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A lightweight spatiotemporal classification framework for tree species with entropy-based change resistance filter using satellite imagery

文献类型： 外文期刊

作者： Zhang, Biao 1 ; Wang, Zhichao 1 ; Liang, Boyi 1 ; Dong, Liguo 2 ; Feng, Zebang 3 ; He, Mingyang 1 ; Feng, Zhongke 1 ;

作者机构： 1.Beijing Forestry Univ, Precis Forestry Key Lab Beijing, Beijing 100083, Peoples R China

2.Ningxia Acad Agr & Forestry, Yinchuan 750000, Ningxia, Peoples R China

3.NavInfo Co Ltd, Beijing 100083, Peoples R China

关键词： Forest resources survey; Lightweight spatiotemporal classification; framework; Spatiotemporal Entropy; High-variance areas

期刊名称：INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION （ 影响因子：8.6； 五年影响因子：8.6 ）

ISSN： 1569-8432

年卷期： 2025 年 138 卷

页码：

收录情况： SCI

摘要： 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 Entropybased 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.

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