Robust Semi-supervised Kernel-FCM Algorithm Incorporating Local Spatial Information for Remote Sensing Image Classification
| Autor: | Nu Wen, Qiang Zhao, Chengjie Zhu, Shizhi Yang, Shengcheng Cui |
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| Rok vydání: | 2013 |
| Předmět: |
Iterative and incremental development
Contextual image classification business.industry Computer science Geography Planning and Development ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Image segmentation Fuzzy logic Multispectral pattern recognition ComputingMethodologies_PATTERNRECOGNITION Kernel method Kernel (image processing) Computer Science::Computer Vision and Pattern Recognition Earth and Planetary Sciences (miscellaneous) Artificial intelligence business Spatial analysis Algorithm Remote sensing |
| Zdroj: | Journal of the Indian Society of Remote Sensing. 42:35-49 |
| ISSN: | 0974-3006 0255-660X |
| DOI: | 10.1007/s12524-013-0296-x |
| Popis: | Fuzzy c-means (FCM) algorithm is a popular method in image segmentation and image classification. However, the traditional FCM algorithm cannot achieve satisfactory classification results because remote sensing image data are not subjected to Gaussian distribution, contain some types of noise, are nonlinear, and lack labeled data. This paper presents a robust semi-supervised kernel-FCM algorithm incorporating local spatial information (RSSKFCM_S) to solve the aforementioned problems. In the proposed algorithm, insensitivity to noise is enhanced by introducing contextual spatial information. The non-Euclidean structure and the problem in nonlinearity are resolved through kernel methods. Semi-supervised learning technique is utilized to supervise the iterative process to reduce step number and improve classification accuracy. Finally, the performance of the proposed RSSKFCM_S algorithm is tested and compared with several similar approaches. Experimental results for the multispectral remote sensing image show that the RSSKFCM_S algorithm is more effective and efficient. |
| Databáze: | OpenAIRE |
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