Abstract
A smart system integrates appliances of sensing, acquisition, classification and managing with regard to interpreting and analyzing a situation to generate decisions depending on the available data in a predictive way. Remotely sensed images are an essential tool for evaluating and analyzing land cover dynamics, particularly for forest-cover change. The remote data gathered for this operation from different sensors are of high spatial resolution and thus suffer from high interclass and low intraclass vulnerability issues which retards classification accuracy. To address this problem, in this research analysis, a smart logistic fusion-based supervised multi-class classification (SLFSMC) model is proposed to obtain a thematic map of different land cover types and thereby performing smart actions. In the pre-processing stage of the proposed work, a pair of closing and opening morphological operations is employed to produce the fused image to exploit the contextual information of adjacent pixels. Thereafter quality assessment of the fused image is estimated on four fusion metrics. In the second phase, this fused image is taken as input to the proposed classifiers. Afterward, a multi-class classification model is designed based on the supervised learning concept to generate maps for analyzing and exporting decisions based on any critical climatic situation. In our paper, for estimating the performance of proposed SLFSMC among few conventional classification techniques such as the Naïve Bayes classifier, decision tree, Support vector machine, and K-nearest neighbors, a statistical tool called as Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is involved. We have implemented proposed SLFSMC system on some of the regions of Victoria, a state of Australia, after the deforestation caused due to different reasons.
Similar content being viewed by others
References
Giri C, Long J. Land cover characterization and mapping of South America for the year 2010 using Landsat 30 m satellite data. Remote Sens. 2014;6(10):9494–510.
Gómez C, White JC, Wulder MA. Optical remotely sensed time series data for land cover classification: a review. ISPRS J Photogramm Remote Sens. 2016;116:55–72.
Kim C. Land use classification and land-use change analysis using satellite images in Lombok Island, Indonesia. Forest Sci Technol. 2016;12(4):183–91.
Bosecker RR. Sampling methods in agriculture. National Agricultural Statistics Service, US Department of Agriculture; 1988.
Tsoeleng LT, Odindi J, Mhangara P. A comparison of two morphological techniques in the classification of urban land cover. Remote Sens. 2020;12(7):1089.
Hu T, Yang J, Li X, Gong P. mapping urban land use by using Landsat images and open 340 social data. Remote Sens. 2016;8(2):151.
Carrizosa E, Morales DR. Supervised classification and mathematical optimization. Comput Oper Res. 2013;40(1):150–65.
Puletti N, Perria R, Storchi P. Unsupervised classification of very high remotely sensed images for grapevine rows detection. Eur J Remote Sens. 2014;47(1):45–54.
Campagnolo ML, Cerdeira JO. Contextual classification of remotely sensed images with integer linear programming. In: Proceedings of Comp IMAGE-Computational Modelling of Objects Represented in Images: Fundamentals, Methods, and Applications, 2006, pp. 123–128.
Gong P, Miller J, Freemantle J, Chen B. Spectral decomposition of Landsat thematic mapper data for urban land cover mapping. In: Proceedings of the 14th Canadian Symposium on Remote Sensing, 1991, pp. 458–461.
Duro DC, Franklin SE, Dubé MG. A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using spot-5 hrg imagery. Remote Sens Environ. 2012;118:259–72.
Lillesand T, Kiefer RW, Chipman J. Remote sensing and image interpretation. John Wiley & Sons; 2015.
Chaji N, Ghassemian H. Texture-gradient-based contour detection. EURASIP J Adv Signal Process. 2006;2006:1–8.
Chang C-C, Lin C-J. Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol (TIST). 2011;2(3):1–27.
Chanussot J, Benediktsson JA, Fauvel M. Classification of remote sensing images from urban areas using a fuzzy possibilistic model. IEEE Geosci Remote Sens Lett. 2006;3(1):40–4.
Soille P, Pesaresi M. Advances in mathematical morphology applied to geoscience and remote sensing. IEEE Trans Geosci Remote Sens. 2002;40(9):2042–55.
Haralick R. Statistical and structural approaches to texture. Digital Image Process Anal. 1985;2:304–22.
Pelletier C, Valero S, Inglada J, Champion N, Marais Sicre C, Dedieu G. Effect of training class label noise on classification performances for land cover mapping with satellite image time series. Remote Sens. 2017;9(2):173.
Bégué A, Arvor D, Bellon B, Betbeder J, De Abelleyra D, Ferraz RPD, Lebourgeois V, Lelong C, Simões M, Verón SR. Remote sensing and cropping practices: a review. Remote Sens. 2018;10(1):99.
Jinju J, Santhi N, Ramar K, Bama BS. Spatial frequency discrete wavelet transform image fusion technique for remote sensing applications. Eng Sci Technol Int J. 2019;22(3):715–26.
MohanRajan SN, Loganathan A, Manoharan P. Survey on land use/land cover (LU/LC) change analysis in remote sensing and GIS environment: techniques and challenges. Environ Sci Pollut Res. 2020;27:29900–26.
Wulder MA, Coops NC, Roy DP, White JC, Hermosilla T. Land cover 2.0. Int J Remote Sens. 2018;39(12):4254–84.
Sahu M and Dash R. A fusion-based land cover classification model using remote sensed images. Intell Decis Technol. 2022;16(3):1–13.
Myint SW, Gober P, Brazel A, Grossman-Clarke S, Weng Q. Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sens Environ. 2011;115(5):1145–61.
Tso BC, Mather PM. Classification of multisource remote sensing imagery using a genetic algorithm and Markov random fields. IEEE Trans Geosci Remote Sens. 1999;37(3):1255–60.
Stuckens J, Coppin P, Bauer M. Integrating contextual information with per-pixel classification for improved land cover classification. Remote Sens Environ. 2000;71(3):282–96.
Atkinson PM, Naser DK. A geostatistically weighted KNN classifier for remotely sensed imagery. Geogr Anal. 2010;42(2):204–25.
Moser G, Serpico SB, Benediktsson JA. Land-cover mapping by Markov modeling of spatial–contextual information in very-high-resolution remote sensing images. Proc IEEE. 2012;101(3):631–51.
Jensen JR. Remote sensing of the environment: an earth resource perspective 2/e. Pearson Education India; 2009.
Carleer A, Wolff E. Urban land cover multi-level region-based classification of VHR data by selecting relevant features. Int J Remote Sens. 2006;27(6):1035–51.
Haralick RM, Sternberg SR, Zhuang X. Image analysis using mathematical morphology. IEEE Trans Pattern Anal Mach Intell. 1987;PAMI-9(4):532–50.
Fauvel M, Benediktsson JA, Chanussot J, Sveinsson JR. Spectral and spatial classification of hyperspectral data using SVM and morphological profiles. IEEE Trans Geosci Remote Sens. 2008;46(11):3804–14.
Thanh Noi P, Kappas M. Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using sentinel-2 imagery. Sensors. 2018;18(1):18.
Azzari G, Lobell D. Landsat-based classification in the cloud: an opportunity for a paradigm shift in land cover monitoring. Remote Sens Environ. 2017;202:64–74.
Lu D, Weng Q. A survey of image classification methods and techniques for improving classification performance. Int J Remote Sens. 2007;28(5):823–70.
Prasad AM, Iverson LR, Liaw A. Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems. 2006;9(2):181–99.
Heydari SS, Mountrakis G. Effect of classifier selection, reference sample size, reference class distribution and scene heterogeneity in per-pixel classification accuracy using 26 Landsat sites. Remote Sens Environ. 2018;204:648–58.
Sitthi A, Nagai M, Dailey M, Ninsawat S. exploring land use and land cover of geotagged social-sensing images using naive Bayes classifier. Sustainability. 2016;8(9):921.
Ballestero E, Romero C. Multiple criteria decisions making and its applications to economic problems. Springer Science & Business Media; 2013.
Othman MR, Wozny G, Repke J. Selection of sustainable chemical process design using anp: a biodiesel case study. In: Proceedings of the International Symposium on the Analytic Hierarchy Process, 2011.
Saaty, T. L. What is the analytic hierarchy process? In Mathematical models for decision support. Springer, Berlin, Heidelberg, 1988, 109–121.
Ferreira L, Borenstein D, Santi E. Hybrid fuzzy madm ranking procedure for better alternative discrimination. Eng Appl Artif Intell. 2016;50:71–82.
Behzadian M, Otaghsara SK, Yazdani M, Ignatius J. A state-of-the-art survey of TOPSIS applications. Expert Syst Appl. 2012;39(17):13051–69.
Bradshaw CJ. little left to lose: deforestation and forest degradation in Australia since European colonization. J Plant Ecol. 2012;5(1):109–20 (435).
Hu T, van Dijk AI, Renzullo LJ, Xu Z, He J, Tian S, Zhou J, Li H. on agricultural drought monitoring in Australia using Himawari-8 geostationary thermal infrared observations. Int J Appl Earth Obs Geoinf. 2020;91: 102153.
Ghosh S, Biswas D, Biswas S, Sarkar DC, Sarkar PP. Soil classification from large imagery databases using a neuro-fuzzy classifier. Can J Electr Comput Eng. 2016;39(4):333–43.
Bey A, Sánchez-Paus Díaz A, Maniatis D, Marchi G, Mollicone D, Ricci S, Bastin J-F, Moore R, Federici S, Rezende M, et al. Collect earth: land use and land cover assessment through augmented visual interpretation. Remote Sens. 2016;8(10):807.
Wulder MA, Loveland TR, Roy DP, Crawford CJ, Masek JG, Woodcock CE, Allen RG, Anderson MC, Belward AS, Cohen WB, et al. Current status of Landsat program, science, and applications. Remote Sens Environ. 2019;225:127–47.
Hermosilla T, Wulder MA, White JC, Coops NC, Hobart GW, Campbell LB. Mass data processing of time series Landsat imagery: pixels to data products for forest monitoring. Int J Digital Earth. 2016;9(11):1035–54.
Phiri D, Morgenroth J. Developments in Landsat land cover classification methods: a review. Remote Sens. 2017;9(9):967.
Ma L, Li M, Ma X, Cheng L, Du P, Liu Y. A review of supervised object-based land-cover image classification. ISPRS J Photogramm Remote Sens. 2017;130:277–93.
Nishii R, Tanaka S. Accuracy and inaccuracy assessments in land-cover classification. IEEE Trans Geosci Remote Sens. 1999;37(1):491–8.
Chen P. Effects of normalization on the entropy-based TOPSIS method. Expert Syst Appl. 2019;136:33–41.
de Lima Silva DF, Ferreira L, de Almeida-Filho AT. A new preference disaggregation TOPSIS approach applied to sort corporate bonds based on financial statements and expert’s assessment. Expert Syst Appl. 2020;152: 113369.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Sahu, M., Dash, R., Mishra, S.K. et al. A Smart Logistic Classification Method for Remote Sensed Image Land Cover Data. SN COMPUT. SCI. 3, 477 (2022). https://doi.org/10.1007/s42979-022-01378-5
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s42979-022-01378-5