Can segmentation evaluation metric be used as an indicator of land cover classification accuracy?
| Autor: | Nataša Đurić, Krištof Oštir, Klemen Ritlop, Klemen Čotar, Andreja Švab Lenarčič |
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| Rok vydání: | 2016 |
| Předmět: |
010504 meteorology & atmospheric sciences
Contextual image classification Segmentation-based object categorization business.industry Computer science 0211 other engineering and technologies Scale-space segmentation Image processing Pattern recognition 02 engineering and technology Image segmentation Land cover computer.software_genre 01 natural sciences ComputingMethodologies_PATTERNRECOGNITION General Earth and Planetary Sciences Segmentation Metric (unit) Artificial intelligence Data mining business computer 021101 geological & geomatics engineering 0105 earth and related environmental sciences |
| Zdroj: | Journal of Applied Remote Sensing. 10:045010 |
| ISSN: | 1931-3195 |
| Popis: | It is a broadly established belief that the segmentation result significantly affects subsequent image classification accuracy. However, the actual correlation between the two has never been evaluated. Such an evaluation would be of considerable importance for any attempts to automate the object-based classification process, as it would reduce the amount of user intervention required to fine-tune the segmentation parameters. We conducted an assessment of segmentation and classification by analyzing 100 different segmentation parameter combinations, 3 classifiers, 5 land cover classes, 20 segmentation evaluation metrics, and 7 classification accuracy measures. The reliability definition of segmentation evaluation metrics as indicators of land cover classification accuracy was based on the linear correlation between the two. All unsupervised metrics that are not based on number of segments have a very strong correlation with all classification measures and are therefore reliable as indicators of land cover classification accuracy. On the other hand, correlation at supervised metrics is dependent on so many factors that it cannot be trusted as a reliable classification quality indicator. Algorithms for land cover classification studied in this paper are widely used; therefore, presented results are applicable to a wider area. |
| Databáze: | OpenAIRE |
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