主管单位:中国科学技术协会
主办单位:中国地理学会
承办单位:华东师范大学

世界地理研究 ›› 2026, Vol. 35 ›› Issue (4): 182-198.DOI: 10.3969/j.issn.1004-9479.2026.04.20240691

• 文化与社会 • 上一篇    

基于低光照图像增强的上海夜间旅游感知意象维度构建及特征分析

李凌雁1(), 安浩宇1(), 石保顺2, 朱纯子2, 杨婉婷1,3   

  1. 1.燕山大学,经济管理学院,秦皇岛 066004
    2.燕山大学,信息科学与工程学院,秦皇岛 066004
    3.河北环境工程学院,秦皇岛 066102
  • 收稿日期:2024-08-22 修回日期:2025-02-21 出版日期:2026-04-15 发布日期:2026-04-29
  • 通讯作者: 安浩宇
  • 作者简介:李凌雁(1989—),女,副教授,博士生导师,研究方向为区域旅游发展与管理、旅游大数据,E-mail:llymail@126.com
  • 基金资助:
    国家社会科学基金项目(20CJY048)

The dimension construction and characteristic analysis of Shanghai's nighttime tourism perceived image based on low-light image enhancement method

Lingyan LI1(), Haoyu AN1(), Baoshun SHI2, Chunzi ZHU2, Wanting YANG1,3   

  1. 1.School of Economics and Management, Qinhuangdao 066004, China
    2. School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
    3.Hebei University of Environmental Engineering, Qinhuangdao 066102, China
  • Received:2024-08-22 Revised:2025-02-21 Online:2026-04-15 Published:2026-04-29
  • Contact: Haoyu AN

摘要:

夜间旅游图像蕴含丰富的夜间活动和认知信息,是精准反映游客感知意象偏好的重要载体。但当前学界较少利用游客生成图像探析夜间旅游感知意象,且低光照条件显著增加了分析难度,传统工具难以深挖其内涵。为此,本研究以上海为研究案例,建立了基于低光照增强方法KinD的夜间旅游图像分析模型来还原图像内容,利用DeepSentibank挖掘图像意象并系统构建夜间旅游感知意象维度,采用内容分析、社会网络分析、空间分析法探究夜间旅游感知意象特征,旨在精准把握游客真实态度和情感偏好。研究发现:①KinD能有效提升夜间旅游图像识别精度,准确还原图像内容细节。②基于认知-情感模型将上海夜间旅游感知意象维度建构为8个主意象和22个子意象,认知意象中夜间场景营造占比最高,情感意象中积极情感占比超过一半。上海夜间旅游感知意象维度呈现软硬件要素兼具、新老要素交融、评价总体向好但局部存在不足、要素存在主客观认知偏差等特征。③认知意象网络“多核心、多边缘”特征突出,游客喜爱的意象组合为夜间场景营造和夜娱体验活动要素的交互融合、夜间营造场景间的相互映衬以及城市夜景风光和夜间场景营造的碰撞交织。情感意象网络以低强度积极情感为主导,夜间场景营造意象与积极情感关联性更强,消极情感多出现在夜景风光意象中。④感知意象空间结构呈“局部集中、多点分散”特征,整体空间分布不均衡,游客感知焦点高度集中于上海核心旅游区。本研究有助于完善夜间旅游图像研究思路,拓宽交叉学科方法应用范围,可为夜间旅游意象要素统筹发展及空间格局优化提供科学合理的决策参考。

关键词: 夜间旅游, 感知意象, 低光照图像增强, 意象特征, 上海市

Abstract:

Nighttime tourism photos serve as important carriers to reflect tourists' preferences. Taking Shanghai as an example, this study established a nighttime tourism image analysis model based on the low-light enhancement method KinD to restore photo content. Utilizing Deepsentibank to mine images, and through content analysis, social network analysis and spatial analysis, the study explored the characteristics of nighttime tourism perceived image, aiming to grasp tourists' authentic attitudes and emotional preferences. The results show that: ① KinD can effectively improve the recognition accuracy of nighttime tourism photos and accurately restore photo content details. ② Utilizing the cognitive-affective model, the dimensions of Shanghai's nighttime tourism perceived image are constructed into eight main-images and twenty-two sub-images. Cognitive images show the highest proportion of nighttime scene creation. In affective images, positive emotions account for more than half. The dimensions of Shanghai's nighttime tourism perceived image exhibit characteristics such as coexistence of hardware and software elements, integration of old and new elements, overall positive evaluation with localized deficiencies, and subjective-objective deviations among elements. ③ The cognitive image network exhibits a "multi-core, multi-edge" structure. Tourists preferred image combinations that the interaction between nighttime scene creation and nighttime entertainment experience elements, the mutual reflection between nighttime scenes creation, and the collision and interweaving of urban nightscape scenery and nighttime scene creation dimensions. The affective image network is dominated by low-intensity positive emotions. The image of nighttime scene creation is more strongly associated with positive emotions, while negative emotions primarily emerge in urban nightscape scenery. ④ The spatial structure of Shanghai's nighttime tourism perceived image exhibits a characteristic of "local concentration and multi-point dispersion". The overall spatial distribution is uneven, and tourists' perceptual focus is concentrated in Shanghai's core tourism areas. This study contributes to refining the research approach for nighttime tourism photos and broadening the application scope of interdisciplinary methods, aiming to provide decision-making references for the optimization development of nighttime tourism.

Key words: nighttime tourism, perceived image, low-light image enhancement, image characteristics, Shanghai city