA.2023.0140441
Heidarlou HB, Shafiei AB, Erfanian M, Tayyebi A, Alijanpour A (2019) Effects of preservation policy on land use changes in iranian northern zagros forests. Land Use Policy 81:76–90. https://doi.org/10.1016/j.landusepol.2018.10.036
Hernández-Guzmán R, Ruiz-Luna A, González C (2019) Assessing and modeling the impact of land use and changes in land cover related to carbon storage in a western basin in Mexico. Remote Sens Appl Soc Environ 13:318–327. https://doi.org/10.1016/j.rsase.2018.12.005
Hong D, Han Z, Yao J, Gao L, Zhang B, Plaza A, Chanussot J (2021) SpectralFormer: rethinking hyperspectral image classification with transformers. IEEE Trans Geosci Remote Sens 60:1–15. https://doi.org/10.1109/TGRS.2021.3130716
HongLei Y, JunHuan P, BaiRu X, DingXuan Z (2013) Remote sensing classification using fuzzy C-means clustering with spatial constraints based on markov random field. Eur J Remote Sens 46(1):305–316. https://doi.org/10.5721/EuJRS20134617
Hossin M, Sulaiman MN (2015) A review on evaluation metrics for data classification evaluations. Int J Data Min Knowl Manag Process 5(2):1. https://doi.org/10.5121/ijdkp.2015.5201
Hung CC, Kulkarni S, Kuo BC (2010) A new weighted fuzzy c-means clustering algorithm for remotely sensed image classification. IEEE J Sel Top Signal Process 5(3):543–553. https://doi.org/10.1109/JSTSP.2010.2096797
Hussain S, Mubeen M, Karuppannan S (2022) Land use and land cover (LULC) change analysis using TM, ETM+ and OLI landsat images in district of okara, Punjab, Pakistan. Phys Chem Earth Parts a/b/c 126:103117. https://doi.org/10.1016/j.pce.2022.103117
Jamali A, Mahdianpari M (2022) Swin transformer and deep convolutional neural networks for coastal wetland classification using Sentinel-1, Sentinel-2, and LiDAR data. Remote Sensing 14(2):359. https://doi.org/10.3390/rs14020359
Jin J, Yang J (2020) Effects of sampling approaches on quantifying urban forest structure. Landsc Urban Plan 195:103722. https://doi.org/10.1016/j.landurbplan.2019.103722
Kabisch N, Selsam P, Kirsten T, Lausch A, Bumberger J (2019) A multi-sensor and multi-temporal remote sensing approach to detect land cover change dynamics in heterogeneous urban landscapes. Ecol Ind 99:273–282. https://doi.org/10.1016/j.ecolind.2018.12.033
Kafy AA, Dey NN, Al Rakib A, Rahaman ZA, Nasher NR, Bhatt A (2021) Modeling the relationship between land use/land cover and land surface temperature in Dhaka. Bangladesh Using CA-ANN Algorithm Environ Chall 4:100190. https://doi.org/10.1016/j.envc.2021.100190
Kaur S, Bansal RK, Mittal M, Goyal LM, Kaur I, Verma A, Son LH (2019) Mixed pixel decomposition based on extended fuzzy clustering for single spectral value remote sensing images. J Ind Soc Remote Sens 47(3):427–437. https://doi.org/10.1007/s12524-019-00946-2
Khan S, Naseer M, Hayat M, Zamir SW, Khan FS, Shah M (2022) Transformers in vision: a survey. ACM Comput Surveys (CSUR) 54(10s):1–41. https://doi.org/10.1145/3505244
Kotoky P, Dutta MK, Borah GC (2012) Changes in landuse and landcover along the Dhansiri River channel, Assam—A remote sensing and GIS approach. J Geol Soc India 79(1):61–68. https://doi.org/10.1007/s12594-012-0002-6
Kulkarni G, Muley A, Deshmukh N, Bhalchandra P (2020) Land use land cover change detection through GIS and unsupervised learning technique. In: Information and Communication Technology for Sustainable Development (pp. 239–247). Springer, Singapore. https://doi.org/10.1007/978-981-13-7166-0_23
Kumar R, Nandy S, Agarwal R, Kushwaha SPS (2014) Forest cover dynamics analysis and prediction modeling using logistic regression model. Ecol Ind 45:444–455. https://doi.org/10.1016/j.ecolind.2014.05.003
Li C, Liu L, Sun X, Zhao J, Yin J (2019) Image segmentation based on fuzzy clustering with cellular automata and features weighting. EURASIP J Image Vid Process 2019(1):1–11. https://doi.org/10.1186/s13640-019-0436-5
Liping C, Yujun S, Saeed S (2018) Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques—A case study of a hilly area, jiangle. China Plos One 13(7):e0200493. https://doi.org/10.1371/journal.pone.0200493
Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Guo B (2021) Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 10012–10022
Loukika KN, Keesara VR, Sridhar V (2021) Analysis of land use and land cover using machine learning algorithms on google earth engine for Munneru River basin. India Sustain 13(24):13758. https://doi.org/10.3390/su132413758
Mai DS, Ngo LT, Hagras H (2021) A hybrid interval type-2 semi-supervised possibilistic fuzzy c-means clustering and particle swarm optimization for satellite image analysis. Inf Sci 548:398–422. https://doi.org/10.1016/j.ins.2020.10.003
Mallupattu PK, Sreenivasula Reddy JR (2013) Analysis of land use/land cover changes using remote sensing data and GIS at an urban area, Tirupati India. Sci World J 2013:268623. https://doi.org/10.1155/2013/268623
Mangan P, Haq MA, Baral P (2019) Morphometric analysis of watershed using remote sensing and GIS—a case study of Nanganji River basin in Tamil Nadu, India. Arab J Geosci 12:1–14. https://doi.org/10.1007/s12517-019-4382-4
Mangan P et al (2022) Analytic hierarchy process based land suitability for organic farming in the arid region. Sustainability 14(8):4542. https://doi.org/10.3390/su14084542
Mishra G, Sethi N, Agilandeeswari L, Hu Y-C (2023) Intelligent Abstractive Text Summarization using Hybrid Word2Vec and Swin Transformer for Long Documents. International Journal of Computer Information Systems and Industrial Management Applications 15(2023):212–226
Mohamed SA, El-Raey ME (2019) Land cover classification and change detection analysis of qaroun and Wadi el-rayyan lakes using multi-temporal remotely sensed imagery. Environ Monit Assess 191(4):1–19. https://doi.org/10.1007/s10661-019-7339-x
MohanRajan SN, Loganathan A (2021) Modelling spatial drivers for LU/LC change prediction using hybrid machine learning methods in Javadi Hills, Tamil Nadu, India. J Indian Soc Remote Sens 49:913–934. https://doi.org/10.1007/s12524-020-01258-6
MohanRajan SN, Loganathan A (2023) A novel fuzzy Harris hawks optimization-based supervised vegetation and bare soil prediction system for Javadi Hills. India Arab J Geosci 16(8):478. https://doi.org/10.1007/s12517-023-11538-3
MohanRajan SN, Loganathan A, Manoharan P (2020) Survey on land use/land cover (LU/LC) change analysis in remote sensing and GIS environment: techniques and challenges. Environ Sci Pollut Res 27(24):29900–29926. https://doi.org/10.1007/s11356-020-09091-7
Mohanrajan SN, Loganathan A (2022) Novel vision transformer–based bi-LSTM model for LU/LC prediction—Javadi Hills. India Appl Sci 12(13):6387. https://doi.org/10.3390/app12136387
Mu L, Wang L, Wang Y, Chen X, Han W (2019) Urban land use and land cover change prediction via self-adaptive cellular based deep learning with multisourced data. IEEE J Sel Top Appl Earth Observ Remote Sens 12(12):5233–5247. https://doi.org/10.1007/s10661-019-7339-x
Myint SW, Okin GS (2009) Modelling land-cover types using multiple endmember spectral mixture analysis in a desert city. Int J Remote Sens 30(9):2237–2257. https://doi.org/10.1080/01431160802549328
Nandy S, Kushwaha SPS (2011) Study on the utility of IRS 1D LISS-III data and the classification techniques for mapping of sunderban mangroves. J Coast Conserv 15(1):123–137. https://doi.org/10.1007/s11852-010-0126-z
Navin MS, Agilandeeswari L (2020b) Multispectral and hyperspectral images based land use/land cover change prediction analysis: an extensive review. Multimed Tools Appl 79(39):29751–29774. https://doi.org/10.1007/s11042-020-09531-z
Navin MS, Agilandeeswari L (2020a). Comprehensive review on land use/land cover change classification in remote sensing. J Spectr Imaging, 9. https://doi.org/10.1255/jsi.2020.a8
Nithya K, Shanmugasundaram R, Santhiyakumari N (2017) Study of salem city resource management using k-means clustering. In: 2017 Conference on Emerging Devices and Smart Systems (ICEDSS) (pp. 79–83). IEEE. https://doi.org/10.1109/ICEDSS.2017.8073663
Oo TK, Arunrat N, Sereenonchai S, Ussawarujikulchai A, Chareonwong U, Nutmagul W (2022) Comparing four machine learning algorithms for land cover classification in gold mining: a case study of kyaukpahto gold mine. Northern Myanmar Sustain 14(17):10754. https://doi.org/10.3390/su141710754
Ouchra, H., & Belangour, A. (2021). Satellite image classification methods and techniques: A survey. In: 2021 IEEE International Conference on Imaging Systems and Techniques (IST) (pp. 1–6). IEEE. https://doi.org/10.1109/IST50367.2021.9651454
Pande CB, Moharir KN, Khadri SFR, Patil S (2018) Study of land use classification in an arid region using multispectral satellite images. Appl Water Sci 8(5):1–11. https://doi.org/10.1007/s13201-018-0764-0
Phiri D, Morgenroth J (2017) Developments in landsat land cover classification methods: a review. Remote Sensing 9(9):967. https://doi.org/10.3390/rs9090967
Prabu P, Dar MA (2018) Land-use/cover change in Coimbatore urban area (tamil Nadu, India)—a remote sensing and GIS-based study. Environ Monit Assess 190(8):1–14. https://doi.org/10.1007/s10661-018-6807-z
Prabukumar M, Sawant S, Samiappan S, Agilandeeswari L (2018) Three-dimensional discrete cosine transform-based feature extraction for hyperspectral image classification. J Appl Remote Sens 12(4):046010–046010. https://doi.org/10.1117/1.JRS.12.046010
Román-Cuesta RM, Retana J, Gracia M, Rodriguez R (2005) A quantitative comparison of methods for classifying burned areas with LISS-III imagery. Int J Remote Sens 26(9):1979–2003. https://doi.org/10.1080/01431160512331299315
Rwanga SS, Ndambuki JM (2017) Accuracy assessment of land use/land cover classification using remote sensing and GIS. Int J Geosci 8(04):611. https://doi.org/10.4236/ijg.2017.84033
Shah S, Sharma DP (2015) Land use change detection in Solan forest division, Himachal Pradesh. India Forest Ecosystems 2(1):1–12. https://doi.org/10.1186/s40663-015-0050-7
Shalan MA, Arora MK, Ghosh SK (2003) An evaluation of fuzzy classifications from IRS 1C LISS III imagery: a case study. Int J Remote Sens 24(15):3179–3186. https://doi.org/10.1080/0143116031000094791
Sharma J, Prasad R, Mishra VN, Yadav VP, Bala R (2018) Land use and land cover classification of multispectral LANDSAT-8 satellite imagery using discrete wavelet transform. Int Arch Photogr Remote Sens Spatial Inf Sci 42:703–706. https://doi.org/10.5194/isprs-archives-XLII-5-703-2018
Shimizu K, Ota T, Mizoue N, Yoshida S (2018) Assessments of preprocessing methods for landsat time series images of mountainous forests in the tropics. J for Res 23(3):139–148. https://doi.org/10.1080/13416979.2018.1434034
Sowmya B, Sheelarani B (2011) Land cover classification using reformed fuzzy C-means. Sadhana 36(2):153–165. https://doi.org/10.1007/s12046-011-0018-4
Stehman SV, Foody GM (2019) Key issues in rigorous accuracy assessment of land cover products. Remote Sens Environ 231:111199. https://doi.org/10.1016/j.rse.2019.05.018
Svoboda J, Štych P, Laštovička J, Paluba D, Kobliuk N (2022) Random Forest classification of land use, land-use change and forestry (LULUCF) using Sentinel-2 data—A case study of czechia. Remote Sensing 14(5):1189. https://doi.org/10.3390/rs14051189
Talukdar S, Singha P, Mahato S, Pal S, Liou YA, Rahman A (2020) Land-use land-cover classification by machine learning classifiers for satellite observations—A review. Remote Sensing 12(7):1135. https://doi.org/10.3390/rs12071135
Thanh Noi P, Kappas M (2017) Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using Sentinel-2 imagery. Sensors 18(1):18. https://doi.org/10.3390/s18010018
Thyagharajan KK, Vignesh T (2019) Soft computing techniques for land use and land cover monitoring with multispectral remote sensing images: a review. Arch Comput Methods Eng 26(2):275–301. https://doi.org/10.1007/s11831-017-9239-y
Vijayan D, Shankar GR, Shankar TR (2014) Hyperspectral data for land use/land cover classification. Int Arch Photogr Remote Sens Spatial Inf Sci 40(8):991. https://doi.org/10.5194/isprsarchives-XL-8-991-2014
Wen Q, Zhou T, Zhang C, Chen W, Ma Z, Yan J, Sun L (2022) Transformers in time series: A survey. arXiv preprint arXiv:2202.07125. https://doi.org/10.48550/arXiv.2202.07125
Xiao B, Liu J, Jiao J, Li Y, Liu X, Zhu W (2022) Modeling dynamic land use changes in the eastern portion of the hexi corridor, China by cnn-gru hybrid model. Gisci Remote Sens 59(1):501–519. https://doi.org/10.1080/15481603.2022.2037888
Xing W, Qian Y, Guan X, Yang T, Wu H (2020) A novel cellular automata model integrated with deep learning for dynamic spatio-temporal land use change simulation. Comput Geosci 137:104430. https://doi.org/10.1016/j.cageo.2020.104430
Xu X, Feng Z, Cao C, Li M, Wu J, Wu Z, Shang Y, Ye S (2021) An improved swin transformer-based model for remote sensing object detection and instance segmentation. Remote Sensing 13(23):4779. https://doi.org/10.3390/rs13234779
Xu, R (2022) Fuzzy C-means clustering image segmentation algorithm based on hidden markov model. Mobile Networks and Applications, 1–9. https://doi.org/10.1007/s11036-022-01917-7
Yang Q, Zhang H, Peng W, Lan Y, Luo S, Shao J, Chen D, Wang G (2019) Assessing climate impact on forest cover in areas undergoing substantial land cover change using landsat imagery. Sci Total Environ 659:732–745. https://doi.org/10.1016/j.scitotenv.2018.12.290
Yao J, Jin S (2022) Multi-category segmentation of Sentinel-2 images based on the swin UNet method. Remote Sensing 14(14):3382. https://doi.org/10.3390/rs14143382
Zhang F, Yushanjiang A, Jing Y (2019) Assessing and predicting changes of the ecosystem service values based on land use/cover change in ebinur Lake wetland National Nature Reserve, Xinjiang, China. Sci Total Environ 656:1133–1144. https://doi.org/10.1016/j.scitotenv.2018.11.444
Acknowledgements
The authors extend their sincere thanks to Prince Sattam Bin Abdulaziz University, Saudi Arabia for funding this work under grant number (PSAU/2023/R/1445) and Vellore Institute of Technology, Vellore, India for providing space to carry out this work successfully.
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The authors extend their sincere thanks to Prince Sattam Bin Abdulaziz University, Saudi Arabia for funding this work under grant number (PSAU/2023/R/1445) and Vellore Institute of Technology, Vellore, India for providing space to carry out this work successfully.
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All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by [Sam Navin M], [Agilandeeswari Loganathan], and [Prabukumar Manoharan]. The first draft of the manuscript was written by [Sam Navin M] and all other authors [Agilandeeswari Loganathan], [Prabukumar Manoharan] and [Farhan A. Alenizi] commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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MohanRajan, S.N., Loganathan, A., Manoharan, P. et al. Fuzzy Swin transformer for Land Use/ Land Cover change detection using LISS-III Satellite data. Earth Sci Inform 17, 1745–1764 (2024). https://doi.org/10.1007/s12145-023-01208-z
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DOI: https://doi.org/10.1007/s12145-023-01208-z