Automatic segmentation algorithm for high-spatial-resolution remote sensing images based on self-learning super-pixel convolutional network
Super-pixel algorithms based on convolutional neural networks with fuzzy C-means clustering are widely used for high-spatial-resolution remote sensing images segmentation. However, this model requires the number of clusters to be set manually, resulting in a low automation degree due to the complexi...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
|
| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/17538947.2022.2083247 |
| _version_ | 1797678480085745664 |
|---|---|
| author | Zenan Yang Haipeng Niu Liang Huang Xiaoxuan Wang Liangxin Fan Dongyang Xiao |
| author_facet | Zenan Yang Haipeng Niu Liang Huang Xiaoxuan Wang Liangxin Fan Dongyang Xiao |
| author_sort | Zenan Yang |
| collection | DOAJ |
| description | Super-pixel algorithms based on convolutional neural networks with fuzzy C-means clustering are widely used for high-spatial-resolution remote sensing images segmentation. However, this model requires the number of clusters to be set manually, resulting in a low automation degree due to the complexity of the iterative clustering process. To address this problem, a segmentation method based on a self-learning super-pixel network (SLSP-Net) and modified automatic fuzzy clustering (MAFC) is proposed. SLSP-Net performs feature extraction, non-iterative clustering, and gradient reconstruction. A lightweight feature embedder is adopted for feature extraction, thus expanding the receiving range and generating multi-scale features. Automatic matching is used for non-iterative clustering, and the overfitting of the network model is overcome by adaptively adjusting the gradient weight parameters, providing a better irregular super-pixel neighborhood structure. An optimized density peak algorithm is adopted for MAFC. Based on the obtained super-pixel image, this maximizes the robust decision-making interval, which enhances the automation of regional clustering. Finally, prior entropy fuzzy C-means clustering is applied to optimize the robust decision-making and obtain the final segmentation result. Experimental results show that the proposed model offers reduced experimental complexity and achieves good performance, realizing not only automatic image segmentation, but also good segmentation results. |
| first_indexed | 2024-03-11T23:00:22Z |
| format | Article |
| id | doaj.art-45a02a81335a4590a41ee6d308f2e7c1 |
| institution | Directory Open Access Journal |
| issn | 1753-8947 1753-8955 |
| language | English |
| last_indexed | 2024-03-11T23:00:22Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj.art-45a02a81335a4590a41ee6d308f2e7c12023-09-21T14:57:11ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552022-12-011511101112410.1080/17538947.2022.20832472083247Automatic segmentation algorithm for high-spatial-resolution remote sensing images based on self-learning super-pixel convolutional networkZenan Yang0Haipeng Niu1Liang Huang2Xiaoxuan Wang3Liangxin Fan4Dongyang Xiao5Henan Polytechnic UniversityHenan Polytechnic UniversityKunming University of Science and TechnologyHenan Polytechnic UniversityHenan Polytechnic UniversityHenan Polytechnic UniversitySuper-pixel algorithms based on convolutional neural networks with fuzzy C-means clustering are widely used for high-spatial-resolution remote sensing images segmentation. However, this model requires the number of clusters to be set manually, resulting in a low automation degree due to the complexity of the iterative clustering process. To address this problem, a segmentation method based on a self-learning super-pixel network (SLSP-Net) and modified automatic fuzzy clustering (MAFC) is proposed. SLSP-Net performs feature extraction, non-iterative clustering, and gradient reconstruction. A lightweight feature embedder is adopted for feature extraction, thus expanding the receiving range and generating multi-scale features. Automatic matching is used for non-iterative clustering, and the overfitting of the network model is overcome by adaptively adjusting the gradient weight parameters, providing a better irregular super-pixel neighborhood structure. An optimized density peak algorithm is adopted for MAFC. Based on the obtained super-pixel image, this maximizes the robust decision-making interval, which enhances the automation of regional clustering. Finally, prior entropy fuzzy C-means clustering is applied to optimize the robust decision-making and obtain the final segmentation result. Experimental results show that the proposed model offers reduced experimental complexity and achieves good performance, realizing not only automatic image segmentation, but also good segmentation results.http://dx.doi.org/10.1080/17538947.2022.2083247deep convolution neural network modelsuper-pixel algorithmautomatic fuzzy clusteringprior entropy fuzzy c-means clustering algorithmremote sensing images |
| spellingShingle | Zenan Yang Haipeng Niu Liang Huang Xiaoxuan Wang Liangxin Fan Dongyang Xiao Automatic segmentation algorithm for high-spatial-resolution remote sensing images based on self-learning super-pixel convolutional network International Journal of Digital Earth deep convolution neural network model super-pixel algorithm automatic fuzzy clustering prior entropy fuzzy c-means clustering algorithm remote sensing images |
| title | Automatic segmentation algorithm for high-spatial-resolution remote sensing images based on self-learning super-pixel convolutional network |
| title_full | Automatic segmentation algorithm for high-spatial-resolution remote sensing images based on self-learning super-pixel convolutional network |
| title_fullStr | Automatic segmentation algorithm for high-spatial-resolution remote sensing images based on self-learning super-pixel convolutional network |
| title_full_unstemmed | Automatic segmentation algorithm for high-spatial-resolution remote sensing images based on self-learning super-pixel convolutional network |
| title_short | Automatic segmentation algorithm for high-spatial-resolution remote sensing images based on self-learning super-pixel convolutional network |
| title_sort | automatic segmentation algorithm for high spatial resolution remote sensing images based on self learning super pixel convolutional network |
| topic | deep convolution neural network model super-pixel algorithm automatic fuzzy clustering prior entropy fuzzy c-means clustering algorithm remote sensing images |
| url | http://dx.doi.org/10.1080/17538947.2022.2083247 |
| work_keys_str_mv | AT zenanyang automaticsegmentationalgorithmforhighspatialresolutionremotesensingimagesbasedonselflearningsuperpixelconvolutionalnetwork AT haipengniu automaticsegmentationalgorithmforhighspatialresolutionremotesensingimagesbasedonselflearningsuperpixelconvolutionalnetwork AT lianghuang automaticsegmentationalgorithmforhighspatialresolutionremotesensingimagesbasedonselflearningsuperpixelconvolutionalnetwork AT xiaoxuanwang automaticsegmentationalgorithmforhighspatialresolutionremotesensingimagesbasedonselflearningsuperpixelconvolutionalnetwork AT liangxinfan automaticsegmentationalgorithmforhighspatialresolutionremotesensingimagesbasedonselflearningsuperpixelconvolutionalnetwork AT dongyangxiao automaticsegmentationalgorithmforhighspatialresolutionremotesensingimagesbasedonselflearningsuperpixelconvolutionalnetwork |