Abstract
Aiming at optimal segmentation scale for different surface features with different features in high-resolution remote sensing images takes a lot of experiments and exists subjectivity. This paper proposes an optimal segmentation algorithm, a method that combines principal component analysis (PCA) with fuzzy c-means (FCM). In this method, the initial clustering centers of FCM are generated by sorting values after dimension reduction by PCA on high-resolution remote sensing images. Then using fuzzy c-means algorithm merges the homogenous image units into one object, and thus, we can gain the segmentation results which rule out influence of subjectivity and uncertainty of initial clustering centers and segmentation scale. Our final result, visual evaluation and clustering internal evaluation indicators and segmentation evaluation indicators show that the high-resolution remote sensing images segmentation algorithm based on PCA and FCM is better than original FCM, and other traditional image segmentation methods mentioned in the paper.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ma L, Li M, Ma X et al (2017) A review of supervised object-based land-cover image classification. ISPRS J Photogramm Remote Sensing 130:277–293
Deng SB (2014) ENVI remote sensing image processing methods, 2nd ed. Higher Education Press
Arbiol R, Zhang Y, Palà V (2006) Advanced classification techniques: A review. In: ISPRS Commission VII Mid-term Symposium Remote Sensing: From Pixels to Processes, Enschede, the Netherlands 292–296
Smith A (2010) Image segmentation scale parameter optimization and land cover classification using the Random Forest algorithm. Spat Sci 55(1):69–79
Kim M, Warner TA, Madden M et al (2011) Multi-scale GEOBIA with very high spatial resolution digital aerial imagery: scale, texture and image objects. Int J Remote Sens 32(10):2825–2850
Myint SW, Gober P, Brazel A et al (2011) Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sens Environ 115(5):1145–1161
Hussain M, Chen D, Cheng A et al (2013) Change detection from remotely sensed images: from pixel-based to object-based approaches. ISPRS J Photogramm Remote Sens 80:91–106
Gao RQ, Ou YJ, Chen LX et al (2019) Research on high resolution image classification of Hedi reservoir based on object-oriented method. Sci Surv Mapp 1–14. http://kns.cnki.net/kcms/detail/11.4415.P.20181115.1629.002.html
Chen K, Chen XH (2019) Fuzzy C-means clustering image segmentation algorithm with local spatial information based on ELM. J Data Acquis Process 34(1):100–110
Wu YJ (2018) Modified fuzzy c-means clustering algorithm of image segmentation. Northwest Normal University
Zhang H, Shi W, Hao M et al (2018) An adaptive spatially constrained fuzzy c-means algorithm for multispectral remotely sensed imagery clustering. Int J Remote Sens 39(8):2207–2237
Fan J, Wang J (2018) A two-phase fuzzy clustering algorithm based on neurodynamic optimization with its application for PolSAR image segmentation. IEEE Trans Fuzzy Syst 26(1):72–83
Drǎguţ L, Tiede D, Levick SR (2010) ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. Int J Geogr Inf Sci 24(6):859–871
Fortin MJ, Olson RJ, Ferson S et al (2000) Issues related to the detection of boundaries. Landsc Ecol 15(5):453–466
Yangyang C, Dongping M, Lu X et al (2017) An overview of quantitative experimental methods for segmentation evaluation of high spatial remote sensing images. J Geo-Inf Sci 19(6):818–830
Meinel G, Neubert M (2004) A comparison of segmentation programs for high resolution remote sensing data. Int Arch Photogrammetry Remote Sens 35:1097–1105
Qingchao J, Xuefeng Y (2018) Parallel PCA–KPCA for nonlinear process monitoring. Control Eng Pract 80:17–25
Arora J, Khatter K et al (2019) Fuzzy c-means clustering strategies: a review of distance measures. Softw Eng
Dunn JC (1973) A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J Cybern 3(3):32–57
Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Adv Appl Pattern Recogn 22(1171):203–239
Zhou BJ, Tao YZ et al (2018) Optimizing k-means initial clustering centers by minimizing. Comput Eng Appl 54(910)(15):53–57
Jiang WB, Liu LP, Sun XH (2019) K-means model based on adaptive weight method for remote sensing image segmentation. Comput Appl Softw 5:231–234+261
Tong X-Y et al (2018) Learning transferable deep models for land-use classification with high-resolution remote sensing images. [Online]. Available http://arxiv.org/abs/1807.05713
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Jiang, C., Huo, H., Feng, Q. (2020). A High-Resolution Remote Sensing Images Segmentation Algorithm Based on PCA and Fuzzy C-Means. In: Wang, L., Wu, Y., Gong, J. (eds) Proceedings of the 6th China High Resolution Earth Observation Conference (CHREOC 2019). CHREOC 2019. Lecture Notes in Electrical Engineering, vol 657. Springer, Singapore. https://doi.org/10.1007/978-981-15-3947-3_40
Download citation
DOI: https://doi.org/10.1007/978-981-15-3947-3_40
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-3946-6
Online ISBN: 978-981-15-3947-3
eBook Packages: EngineeringEngineering (R0)