Characterising the Thematic Content of Image Pixels with Topologically Structured Clustering
The location of a pixel in feature space is a function of its thematic composition. The latter is central to an image classification analysis, notably as an input (e.g., training data for a supervised classifier) and/or an output (e.g., predicted class label). Whether as an input to or output from a...
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| Định dạng: | Bài viết |
| Ngôn ngữ: | English |
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MDPI AG
2025-01-01
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| Loạt: | Remote Sensing |
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| Truy cập trực tuyến: | https://www.mdpi.com/2072-4292/17/1/130 |
| _version_ | 1826882820254990336 |
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| author | Giles M. Foody |
| author_facet | Giles M. Foody |
| author_sort | Giles M. Foody |
| collection | DOAJ |
| description | The location of a pixel in feature space is a function of its thematic composition. The latter is central to an image classification analysis, notably as an input (e.g., training data for a supervised classifier) and/or an output (e.g., predicted class label). Whether as an input to or output from a classification, little if any information beyond a class label is typically available for a pixel. The Kohonen self-organising feature map (SOFM) neural network however offers a means to both cluster together spectrally similar pixels that can be allocated suitable class labels and indicate relative thematic similarity of the clusters generated. Here, the thematic composition of pixels allocated to clusters represented by individual SOFM output units was explored with two remotely sensed data sets. It is shown that much of the spectral information of the input image data is maintained in the production of the SOFM output. This output provides a topologically structured representation of the image data, allowing spectrally similar pixels to be grouped together and the similarity of different clusters to be assessed. In particular, it is shown that the thematic composition of both pure and mixed pixels can be characterised by a SOFM. The location of the output unit in the output layer of the SOFM associated with a pixel conveys information on its thematic composition. Pixels in spatially close output units are more similar spectrally and thematically than those in more distant units. This situation also enables specific sub-areas of interest in the SOFM output space and/or feature space to be identified. This may, for example, provide a means to target efforts in training data acquisition for supervised classification as the most useful training cases may have a tendency to lie within specific sub-areas of feature space. |
| first_indexed | 2025-02-17T03:28:54Z |
| format | Article |
| id | doaj.art-1c7f2a3fa2f7435a91f54fd64c29cfd2 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2025-02-17T03:28:54Z |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-1c7f2a3fa2f7435a91f54fd64c29cfd22025-01-10T13:20:19ZengMDPI AGRemote Sensing2072-42922025-01-0117113010.3390/rs17010130Characterising the Thematic Content of Image Pixels with Topologically Structured ClusteringGiles M. Foody0School of Geography, University of Nottingham, Nottingham NG7 2RD, UKThe location of a pixel in feature space is a function of its thematic composition. The latter is central to an image classification analysis, notably as an input (e.g., training data for a supervised classifier) and/or an output (e.g., predicted class label). Whether as an input to or output from a classification, little if any information beyond a class label is typically available for a pixel. The Kohonen self-organising feature map (SOFM) neural network however offers a means to both cluster together spectrally similar pixels that can be allocated suitable class labels and indicate relative thematic similarity of the clusters generated. Here, the thematic composition of pixels allocated to clusters represented by individual SOFM output units was explored with two remotely sensed data sets. It is shown that much of the spectral information of the input image data is maintained in the production of the SOFM output. This output provides a topologically structured representation of the image data, allowing spectrally similar pixels to be grouped together and the similarity of different clusters to be assessed. In particular, it is shown that the thematic composition of both pure and mixed pixels can be characterised by a SOFM. The location of the output unit in the output layer of the SOFM associated with a pixel conveys information on its thematic composition. Pixels in spatially close output units are more similar spectrally and thematically than those in more distant units. This situation also enables specific sub-areas of interest in the SOFM output space and/or feature space to be identified. This may, for example, provide a means to target efforts in training data acquisition for supervised classification as the most useful training cases may have a tendency to lie within specific sub-areas of feature space.https://www.mdpi.com/2072-4292/17/1/130ground datareference datatraining sitesunsupervised classificationsupervised classificationKohonen neural network |
| spellingShingle | Giles M. Foody Characterising the Thematic Content of Image Pixels with Topologically Structured Clustering Remote Sensing ground data reference data training sites unsupervised classification supervised classification Kohonen neural network |
| title | Characterising the Thematic Content of Image Pixels with Topologically Structured Clustering |
| title_full | Characterising the Thematic Content of Image Pixels with Topologically Structured Clustering |
| title_fullStr | Characterising the Thematic Content of Image Pixels with Topologically Structured Clustering |
| title_full_unstemmed | Characterising the Thematic Content of Image Pixels with Topologically Structured Clustering |
| title_short | Characterising the Thematic Content of Image Pixels with Topologically Structured Clustering |
| title_sort | characterising the thematic content of image pixels with topologically structured clustering |
| topic | ground data reference data training sites unsupervised classification supervised classification Kohonen neural network |
| url | https://www.mdpi.com/2072-4292/17/1/130 |
| work_keys_str_mv | AT gilesmfoody characterisingthethematiccontentofimagepixelswithtopologicallystructuredclustering |