Improving the Accuracy of Land Cover Mapping by Distributing Training Samples
| العنوان: | Improving the Accuracy of Land Cover Mapping by Distributing Training Samples |
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| المؤلفون: | Quanlong Feng, Yuanyuan Zhao, Weijian Yu, Liuyue Wang, Zaiying Ma, Hao Guo, Bingbo Gao, Donglin Tan, Chenxi Li |
| المصدر: | Remote Sensing Volume 13 Issue 22 Pages: 4594 Remote Sensing, Vol 13, Iss 4594, p 4594 (2021) |
| بيانات النشر: | Multidisciplinary Digital Publishing Institute, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | supervised classification, spatial distribution, Computer science, Science, Sampling (statistics), Systematic sampling, Sample (statistics), Stratified sampling, Latin hypercube sampling, Sampling distribution, land cover, Sample size determination, Statistics, General Earth and Planetary Sciences, Sample collection, training samples |
| الوصف: | High-quality training samples are essential for accurate land cover classification. Due to the difficulties in collecting a large number of training samples, it is of great significance to collect a high-quality sample dataset with a limited sample size but effective sample distribution. In this paper, we proposed an object-oriented sampling approach by segmenting image blocks expanded from systematically distributed seeds (object-oriented sampling approach) and carried out a rigorous comparison of seven sampling strategies, including random sampling, systematic sampling, stratified sampling (stratified sampling with the strata of land cover classes based on classification product, Latin hypercube sampling, and spatial Latin hypercube sampling), object-oriented sampling, and manual sampling, to explore the impact of training sample distribution on the accuracy of land cover classification when the samples are limited. Five study areas from different climate zones were selected along the China–Mongolia border. Our research identified the proposed object-oriented sampling approach as the first-choice sampling strategy in collecting training samples. This approach improved the diversity and completeness of the training sample set. Stratified sampling with strata defined by the combination of different attributes and stratified sampling with the strata of land cover classes had their limitations, and they performed well in specific situations when we have enough prior knowledge or high-accuracy product. Manual sampling was greatly influenced by the experience of interpreters. All these sampling strategies mentioned above outperformed random sampling and systematic sampling in this study. The results indicate that the sampling strategies of training datasets do have great impacts on the land cover classification accuracies when the sample size is limited. This paper will provide guidance for efficient training sample collection to increase classification accuracies. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 2072-4292 |
| DOI: | 10.3390/rs13224594 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e99f8a3fe61386039c05377f3a63a74e |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....e99f8a3fe61386039c05377f3a63a74e |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 20724292 |
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| DOI: | 10.3390/rs13224594 |