INTERVAL TYPE-2 FUZZY BASED NEURAL NETWORK FOR HIGH RESOLUTION REMOTE SENSING IMAGE SEGMENTATION
Recently, high resolution remote sensing image segmentation is a hot issue in image procesing procedures. However, it is a difficult task. The difficulties derive from the uncertainties of pixel segmentation and decision-making model. To this end, we take spatial relationship into consideration when...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2016-06-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B7/385/2016/isprs-archives-XLI-B7-385-2016.pdf |
| _version_ | 1819107133227008000 |
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| author | C. Wang A. Xu C. Li X. Zhao |
| author_facet | C. Wang A. Xu C. Li X. Zhao |
| author_sort | C. Wang |
| collection | DOAJ |
| description | Recently, high resolution remote sensing image segmentation is a hot issue in image procesing procedures. However, it is a difficult task. The difficulties derive from the uncertainties of pixel segmentation and decision-making model. To this end, we take spatial relationship into consideration when constructing the interval type-2 fuzzy neural networks for high resolution remote sensing image segmentation. First, the proposed algorithm constructs a Gaussian model as a type-1 fuzzy model to describe the uncertainty contained in the image. Second, interval type-2 fuzzy model is obtained by blurring the mean and variance in type-1 model. The proposed interval type-2 model can strengthen the expression of uncertainty and simultaneously decrease the uncertainty in the decision model. Then the fuzzy membership function itself and its upper and lower fuzzy membership functions of the training samples are used as the input of neuron network which acts as the decision model in proposed algorithm. Finally, the relationship of neighbour pixels is taken into consideration and the fuzzy membership functions of the detected pixel and its neighbourhood are used to decide the class of each pixel to get the final segmentation result. The proposed algorithm, FCM and HMRF-FCM algorithm and an interval type-2 fuzzy neuron networks without spatial relationships are performed on synthetic and real high resolution remote sensing images. The qualitative and quantitative analyses demonstrate the efficient of the proposed algorithm, especially for homogeneous regions which contains a great difference in its gray level (for example forest). |
| first_indexed | 2024-12-22T02:49:11Z |
| format | Article |
| id | doaj.art-d51f0a3136964c91bdaab7882bfe4041 |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-12-22T02:49:11Z |
| publishDate | 2016-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-d51f0a3136964c91bdaab7882bfe40412022-12-21T18:41:26ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342016-06-01XLI-B738539110.5194/isprs-archives-XLI-B7-385-2016INTERVAL TYPE-2 FUZZY BASED NEURAL NETWORK FOR HIGH RESOLUTION REMOTE SENSING IMAGE SEGMENTATIONC. Wang0A. Xu1C. Li2X. Zhao3School of Mining Industry and Technology, Liaoning Technical University Huludao 125105School of Geomatics, Liaoning Technical University, Fuxin 123000Yunnan Technology Center of Basic Surveying and Mapping, Yunnan 650034 ChinaSchool of Geomatics, Liaoning Technical University, Fuxin 123000Recently, high resolution remote sensing image segmentation is a hot issue in image procesing procedures. However, it is a difficult task. The difficulties derive from the uncertainties of pixel segmentation and decision-making model. To this end, we take spatial relationship into consideration when constructing the interval type-2 fuzzy neural networks for high resolution remote sensing image segmentation. First, the proposed algorithm constructs a Gaussian model as a type-1 fuzzy model to describe the uncertainty contained in the image. Second, interval type-2 fuzzy model is obtained by blurring the mean and variance in type-1 model. The proposed interval type-2 model can strengthen the expression of uncertainty and simultaneously decrease the uncertainty in the decision model. Then the fuzzy membership function itself and its upper and lower fuzzy membership functions of the training samples are used as the input of neuron network which acts as the decision model in proposed algorithm. Finally, the relationship of neighbour pixels is taken into consideration and the fuzzy membership functions of the detected pixel and its neighbourhood are used to decide the class of each pixel to get the final segmentation result. The proposed algorithm, FCM and HMRF-FCM algorithm and an interval type-2 fuzzy neuron networks without spatial relationships are performed on synthetic and real high resolution remote sensing images. The qualitative and quantitative analyses demonstrate the efficient of the proposed algorithm, especially for homogeneous regions which contains a great difference in its gray level (for example forest).https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B7/385/2016/isprs-archives-XLI-B7-385-2016.pdf |
| spellingShingle | C. Wang A. Xu C. Li X. Zhao INTERVAL TYPE-2 FUZZY BASED NEURAL NETWORK FOR HIGH RESOLUTION REMOTE SENSING IMAGE SEGMENTATION The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | INTERVAL TYPE-2 FUZZY BASED NEURAL NETWORK FOR HIGH RESOLUTION REMOTE SENSING IMAGE SEGMENTATION |
| title_full | INTERVAL TYPE-2 FUZZY BASED NEURAL NETWORK FOR HIGH RESOLUTION REMOTE SENSING IMAGE SEGMENTATION |
| title_fullStr | INTERVAL TYPE-2 FUZZY BASED NEURAL NETWORK FOR HIGH RESOLUTION REMOTE SENSING IMAGE SEGMENTATION |
| title_full_unstemmed | INTERVAL TYPE-2 FUZZY BASED NEURAL NETWORK FOR HIGH RESOLUTION REMOTE SENSING IMAGE SEGMENTATION |
| title_short | INTERVAL TYPE-2 FUZZY BASED NEURAL NETWORK FOR HIGH RESOLUTION REMOTE SENSING IMAGE SEGMENTATION |
| title_sort | interval type 2 fuzzy based neural network for high resolution remote sensing image segmentation |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B7/385/2016/isprs-archives-XLI-B7-385-2016.pdf |
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