Abstract
Satellite image classification is a significant piece of utilizations in different fields, for example, horticulture, nature observing, and disaster management. This work is expected to improve the spatial and spectral information of satellite images by using higher request bits of knowledge in a blend with power shade l inundation utilizing Adaptive Supervised Multi-Resolution demonstrating classification approach. The proposed Adaptive Supervised Multi-Resolution based strategy that naturally orders the various regions from spatiotransient remote detecting pictures. Initially, a kernel representation has been planned by the structure of multispectral and temporal remote detecting information. Also, the Adaptive Supervised Multi-Resolution system with tweaked parameters has been proposed for preparing region tests and learning spatiotemporal discriminative representations. The following parameters are used to assess the execution of the proposed Adaptive Supervised Multi-Resolution: sensitivity, specificity, accuracy, and false classification ratio. To reduce the dimensionality of multi-band satellite images, the autonomous part assessment is used, which uses the higher-organize experiences of the data independently. The exhibition of the proposed technique was approved through simulation utilizing the MATLAB programming. Compared with ordinary satellite image classifier with a proposed Adaptive Supervised Multi-Resolution strategy based classifier execution by accomplishing the result of 97.695% in accuracy, 94.815% in sensitivity, and 97.75% in specificity.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Change history
04 July 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s12652-022-04276-z
References
Borengasser M, Hungate WS, Watkins R (2007) Hyperspectral remote sensing: principles and applications. CRC Press, Boca Raton
Camps-Valls G, Tuia D, Bruzzone L, Benediktsson J (2013) Advances in hyperspectral image classification: earth monitoring with statistical learning methods. IEEE Signal Process Mag. https://doi.org/10.1109/MSP.2013.2279179
Canyi L, Hai M, Gui J, Zhu L, Lei Y-K (2013) Face recognition via weighted sparse representation. J Vis Commun Image Represent 24:111–116. https://doi.org/10.1016/j.jvcir.2012.05.003
Chen Y, Nasrabadi NM, Tran T (2011) Hyperspectral image classification using dictionary-based sparse representation. IEEE Trans Geosci Remote Sens 49:3973–3985. https://doi.org/10.1109/TGRS.2011.2129595
Chen Y, Nasrabadi NM, Tran T (2013) Hyperspectral image classification via kernel sparse representation. IEEE Trans Geosci Remote Sens 51:217–231. https://doi.org/10.1109/TGRS.2012.2201730
Cierniewski J, Kazmierowski C, Królewicz S, Piekarczyk J, Wrobel M, Zagajewski B (2014) Effects of different illumination and observation techniques of cultivated soils on their hyperspectral bidirectional measurements under field and laboratory conditions. IEEE J Sel Top Appl Earth Obs Remote Sens 7:2525–2530. https://doi.org/10.1109/JSTARS.2014.2298098
Cui J, Bokun Y, Dong X, Zhang S, Zhang J, Tian F, Wang R (2015) Temperature and emissivity separation and mineral map** based on airborne TASI hyperspectral thermal infrared data. Int J Appl Earth Obs Geoinf. https://doi.org/10.1016/j.jag.2015.03.014
Gan L, Du P, **a J, Meng Y (2017) Kernel fused representation-based classifier for hyperspectral imagery. IEEE Geosci Remote Sens Lett. https://doi.org/10.1109/LGRS.2017.2671852
Gao S, Tsang I, Chia L-T (2010) Kernel sparse representation for image classification and face recognition. Eur Conf Comput Vis 6314:1–14. https://doi.org/10.1007/978-3-642-15561-1_1
Khaire UM, Dhanalakshmi R (2020) High-dimensional microarray dataset classification using an improved adam optimizer (iAdam). J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-020-01832-3
Li W, Du Q (2014) Joint within-class collaborative representation for hyperspectral image classification. IEEE J Sel Top Appl Earth Obs Remote Sens 7:2200–2208. https://doi.org/10.1109/JSTARS.2014.2306956
Li W, Tramel E, Prasad S, Fowler J (2014) Nearest regularized subspace for hyperspectral classification. IEEE Trans Geosci Remote Sens 52:477–489. https://doi.org/10.1109/TGRS.2013.2241773
Liu J, Wu Z, Wei Z, **ao L, Sun L (2013) Spatial-spectral kernel sparse representation for hyperspectral image classification. IEEE J Sel Top Appl Earth Obs Remote Sens 6:2462–2471. https://doi.org/10.1109/JSTARS.2013.2252150
Luft L, Neumann C, Freude M, Blaum N, Jeltsch F (2014) Hyperspectral modeling of ecological indicators—a new approach for monitoring former military training areas. Ecol Ind 46:264–285. https://doi.org/10.1016/j.ecolind.2014.06.025
Peng X, Tang HZL, Yi Z, **ao S (2015) A unified framework for representation-based subspace clustering of out-of-sample and large-scale data. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2015.2490080
Peng X, Lu J, Yi Z (2016a) Automatic subspace learning via principal coefficients embedding. IEEE Trans Cybern. https://doi.org/10.1109/tcyb.2016.2572306
Peng X, Yu Z, Yi Z, Tang H (2016b) Constructing the L2-graph for robust subspace learning and subspace clustering. IEEE Trans Cybern 47:1–14. https://doi.org/10.1109/TCYB.2016.2536752
Wang Q, Wan J, Yuan Y (2005) Locality constraint distance metric learning for traffic congestion detection. J Pattern Recognit 75:272–281
Wang Q, Lin J, Yuan Y (2016) Salient band selection for hyperspectral image classification via manifold ranking. IEEE Trans Neural Netw Learn Syst 27:1–11. https://doi.org/10.1109/TNNLS.2015.2477537
Wang Q, Chen M, Li X (2017) Quantifying and detecting collective motion by manifold learning. In: 31st AAAI Conference Artificial Intelligence, AAAI Publications. pp 4292–4298
Yuan Y, Lin J, Qi Wang (2015) Hyperspectral image classification via multitask joint sparse representation and stepwise MRF optimization. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2015.2484324
Zhang L, Yang M, Feng X, Ma Y, Zhang D (2012) Collaborative representation based classification for face recognition, vol 1204. ar**v:1204.2358
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12652-022-04276-z
About this article
Cite this article
Saraswathi, S., Madheswaran, M. RETRACTED ARTICLE: Adaptive supervised multi-resolution approach based modeling of performance improvement in satellite image classification. J Ambient Intell Human Comput 12, 6421–6431 (2021). https://doi.org/10.1007/s12652-020-02251-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-020-02251-0