A lightweight spatiotemporal classification framework for tree species with entropy-based change resistance filter using satellite imagery
The spatiotemporal characteristics of remote sensing data are often time-varying, leading to significant fluctuation and instability in tree species classification results across different years, especially in regions referred to as high-variance areas. To improve the stability and accuracy of the c...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225000962 |
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| author | Biao Zhang Zhichao Wang Boyi Liang Liguo Dong Zebang Feng Mingyang He Zhongke Feng |
| author_facet | Biao Zhang Zhichao Wang Boyi Liang Liguo Dong Zebang Feng Mingyang He Zhongke Feng |
| author_sort | Biao Zhang |
| collection | DOAJ |
| description | The spatiotemporal characteristics of remote sensing data are often time-varying, leading to significant fluctuation and instability in tree species classification results across different years, especially in regions referred to as high-variance areas. To improve the stability and accuracy of the classification results, this study proposes a lightweight spatiotemporal classification framework, with the core algorithm being the Spatiotemporal Entropy-based Change Resistance Filter (STECR-F) algorithm. The STECR-F algorithm integrates the concept of Spatiotemporal Entropy (STE) and, by applying weighted spatiotemporal neighborhood information, suppresses uncertainty in the classification process. It effectively enhances the spatiotemporal consistency of the classification results, particularly in high-variance regions, and reduces classification instability caused by spatiotemporal fluctuations. This study comprehensively evaluates the performance of STECR-F from three dimensions: STE, transfer change, and classification accuracy, and compares it with other methods. The results show that STECR-F significantly reduces the STE value, with an average decrease of 0.3876, effectively mitigating the fluctuation of the classification results. In high-variance regions, the effect of STECR-F is particularly pronounced, with an average decrease in STE value of up to 0.6847. Moreover, STECR-F significantly suppresses random transitions between classes, reducing category transitions by an average of 22.47%, with the maximum reduction reaching 46%. In terms of classification accuracy, STECR-F achieved an overall accuracy of 91.35%, representing an improvement of 8.02% compared to the results without using STECR-F. Additionally, compared to the DMSPN method using only neighborhood information and pattern filtering, STECR-F’s performance improved by 5.86% and 6.42%, respectively. Overall, the STECR-F algorithm effectively addresses the interannual dynamics and uncertainty in tree species classification results. By integrating weighted spatiotemporal neighborhood information, it significantly enhances classification stability and reduces random variability, making it particularly suitable for areas with high spatiotemporal variability and classification uncertainty. |
| format | Article |
| id | doaj-art-19ef1d437062465e8d979e65c37f706f |
| institution | DOAJ |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-19ef1d437062465e8d979e65c37f706f2025-08-20T02:46:29ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-04-0113810444910.1016/j.jag.2025.104449A lightweight spatiotemporal classification framework for tree species with entropy-based change resistance filter using satellite imageryBiao Zhang0Zhichao Wang1Boyi Liang2Liguo Dong3Zebang Feng4Mingyang He5Zhongke Feng6Precision Forestry Key Laboratory of Beijing, Beijing Forestry University, Beijing 100083, ChinaPrecision Forestry Key Laboratory of Beijing, Beijing Forestry University, Beijing 100083, China; Corresponding authors.Precision Forestry Key Laboratory of Beijing, Beijing Forestry University, Beijing 100083, ChinaNingxia Academy of Agriculture and Forestry, Ningxia 750000, ChinaNavInfo Co., Ltd, Beijing 100083, ChinaPrecision Forestry Key Laboratory of Beijing, Beijing Forestry University, Beijing 100083, ChinaPrecision Forestry Key Laboratory of Beijing, Beijing Forestry University, Beijing 100083, China; Corresponding authors.The spatiotemporal characteristics of remote sensing data are often time-varying, leading to significant fluctuation and instability in tree species classification results across different years, especially in regions referred to as high-variance areas. To improve the stability and accuracy of the classification results, this study proposes a lightweight spatiotemporal classification framework, with the core algorithm being the Spatiotemporal Entropy-based Change Resistance Filter (STECR-F) algorithm. The STECR-F algorithm integrates the concept of Spatiotemporal Entropy (STE) and, by applying weighted spatiotemporal neighborhood information, suppresses uncertainty in the classification process. It effectively enhances the spatiotemporal consistency of the classification results, particularly in high-variance regions, and reduces classification instability caused by spatiotemporal fluctuations. This study comprehensively evaluates the performance of STECR-F from three dimensions: STE, transfer change, and classification accuracy, and compares it with other methods. The results show that STECR-F significantly reduces the STE value, with an average decrease of 0.3876, effectively mitigating the fluctuation of the classification results. In high-variance regions, the effect of STECR-F is particularly pronounced, with an average decrease in STE value of up to 0.6847. Moreover, STECR-F significantly suppresses random transitions between classes, reducing category transitions by an average of 22.47%, with the maximum reduction reaching 46%. In terms of classification accuracy, STECR-F achieved an overall accuracy of 91.35%, representing an improvement of 8.02% compared to the results without using STECR-F. Additionally, compared to the DMSPN method using only neighborhood information and pattern filtering, STECR-F’s performance improved by 5.86% and 6.42%, respectively. Overall, the STECR-F algorithm effectively addresses the interannual dynamics and uncertainty in tree species classification results. By integrating weighted spatiotemporal neighborhood information, it significantly enhances classification stability and reduces random variability, making it particularly suitable for areas with high spatiotemporal variability and classification uncertainty.http://www.sciencedirect.com/science/article/pii/S1569843225000962Forest resources surveyLightweight spatiotemporal classification frameworkSpatiotemporal EntropyHigh-variance areas |
| spellingShingle | Biao Zhang Zhichao Wang Boyi Liang Liguo Dong Zebang Feng Mingyang He Zhongke Feng A lightweight spatiotemporal classification framework for tree species with entropy-based change resistance filter using satellite imagery International Journal of Applied Earth Observations and Geoinformation Forest resources survey Lightweight spatiotemporal classification framework Spatiotemporal Entropy High-variance areas |
| title | A lightweight spatiotemporal classification framework for tree species with entropy-based change resistance filter using satellite imagery |
| title_full | A lightweight spatiotemporal classification framework for tree species with entropy-based change resistance filter using satellite imagery |
| title_fullStr | A lightweight spatiotemporal classification framework for tree species with entropy-based change resistance filter using satellite imagery |
| title_full_unstemmed | A lightweight spatiotemporal classification framework for tree species with entropy-based change resistance filter using satellite imagery |
| title_short | A lightweight spatiotemporal classification framework for tree species with entropy-based change resistance filter using satellite imagery |
| title_sort | lightweight spatiotemporal classification framework for tree species with entropy based change resistance filter using satellite imagery |
| topic | Forest resources survey Lightweight spatiotemporal classification framework Spatiotemporal Entropy High-variance areas |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225000962 |
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