A Clustering Based Hyper Spectral Image (HSI) Classification and Segmentation for Satellite Remote Sensing
Keywords:
Hyper Spectral Image, Satellite Remote Sensing, Clustering, Classification, SegmentationAbstract
In this paper, a new algorithm has been designated for classification of satellite remote sensing of hyperspectral image. The classification process is based on the three main categories: (1) image fusion, performed using morphological process of both spatial and spectral information available in the remote sensed images. (2) Clustering, which performed in supervised techniques using thresholding effect of image pixel intensity and (3) segmented and texture based image analysis, in this process to achieve a new textural based image clustering to overcome the problem of multi-label images in satellite remote processing. Finally, it gets clustered and result in segmented output. The proposed research contribution is validated by classification experiments using Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) image sensors from the results the overall accuracy of single and multi-label of Salinas’s dataset.
References
- Min Wang, Jiru Huang, and Dongping Ming. “Region-Line Association Constraints for High-Resolution Image Segmentation,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 10 (2), pp. 628-637.
- Yongyue Hu, Jianyu Chen, Delu Pan, and Zengzhou Hao, “Edge-Guided Image Object Detection in Multiscale Segmentation for High-Resolution Remotely Sensed Imagery” IEEE Transactions on Geoscience and Remote Sensing, 2016 ,54( 8),pp. 4702-4711.
- H. Gökhan Akçay and Selim Aksoy, “Automatic Detection of Compound Structures by Joint Selection of Region Groups from a Hierarchical Segmentation” IEEE Transactions on Geoscience and Remote Sensing,2016, 54 (6),pp. 3485-3501.
- Yanfei Zhong, Rongrong Gao, and Liangpei Zhang,“Multiscale and Multifeature Normalized Cut Segmentation for High Spatial Resolution Remote Sensing Imagery” IEEE Transactions On Geoscience And Remote Sensing, 2016,54(10),pp.6061-6075.
- Guiyun Zhou, Shuai Cao, and Junjie Zhou, “Planar Segmentation Using Range Images From Terrestrial Laser Scanning”, IEEE Geoscience And Remote Sensing Letters, 2016,13(2),pp.257-261.
- F. A. Ávila Rodrigues, J. F. S. Rocha Neto, R. C. Pinheiro Marques, F. N. Sombra de Medeiros, and J. Santos Nobre. “SAR Image Segmentation Using the Roughness Information” IEEE Geoscience and Remote Sensing letters, 2016, 13( 2),pp.132-136.
- Zhang Chunsen, Zheng Yiwei, and Feng Chenyi, “Spectral–Spatial Classification of Hyperspectral Images Using Probabilistic Weighted Strategy for Multifeature Fusion” IEEE Geoscience and Remote Sensing letters,2016,13,pp.1562-1566.
- Junping Zhang, Tong Li, Xiaochen Lu, and Zhen Cheng, “Semantic Classification of High-Resolution Remote-Sensing Images Based on Mid-level Features” IEEE Journal Of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(6),pp.2343-2353.
- Ribana Rosche, and Björn Waske, “Shapelet-Based Sparse Representation for Landcover Classification of Hyperspectral Images” IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(3),pp.1623-1634.
- Erlei Zhang, Licheng Jiao, Xiangrong Zhang, Hongying Liu, and Shuang Wang, “Class-Level Joint Sparse Representation for Multifeature-Based Hyperspectral Image Classification” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9( 9),pp.4160-4177.
Downloads
Published
Issue
Section
License
Copyright (c) IJSRSET
This work is licensed under a Creative Commons Attribution 4.0 International License.