Abstract
Machine learning(ML) based techniques for Land Use Land Cover(LULC) classification is crucial for extracting valuable insights from satellite imagery. The impact of severe class imbalance problems on LULC datasets, added to the limited temporal coverage, incomplete spectral information, spatial resolution constraints, and limitations due to sensor characteristics of a single satellite, hinder the efficient capturing of complex image features. The object-based classification using Gray-level co-occurrence matrix(GLCM) and Simple Non-Iterative Clustering(SNIC) captures the textural and spectral information, respectively, to enhance the accuracy in heterogeneous landscapes and overcome the limitations of pixel-based classification, such as the sensitivity towards noise and spectral confusion in mixed pixels at the cost of increased additional computational steps. In this context, the proposed study leverages high-quality fused images with diverse temporal, spectral, and spatial information obtained by fusing Landsat-8 and Sentinel-2 satellite imageries using Spatial-and-Temporal-Adaptive-Reflectance-Fusion-Model(STARFM). Further, to solve the class imbalance problem, a Granular Computing(GrC) based Segmentation and Textural Analysis(GrCSTA) framework is proposed for reducing the number of image primitives and computations required for subsequent image analysis processes in the object-based classification. The GrCSTA framework focuses on extracting the Spatial Granules(Gs) from the fused imagery using Spatial neighborhood Granulation(SNGr), textural indices of the Gs using GLCM, and reduced textural indices using Principal Component Analysis(PCA). Gs and its reduced textural indices are input features to train the Random Forest(RF) classifier. Experimental results demonstrate that the proposed GrCSTA framework achieves comparably higher accuracy than the state-of-the-art models.
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References
Ghamisi P, Yokoya N, Li J, Liao W, Liu S, Plaza A, Rasti B, Plaza J (2017) Advances in hyperspectral image and signal processing: A comprehensive overview of the state of the art. IEEE Geosci Remote Sens Mag 5(4):37–78. https://doi.org/10.1109/MGRS.2017.2762087
Ghimire B, Rogan J, Galiano VR, Panday P, Neeti N (2013) An evaluation of bagging, boosting, and random forests for land-cover classification in cape cod, massachusetts, usa. Remote Sens Lett 4(5):423–431. https://doi.org/10.1080/01431161.2013.775664
Pinheiro G, Raj A, Minz S, Choudhury T, Um J-S (2023) Inundation extend mapping for multi-temporal sar using automatic thresholding and change detection: A case study on kosi river of india. Spatial Inf Res 1–15. https://doi.org/10.1007/s41324-023-00555-9
Chen B, Huang B, Xu B (2015) Comparison of spatiotemporal fusion models: A review. Remote Sens 7(2):1798–1835. https://doi.org/10.3390/rs70201798
Ghassemian H (2016) A review of remote sensing image fusion methods. Inf Fusion 32:75–89. https://doi.org/10.1016/j.inffus.2016.03.003
Masood S, Sharif M, Yasmin M, Shahid MA, Rehman A (2017) Image fusion methods: A survey. J Eng Sci Technol Rev 10(6):186–194. https://doi.org/10.25103/jestr.106.24
Tripathi A, Kumar S, Maithani S (2023) Improved Data Fusion-Based Land Use/Land Cover Classification Using PolSAR and Optical Remotely Sensed Satellite Data: A Machine Learning Approach. CRC Press, Boca Raton, pp 123–145
Camargo FF, Sano EE, Almeida CM, Mura JC, Almeida T (2019) A comparative assessment of machine-learning techniques for land use and land cover classification of the brazilian tropical savanna using alos-2/palsar-2 polarimetric images. Remote Sens 11(13):1600. https://doi.org/10.3390/rs11131600
Aliyu AO, Akomolafe EA, Adamu B, Youngu T, Hassan M, Swafiyudeen B (2023) Integrated method for classifying medium resolution satellite remotely sensed imagery into land use map. Int J Environ Geoinf 10(2):135–144. https://doi.org/10.30897/ijegeo.1150436
Belgiu M, Csillik O (2018) Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis. Remote Sens Environ 204:509–523. https://doi.org/10.1016/J.RSE.2017.10.005
Qu L, Chen Z, Li M, Zhi J, Wang H (2021) Accuracy improvements to pixel-based and object-based lulc classification with auxiliary datasets from google earth engine. Remote Sens 13(3):453. https://doi.org/10.3390/rs13030453
Shahri AA, Spross J, Johansson F, Larsson S (2019) Landslide susceptibility hazard map in southwest sweden using artificial neural network. Catena 183:104225. https://doi.org/10.1016/j.catena.2019.104225
Esetlili MT, Balcik FB, Sanli FB, Kalkan K, Ustuner M, Goksel C, Gazioğlu C, Kurucu Y (2018) Comparison of object and pixel-based classifications for mapping crops using rapideye imagery: a case study of menemen plain, turkey. Int J Environ Geoinf 5(2):231–243. https://doi.org/10.30897/ijegeo.442002
Shafizadeh-Moghadam H, Khazaei M, Alavipanah SK, Weng Q (2021) Google earth engine for large-scale land use and land cover mapping: An object-based classification approach using spectral, textural and topographical factors. GIScience & Remote Sens 58(6):914–928. https://doi.org/10.1080/15481603.2021.1947623
Yang L, Mansaray LR, Huang J, Wang L (2019) Optimal segmentation scale parameter, feature subset and classification algorithm for geographic object-based crop recognition using multisource satellite imagery. Remote Sens 11(5):514. https://doi.org/10.3390/rs11050514
Vizzari M (2022) Planetscope, sentinel-2, and sentinel-1 data integration for object-based land cover classification in google earth engine. Remote Sens 14(11):2628. https://doi.org/10.3390/rs14112628
Xue H, Xu X, Zhu Q, Yang G, Long H, Li H, Yang X, Zhang J, Yang Y, Xu S et al (2023) Object-oriented crop classification using time series sentinel images from google earth engine. Remote Sens 15(5):1353. https://doi.org/10.3390/rs15051353
Tassi A, Vizzari M (2020) Object-oriented lulc classification in google earth engine combining snic, glcm, and machine learning algorithms. Remote Sens 12(22):3776. https://doi.org/10.3390/rs12223776
Baatz M, Hoffmann C, Willhauck G (2008) Progressing from object-based to object-oriented image analysis. Object-based image analysis: Spatial concepts for knowledge-driven remote sensing applications 29–42. https://doi.org/10.1007/978-3-540-77058-9_2
Blaschke T (2010) Object based image analysis for remote sensing. ISPRS J Photogrammetry Remote Sen 65(1):2–16. https://doi.org/10.1016/j.isprsjprs.2009.06.004
De Luca G, Silva J, Cerasoli S, Araújo J, Campos J, Fazio S, Modica G (2019) Object-based land cover classification of cork oak woodlands using uav imagery and orfeo toolbox. Remote Sens 11(10):1238. https://doi.org/10.3390/rs11101238
Solano F, Fazio S, Modica G (2019) A methodology based on geobia and worldview-3 imagery to derive vegetation indices at tree crown detail in olive orchards. Int J Appl Earth Observation Geoinf 83:101912. https://doi.org/10.1016/J.JAG.2019.101912
Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R (2017) Google earth engine: Planetary-scale geospatial analysis for everyone. Remote Sens Environ 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031
Naik DL, Kiran R (2021) A novel sensitivity-based method for feature selection. J Big Data 8:1–16. https://doi.org/10.1186/s40537-021-00515-w
Ge Y, Zhang X, Atkinson PM, Stein A, Li L (2022) Geoscience-aware deep learning: A new paradigm for remote sensing. Sci Remote Sens 5:100047. https://doi.org/10.1016/j.srs.2022.100047
Ye S, Pontius RG Jr, Rakshit R (2018) A review of accuracy assessment for object-based image analysis: From per-pixel to per-polygon approaches. ISPRS J Photogrammetry Remote Sens 141:137–147. https://doi.org/10.1016/j.isprsjprs.2018.04.002
Kucharczyk M, Hay GJ, Ghaffarian S, Hugenholtz CH (2020) Geographic object-based image analysis: a primer and future directions. Remote Sens 12(12):2012. https://doi.org/10.3390/rs12122012
Ren X, Malik J (2003) Learning a classification model for segmentation. Proceedings of the IEEE international conference on computer vision 1:10–17. https://doi.org/10.1109/ICCV.2003.1238308
Hossain MD, Chen D (2019) Segmentation for object-based image analysis (obia): A review of algorithms and challenges from remote sensing perspective. ISPRS J Photogrammetry Remote Sens 150:115–134. https://doi.org/10.1016/j.isprsjprs.2019.02.009
Albalawi EK (2023) Comparing pixel-based to object-based image classifications for assessing lulc change in an arid environment of northern west saudi arabia. Egyptian J Environ Change https://doi.org/10.21608/ejec.2023.286642
Balha A, Mallick J, Pandey S, Gupta S, Singh CK (2021) A comparative analysis of different pixel and object-based classification algorithms using multi-source high spatial resolution satellite data for lulc mapping. Earth Sci Inf 14(4):2231–2247. https://doi.org/10.1007/s12145-021-00685-4
Stutz D, Hermans A, Leibe B (2018) Superpixels: An evaluation of the state-of-the-art. Computer Vision and Image Understanding 166:1–27. https://doi.org/10.1016/J.CVIU.2017.03.007
Story M, Congalton RG (2023) Accuracy Assessment: A User’s Perspective. http://www.spatialanalysisonline.com/output/html/AccuracyAssessment.html. Accessed on April 12, 2023
Fujita H, Gaeta A, Loia V, Orciuoli F (2018) Resilience analysis of critical infrastructures: a cognitive approach based on granular computing. IEEE Trans Cybernetics 49(5):1835–1848. https://doi.org/10.1109/TCYB.2018.2815178
Liu H, Li W, Li R (2017) A comparative analysis of granular computing clustering from the view of set. J Intell & Fuzzy Syst 32(1):509–519. https://doi.org/10.3233/JIFS-152327
Yao Y (2004) A partition model of granular computing. In: Transactions on Rough Sets I: James F. Peters-Andrzej Skowron, Editors-in-Chief, Springer, Berlin, Heidelberg, pp 232–253. https://doi.org/10.1007/978-3-540-27794-1_11
Saitta L, Zucker J-D (1998) Semantic abstraction for concept representation and learning. In: Proceedings of the symposium on abstraction, reformulation and approximation, pp 103–120
Hall-Beyer M (2017) GLCM texture: A tutorial v. 3.0 March 2017. https://prism.ucalgary.ca/handle/1880/51900. Accessed on April 14, 2023
Kupidura P (2019) The comparison of different methods of texture analysis for their efficacy for land use classification in satellite imagery. Remote Sens 11(10):1233. https://doi.org/10.3390/rs11101233
Zhang P (2019) A novel feature selection method based on global sensitivity analysis with application in machine learning-based prediction model. Appl Soft Comput 85:105859. https://doi.org/10.1016/j.asoc.2019.105859
Abbaszadeh Shahri A, Chunling S, Larsson S (2023) A hybrid ensemble-based automated deep learning approach to generate 3d geo-models and uncertainty analysis. Eng Comput 1–16. https://doi.org/10.1007/s00366-023-01852-5
Jin Y, Liu X, Chen Y, Liang X (2018) Land-cover mapping using random forest classification and incorporating ndvi time-series and texture: a case study of central shandong. Int J Remote Sens 39(23):8703–8723. https://doi.org/10.1080/01431161.2018.1490976
Amani M, Ghorbanian A, Ahmadi SA, Kakooei M, Moghimi A, Mirmazloumi SM, Moghaddam SHA, Mahdavi S, Ghahremanloo M, Parsian S et al (2020) Google earth engine cloud computing platform for remote sensing big data applications: A comprehensive review. IEEE J Selected Topics Appl Earth Observations Remote Sens 13:5326–5350. https://doi.org/10.1109/JSTARS.2020.3021052
Liu L, Xiao X, Qin Y, Wang J, Xu X, Hu Y, Qiao Z (2020) Mapping cropping intensity in china using time series landsat and sentinel-2 images and google earth engine. Remote Sens Environ 239:111624. https://doi.org/10.1016/j.rse.2019.111624
Rikimaru A, Roy P, Miyatake S et al (2002) Tropical forest cover density mapping. Tropical Ecology (India) 43(1)
Zha Y, Gao J, Ni S (2003) Use of normalized difference built-up index in automatically mapping urban areas from tm imagery. Int J Remote Sens 24(3):583–594. https://doi.org/10.1080/01431160304987
Rouse JW, Haas RH, Schell JA, Deering DW et al (1974) Monitoring vegetation systems in the great plains with erts. NASA Spec Publ 351(1):309
Mileva N, Mecklenburg S, Gascon F (2018) New tool for spatio-temporal image fusion in remote sensing: a case study approach using sentinel-2 and sentinel-3 data. Image and Signal Processing for Remote Sensing XXIV, SPIE 10789:198–208. https://doi.org/10.1117/12.2327091
Pinheiro G, Minz S (2023) Image quality assessment of spatiotemporal image fusion: A case study approach using landsat-8 and sentinel-2. In: 43rd Asian Conference on Remote Sensing 2022 (ACRS2022) (2022). Accessed on March 23, 2023. https://aar-s.org/proceeding/ACRS2022/ACRS22_150.pdf
Pinheiro G, Minz S (2023) Image quality evaluation of various pan-sharpening techniques using landsat-8 imagery. In: Ramdane-Cherif A, Singh TP, Tomar R, Choudhury T, Um J-S (eds.) Machine intelligence and data science applications, Springer, Singapore, pp 391–403. https://doi.org/10.1007/978-981-99-1620-7_31
Raj A, Minz S (2022) Spatial granule based clustering technique for hyperspectral images. In: MysuruCon 2022 - 2022 IEEE 2nd Mysore sub section international conference. https://doi.org/10.1109/MYSURUCON55714.2022.9972609
Yao J, Vasilakos AV, Pedrycz W (2013) Granular computing: Perspectives and challenges. IEEE Trans Cybernetics 43(6):1977–1989. https://doi.org/10.1109/TSMCC.2012.2236648
Butenkov S, Zhukov A, Nagorov A, Krivsha N (2017) Granular computing models and methods based on the spatial granulation. Procedia Comput Sci 103:295–302. https://doi.org/10.1016/J.PROCS.2017.01.111
Pedrycz W (2001) Granular computing: An introduction. Annual Conference of the North American fuzzy information processing society - NAFIPS 3:1349–1354. https://doi.org/10.1109/NAFIPS.2001.943745
Yao Y (1996) Two views of the theory of rough sets in finite universes. Int J Approximate Reason 15(4):291–317. https://doi.org/10.1016/S0888-613X(96)00071-0
Achanta R, Süsstrunk S (2017) Superpixels and polygons using simple non-iterative clustering. In: IEEE Conference on computer vision and pattern recognition, IEEE, pp 4651–4660. https://doi.org/10.1109/CVPR.2017.520 Accessed on April 11, 2023
Dang TH, Mai DS, Ngo LT (2019) Multiple kernel collaborative fuzzy clustering algorithm with weighted super-pixels for satellite image land-cover classification. Eng Appl Artif Intell 85:85–98. https://doi.org/10.1016/J.ENGAPPAI.2019.05.004
Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst, Man, Cybernetics. SMC-3(6):610–621. https://doi.org/10.1109/TSMC.1973.4309314
Breiman L (2001) Random forests. Machine Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324/METRICS
Maxwell AE, Strager MP, Warner TA, Ramezan CA, Morgan AN, Pauley CE (2019) Large-area, high spatial resolution land cover mapping using random forests, geobia, and naip orthophotography: Findings and recommendations. Remote Sens 11(12):1409. https://doi.org/10.3390/rs11121409
Pan X, Wang Z, Gao Y, Dang X, Han Y (2022) Detailed and automated classification of land use/land cover using machine learning algorithms in google earth engine. Geocarto Int 37(18):5415–5432. https://doi.org/10.1080/10106049.2021.1917005
Tamiminia H, Salehi B, Mahdianpari M, Quackenbush L, Adeli S, Brisco B (2020) Google earth engine for geo-big data applications: A meta-analysis and systematic review. ISPRS J Photogrammetry Remote Sens 164:152–170. https://doi.org/10.1016/j.isprsjprs.2020.04.001
Belgiu M, Drăgu L (2016) Random forest in remote sensing: A review of applications and future directions. ISPRS J Photogrammetry Remote Sens 114:24–31. https://doi.org/10.1016/J.ISPRSJPRS.2016.01.011
Chen W, Xie X, Wang J, Pradhan B, Hong H, Tien Bui D, Duan Z, Ma J (2016) A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. Environ Earth Sci 75(2):1–13. https://doi.org/10.1007/s12665-015-5235-0
Mahdianpari M, Salehi B, Mohammadimanesh F, Motagh M (2017) Random forest wetland classification using alos-2 l-band, radarsat-2 c-band, and terrasar-x imagery. ISPRS J Photogrammetry Remote Sens 130:13–31. https://doi.org/10.1016/j.isprsjprs.2017.05.010
Xia J, Falco N, Benediktsson JA, Du P, Chanussot J (2017) Hyperspectral image classification with rotation random forest via kpca. IEEE J Selected Topics Appl Earth Observations Remote Sens 10(4):1601–1609. https://doi.org/10.1109/JSTARS.2016.2636877
Sousa C, Fatoyinbo L, Neigh C, Boucka F, Angoue V, Larsen T (2020) Cloud-computing and machine learning in support of country-level land cover and ecosystem extent mapping in liberia and gabon. PLoS One 15(1):0227438. https://doi.org/10.1371/journal.pone.0227438
Hansen MC, Roy DP, Lindquist E, Adusei B, Justice CO, Altstatt A (2008) A method for integrating modis and landsat data for systematic monitoring of forest cover and change in the congo basin. Remote Sens Environ 112(5):2495–2513. https://doi.org/10.1016/j.rse.2007.11.012
Bwangoy J-RB, Hansen MC, Roy DP, De Grandi G, Justice CO (2010) Wetland mapping in the congo basin using optical and radar remotely sensed data and derived topographical indices. Remote Sens Environ 114(1):73–86. https://doi.org/10.1016/j.rse.2009.08.004
Phan TN, Kuch V, Lehnert LW (2020) Land cover classification using google earth engine and random forest classifier–the role of image composition. Remote Sens 12(15):2411. https://doi.org/10.3390/rs12152411
Hosseini SA, Abbaszadeh Shahri A, Asheghi R (2022) Prediction of bedload transport rate using a block combined network structure. Hydrological Sci J 67(1):117–128. https://doi.org/10.1080/02626667.2021.2003367
Kpienbaareh D, Sun X, Wang J, Luginaah I, Kerr RB, Lupafya E, Dakishoni L (2021) Crop type and land cover mapping in northern malawi using the integration of sentinel-1, sentinel-2, and planetscope satellite data. Remote Sens 13(4):700. https://doi.org/10.3390/rs13040700
Khadka D, Zhang J (2023) Geographic object-based image analysis (geobia) for landslide identification using machine learning on google earth engine (gee). PREPRINT. Version 1. https://doi.org/10.21203/rs.3.rs-3299903/v1
Selvaraj R, Amali DGB (2023) Assessment of object-based classification for mapping land use and land cover using google earth. Global NEST J 25(7):131–138. https://doi.org/10.30955/gnj.004829
Sudianto S, Herdiyeni Y, Prasetyo LB (2023) Classification of sugarcane area using landsat 8 and random forest based on phenology knowledge. JOIV: Int J Inf Visualization 7(3-2):1974–1981. https://doi.org/10.30630/joiv.7.3-2.1401
Yang L, Wang L, Abubakar GA, Huang J (2021) High-resolution rice mapping based on snic segmentation and multi-source remote sensing images. Remote Sens 13(6):1148. https://doi.org/10.3390/rs13061148
Zeng Q, Xie Y, Liu K (2019) Assessment of the patterns of urban land covers and impervious surface areas: A case study of shenzhen, china. Physics and Chemistry of the Earth, Parts A/B/C. 110:1–7. https://doi.org/10.1016/j.pce.2019.04.002
Hall-Beyer M (2017) Practical guidelines for choosing glcm textures to use in landscape classification tasks over a range of moderate spatial scales. Remote Sens 9(12):1230. https://doi.org/10.3390/rs9121230
Pinheiro G, Rather IH, Raj A, Minz S, Kumar S (2024) Image quality assessment of multi-satellite pan-sharpening approach: A case study using sentinel-2 synthetic panchromatic image and landsat-8. EAI Endorsed Trans Scalable Inf Syst. https://doi.org/10.4108/eetsis.5496
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Pinheiro, G., Minz, S. Granular computing based segmentation and textural analysis (GrCSTA) framework for object-based LULC classification of fused remote sensing images. Appl Intell 54, 5748–5767 (2024). https://doi.org/10.1007/s10489-024-05469-z
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DOI: https://doi.org/10.1007/s10489-024-05469-z