Abstract
Multi sensor image data used in diverse applications for Earth observation has portrayed immense potential as a resourceful foundation of information in current context. The scenario has kindled the requirement for efficient content-based image identification from the archived image databases to provide increased insight to the remote sensing platform. Machine learning is the buzzword for contemporary data driven decision making in the domain of emerging trends in computer science. Diverse applications of machine learning have exhibited promising outcomes in recent times in the areas of autonomous vehicles, natural language processing, computer vision and web searching. An important application of machine learning is to extract meaningful signatures from the unstructured data. The process facilitates identification of important information in the hour of need. In this work, the authors have explored the application of machine learning for content based image classification with remotely sensed image data. A hybrid approach of machine learning is implemented in this work for enhancing the classification accuracy and to use classification as a pre cursor of retrieval. Further, the approaches are compared with respect to their classification performances. Observed results have revealed the superiority of the hybrid approach of classification over the individual classification results. The feature extraction techniques proposed in this work have ensured higher accuracy compared to state-of-the-art feature extraction techniques.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Maxwell, A.E., Warner, T.A., Fang, F.: Implementation of machine-learning classification in remote sensing: an applied review. Int. J. Remote Sens. 39(9), 2784–2817 (2018)
Cai, Y., et al.: A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach. Remote Sens. Environ. 210, 35–47 (2018)
Yang, J., Wong, M.S., Ho, H.C.: Retrieval of urban surface temperature using remote sensing satellite imagery. In: Dey, N., Bhatt, C., Ashour, Amira S. (eds.) Big Data for Remote Sensing: Visualization, Analysis and Interpretation, pp. 129–154. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-89923-7_5
Das, R., Walia, E.: Partition selection with sparse autoencoders for content based image classification. Neural Comput. Appl. 31, 675–690 (2017)
Zhao, W., Du, S.: Spectral–spatial feature extraction for hyperspectral image classification: a dimension reduction and deep learning approach. IEEE Trans. Geosci. Remote Sens. 54(8), 4544–4554 (2016)
Li, Y., Zhang, Y., Tao, C., Zhu, H.: Content-based high-resolution remote sensing image retrieval via unsupervised feature learning and collaborative affinity metric fusion. Remote Sens. 8(9), 709 (2016)
Zhang, Y., Yang, X., Cattani, C., Rao, R.V., Wang, S., Phillips, P.: Tea category identification using a novel fractional Fourier entropy and Jaya algorithm. Entropy 18(3), 77 (2016)
Gonzales-Barron, U., Butler, F.: A comparison of seven thresholding techniques with the k-means clustering algorithm for measurement of bread-crumb features by digital image analysis. J. Food Eng. 74(2), 268–278 (2006)
Li, H., Liu, L., Huang, W., Yue, C.: An improved fusion algorithm for infrared and visible images based on multi-scale transform. Infrared Phys. Technol. 74, 28–37 (2016)
Kumar, S., Toshniwal, D.: Analysis of hourly road accident counts using hierarchical clustering and cophenetic correlation coefficient (CPCC). J. Big Data 3(1), 13 (2016)
Zhang, L., Li, A., Zhang, Z., Yang, K.: Global and local saliency analysis for the extraction of residential areas in high-spatial-resolution remote sensing image. IEEE Trans. Geosci. Remote Sens. 54(7), 3750–3763 (2016)
Tang, J., Woods, M., Cossell, S., Liu, S., Whitty, M.: Non-productive vine canopy estimation through proximal and remote sensing. IFAC- Papers On-Line 49(16), 398–403 (2016)
Valizadeh, M., Armanfard, N., Komeili, M., Kabir, E.: A novel hybrid algorithm for binarization of badly illuminated document images. In: 2009 14th International CSI Computer Conference, CSICC 2009, pp. 121–126. IEEE, October 2009
Pitkänen, J.: Individual tree detection in digital aerial images by combining locally adaptive binarization and local maxima methods. Can. J. For. Res. 31(5), 832–844 (2001)
Liu, H., Jezek, K.C.: Automated extraction of coastline from satellite imagery by integrating Canny edge detection and locally adaptive thresholding methods. Int. J. Remote Sens. 25(5), 937–958 (2004)
Al-Amri, S.S., Kalyankar, N.V.: Image segmentation by using threshold techniques. arXiv preprint arXiv:1005.4020 (2010)
Rosin, P.L., Ioannidis, E.: Evaluation of global image thresholding for change detection. Pattern Recogn. Lett. 24(14), 2345–2356 (2003)
Manno-Kovács, A., Ok, A.O.: Building detection from monocular VHR images by integrated urban area knowledge. IEEE Geosci. Remote Sens. Lett. 12(10), 2140–2144 (2015)
Liu, H., He, G.: Shape feature extraction of high resolution remote sensing image based on susan and moment invariant. In: Processing of the 2008 Congress on Image and Signal, CISP 2008, vol. 2, pp. 801–807. IEEE, May 2008
Ezer, T., Liu, H.: On the dynamics and morphology of extensive tidal mudflats: integrating remote sensing data with an inundation model of Cook Inlet, Alaska. Ocean Dyn. 60(5), 1307–1318 (2010)
Neubert, M., Herold, H., Meinel, G.: Evaluation of remote sensing image segmentation quality–further results and concepts. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 36(4/C42) (2006)
Katartzis, A., Sahli, H.: A stochastic framework for the identification of building rooftops using a single remote sensing image. IEEE Trans. Geosci. Remote Sens. 46(1), 259–271 (2008)
Eismann, M.T.: Hyperspectral Remote Sensing. SPIE, Bellingham (2012)
Elmahdy, S.I., Mansor, S., Huat, B.B., Mahmod, A.R.: Structural geologic control with the limestone bedrock associated with piling problems using remote sensing and GIS: a modified geomorphological method. Environ. Earth Sci. 66(8), 2185–2195 (2012)
Cipolletti, M.P., Delrieux, C.A., Perillo, G.M., Piccolo, M.C.: Superresolution border segmentation and measurement in remote sensing images. Comput. Geosci. 40, 87–96 (2012)
Forestier, G., Puissant, A., Wemmert, C., Gançarski, P.: Knowledge-based region labeling for remote sensing image interpretation. Comput. Environ. Urban Syst. 36(5), 470–480 (2012)
Hong, G., Zhang, Y., Mercer, B.: A wavelet and IHS integration method to fuse high resolution SAR with moderate resolution multispectral images. Photogramm. Eng. Remote Sens. 75(10), 1213–1223 (2009)
Zhou, X., Liu, J., Liu, S., Cao, L., Zhou, Q., Huang, H.: A GIHS-based spectral preservation fusion method for remote sensing images using edge restored spectral modulation. J. Photogramm. Remote Sens. 88, 16–27 (2014)
Ling, Y., Ehlers, M., Usery, E.L., Madden, M.: FFT-enhanced IHS transform method for fusing high-resolution satellite images. ISPRS J. Photogramm. Remote Sens. 61(6), 381–392 (2007)
Zhang, L., Li, Y., Lu, H., Yamawaki, A., Yang, S., Serikawa, S.: Maximum local energy method and sum modified Laplacian for remote image fusion based on beyond wavelet transform. Appl. Math. Inf. Sci. 7(1S), 149–156 (2013)
Zhu, Q., Shyu, M.L.: Sparse linear integration of content and context modalities for semantic concept retrieval. IEEE Trans. Emerg. Top. Comput. 3(2), 152–160 (2015)
Byun, Y., Choi, J., Han, Y.: An area-based image fusion scheme for the integration of SAR and optical satellite imagery. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 6(5), 2212–2220 (2013)
Su, Y., Lee, C.H., Tu, T.M.: A multi-optional adjustable IHS-BT approach for high resolution optical and SAR image fusion. Chung Cheng Ling Hsueh Pao/J. Chung Cheng Inst. Technol. 42(1), 119–128 (2013)
Choi, J., Yeom, J., Chang, A., Byun, Y., Kim, Y.: Hybrid pansharpening algorithm for high spatial resolution satellite imagery to improve spatial quality. IEEE Geosci. Remote Sens. Lett. 10(3), 490–494 (2013)
Pohl, C., van Genderen, J.: Remote sensing image fusion: an update in the context of digital earth. Int. J. Digit. Earth 7(2), 158–172 (2014)
Rokni, K., Ahmad, A., Solaimani, K., Hazini, S.: A new approach for surface water change detection: integration of pixel level image fusion and image classification techniques. Int. J. Appl. Earth Obs. Geoinf. 34, 226–234 (2015)
Feng, M.L., Tan, Y.P.: Adaptive binarization method for document image analysis. In: 2004 IEEE International Conference on Multimedia and Expo, ICME 2004, vol. 1, pp. 339–342. IEEE, June 2004
Jalba, A.C., Wilkinson, M.H., Roerdink, J.B.: Morphological hat-transform scale spaces and their use in pattern classification. Pattern Recogn. 37(5), 901–915 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer-Verlag GmbH Germany, part of Springer Nature
About this chapter
Cite this chapter
Das, R., De, S., Thepade, S. (2019). Machine Learning in Hybrid Environment for Information Identification with Remotely Sensed Image Data. In: Gavrilova, M., Tan, C. (eds) Transactions on Computational Science XXXIV. Lecture Notes in Computer Science(), vol 11820. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-59958-7_1
Download citation
DOI: https://doi.org/10.1007/978-3-662-59958-7_1
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-59957-0
Online ISBN: 978-3-662-59958-7
eBook Packages: Computer ScienceComputer Science (R0)