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森林资源管理与监测的早期预警智能算法系统。

An Early Warning Intelligent Algorithm System for Forest Resource Management and Monitoring.

机构信息

College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China.

出版信息

Comput Intell Neurosci. 2022 Oct 11;2022:4250462. doi: 10.1155/2022/4250462. eCollection 2022.

DOI:10.1155/2022/4250462
PMID:36268147
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9578854/
Abstract

The development of remote sensing technology has passed an effective means for forest resource management and monitoring, but remote sensing technology is limited by sensor hardware equipment, and the quality of remote sensing image data is low, which is difficult to meet the needs of forest resource change monitoring. This paper presents a remote sensing image classification method based on the combination of the SSIF algorithm and wavelet denoising. Forest information is extracted from PALSAR/PALSAR-2 radar remote sensing data. The forest distribution map is generated by pixel level fusion algorithm, and the accuracy of the forest distribution map is evaluated by a confusion matrix. The remote sensing image is spatio-temporal fused by the SSIF algorithm to capture more details of forest distribution. The simulation analysis shows that the overall accuracy of the forest classification results obtained by the fusion algorithm is 96% ± 1, and the kappa coefficient is 0.66. The accuracy of forest recognition meets the requirements.

摘要

遥感技术的发展已经成为森林资源管理和监测的有效手段，但遥感技术受到传感器硬件设备的限制，且遥感图像数据质量较低，难以满足森林资源变化监测的需求。本文提出了一种基于 SSIF 算法和小波去噪相结合的遥感图像分类方法。从 PALSAR/PALSAR-2 雷达遥感数据中提取森林信息，通过像素级融合算法生成森林分布图，并通过混淆矩阵评估森林分布图的精度。通过 SSIF 算法对遥感图像进行时空融合，以捕捉更多的森林分布细节。仿真分析表明，融合算法得到的森林分类结果的总体精度为 96%±1，kappa 系数为 0.66。森林识别的准确率满足要求。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a968/9578854/d79ddfabc40f/CIN2022-4250462.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a968/9578854/83ea075ad8f9/CIN2022-4250462.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a968/9578854/9b82f4cf2044/CIN2022-4250462.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a968/9578854/061960ab2708/CIN2022-4250462.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a968/9578854/d79ddfabc40f/CIN2022-4250462.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a968/9578854/83ea075ad8f9/CIN2022-4250462.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a968/9578854/9b82f4cf2044/CIN2022-4250462.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a968/9578854/061960ab2708/CIN2022-4250462.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a968/9578854/d79ddfabc40f/CIN2022-4250462.004.jpg

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