基于无人机图像的非典型传统村落景观及其空间分布模式分类模型

Drones Pub Date : 2024-07-04 DOI:10.3390/drones8070297
Shaojiang Zheng, Lili Wei, Houjie Yu, Weili Kou
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引用次数: 0

摘要

对于非典型传统村落而言,其宝贵的历史痕迹和文化记忆就保存在现存的村落景观中。快速、准确地获取村落各种地表要素的空间信息,是为非典型村落的保护、进步和发展提供科学、合理、可行的规划设计方案的重要前提。本研究以前锋村为例,提出了基于无人机影像的非典型传统村落景观分类模型(ATVLUI),利用无人机 RGB 图像,采用面向对象的模糊逻辑成员分类技术,根据对象的光谱、纹理、几何形状和文脉关系提取对象,旨在精确提取非典型传统村落景观。在景观信息的基础上,计算景观格局指数,探索不同景观的空间分布特征,分析作为非典型传统村落缩影的前锋村的现状。并据此提出非典型村落的保护、规划和发展建议。研究结果表明(1) ATVLUI 对复杂场景下的村落景观具有出色的识别能力,对传统建筑的分类准确率为 84%,总体准确率为 93%,Kappa 系数为 0.89。该模型优于 K-近邻(KNN)、决策树(DT)和随机树(RT);(2)根据面积和比例计算,建筑占前锋村总面积的 33.94%,其中现代建筑和传统建筑分别占 29.69%和 4.25%。传统建筑的数量为 202 座,占总建筑数量的 13%;(3)在村落内部,可以看到现代建筑之间的连接和延伸,这表明传统建筑正在逐渐被现代建筑所取代。村庄外围的生态环境良好。建筑与建筑之间的共同边界较长。现代建筑分布密集。分散分布的传统建筑以小建筑群的形式聚集。总体而言,不同的建筑高度交错,形成零散的分布格局。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
UAV Imagery-Based Classification Model for Atypical Traditional Village Landscapes and Their Spatial Distribution Pattern
For atypical traditional villages, their invaluable historical traces and cultural memories are preserved in the existing village landscapes. Rapid and accurate acquisition of the spatial information of various surface elements in a village is an important prerequisite for a scientific, reasonable, feasible planning and design scheme for conserving, progressing, and developing atypical villages. Taking Qianfeng Village as an example, this research proposes the atypical traditional village landscape classification model based on unmanned aerial vehicle (UAV) imagery (ATVLUI) by virtue of the UAV RGB images and the object-oriented fuzzy logic membership classification technique that extracts objects according to their spectrums, textures, geometries, and context relationships, aiming at precise extraction of atypical traditional village landscapes. Based on the landscape information, the landscape pattern indexes are calculated to explore the spatial distribution characteristics of different landscapes and analyze the current conditions of Qianfeng Village as the epitome of atypical traditional villages. Accordingly, suggestions for protecting, planning, and developing atypical villages are proposed. The results show that: (1) the ATVLUI boasts excellent identification for village landscapes in a complex scenario, with a classification accuracy for traditional structures of 84%, an overall accuracy of 93%, and a Kappa coefficient of 0.89. This model is proven superior to K-nearest neighbors (KNN), decision tree (DT), and random tree (RT); (2) according to the area and proportion calculations, the structures account for 33.94% of Qianfeng Village’s total area, in which 29.69% and 4.25% are modern and traditional structures, respectively. The number of traditional structures is 202, accounting for 13% of the total number of structures; (3) within the village, connectivity between and extension of the modern structures can be recognized, suggesting a trajectory where the traditional structures are being gradually substituted by modern ones. The ecological environment at the periphery of the village is favorable. The building-to-building common boundaries are long. The modern structures are densely distributed. The discretely distributed traditional structures gather as small clusters. In general, different structures are highly interlaced to form a fragmented distribution pattern.
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