ENGLISH

29卷/1期

29卷/1期

華藝線上圖書館

Pages:

1-16

論文名稱

以攝影測量方式建立無人機影像曜光模式之研究

Title

Establishing Sun-glint Estimation Model for Unnamed Aerial System Image through Photogrammetry

作者

李祈叡、王聖鐸

Author

Chi-Jui Li, Sendo Wang

中文摘要

無人機影像和航遙影像同樣會受到太陽曜光影響。雖已有不同方式可最小化太陽曜光對影像的影響,但目前尚不確定過去使用於低空間解析度影像的處理方法,是否能夠有效應用於高空間解析度的影像。 本研究欲於無人機於航線規劃階段,瞭解曜光可能的出現情形。在對研究區域建立地表、太陽及攝影站之空間關係後,研究將進行曜光預估的計算。根據研究之模擬,使用攝影測量方式建立之曜光預估模式可使使用者於航線規劃階段得知曜光於整體影像蒐集過程之分佈,並可依時間、外方位元素之調整要點,為目標飛行時段帶來較佳有效影像蒐集效率之航拍規劃。

Abstract

Nowadays, Unmanned Aerial System (UAS) imagery products also suffer from blurring and degradation caused by sun glint effects. Various techniques, including detection methods and specialized algorithms, are used to minimize sun glint's impact in aerial or remote sensing imagery. However, it remains uncertain whether the processing techniques used for low spatial resolution images can effectively be applied to images with high spatial resolution. By establishing the spatial relationships between the ground, sun, and sensor, a threshold for determining the presence of sun glint was established based on previously captured images, specifically for this research model. The findings of the results are presented from statistical, image-based, and physical spatial perspectives to identify the time period with the least sun glint during the target flight. This finding helps in reducing the effort required for sun glint removal. The key outcome of this approach is that employing photogrammetric techniques to establish a sun glint prediction model allows users to understand the distribution of sun glint throughout the entire image acquisition process during the planning phase. By adjusting the timing, it becomes feasible to plan flight schedules during periods of the day that offer higher efficiency in capturing useful images.

關鍵字

曜光、無人機、攝影測量、飛行規劃

Keywords

Sun Glint, Unnamed Aerial System, Photogrammetry, Flight Planning

附件檔名

華芸線上圖書館

https://www.airitilibrary.com/Publication/Index?DocID=10218661-N202403300014-00001

備註說明

N / A

Pages:

17-34

論文名稱

結合深度學習與街景影像建構街道廣告招牌之空間聚集指標

Title

Applying Deep Learning and Street View Imagery to Create a Spatial Agglomeration Index for Urban Street Signboards

作者

羅章秀、林柏丞

Author

Zhang-Xiu Luo, Bo-Cheng Lin

中文摘要

近年來許多研究透過深度學習建構都市量化指標,作為後續相關議題結合應用。基於臺灣廣告招牌密度高、樣式多元,本研究旨在應用常見深度學習 (Deep Learning) 之語義分割(Semantic Segmentation)以及物件偵測(Object Detection)方式,量化街景影像中廣告招牌街道空間聚集狀態,並探討研究區域空間分布型態。成果顯示,Deeplab v3+模型訓練平均交併比 (Mean Intersection over Union, MIoU) 值可達83%;YOLOv7模型精確率 (Precision) 與召回率 (Recall) 分別可達91.7%與87.1%,顯示有一定辨識成效,亦可與實際分布情形相符合。本研究可為後續廣告招牌進一步應用與探勘,以及相關領域結合應用之契機。

Abstract

In recent years, deep learning has been used to construct quantitative indicators relevant to urban areas. Given the diverse array of dense billboards in Taiwan, this study aims to utilize deep learning techniques, including semantic segmentation and object detection, in conjunction with street view imagery to quantify the spatial distribution of signboards. Moreover, this study examines the spatial distribution patterns within the research area. The results demonstrate that the MIoU value of Deeplab v3+ model achieves 83%, while the Precision and Recall of YOLOv7 model achieves 91.7% and 87.1%. The analysis of spatial distribution patterns results align well with the actual distribution of billboards. This study can serve as a foundation for further exploration and application of billboards, as well as for integration with other related fields.

關鍵字

深度學習、語義分割、物件偵測、街景影像、空間分析

Keywords

Deep Learning, Semantic Segmentation, Object Detection, Street View Imagery, Spatial Analysis

附件檔名

華芸線上圖書館

https://www.airitilibrary.com/Publication/Index?DocID=10218661-N202403300014-00002

備註說明

N / A

Pages:

35-49

論文名稱

藉由相隔15年的兩組數據探索蘇鐵蕨物種分布模型之時間可轉移能力

Title

Exploring the Temporal Transferability in the Species Distribution Model of Brainea insignis Using Two Sets of Data Separated by 15 Years

作者

郭慶津、邵寶嬅、羅南璋、黃凱易

Author

Ching-Jin Kuo, Bao-Hua Shao, Nan-Chang Lo, Kai-Yi Huang

中文摘要

本研究旨在探討不同演算法對物種分布模型 (species distribution model, SDM) 時間可轉移能力 (transferability) 的影響,以及使用深度學習法建立SDM的可能性。研究以蘇鐵蕨為目標物種,獲取該物種兩組調查時間相隔15年之數據,以最大熵值法 (maximum entropy, MAXENT)、隨機森林 (random forest, RF)、支持向量機 (support vector machine, SVM) 和深度學習法U-net進行試驗。結果顯示,MAXENT和SVM有最佳的時間轉移能力,而U-net也有機會獲得甚佳的成果。顯示深度學習具研究潛力,後續研究有必要採納更多種類的深度學習法,並持續試驗。惟在環境變數方面,單獨使用地形因子可能限制了模型的時間轉移性,需尋找與物種更具直接因果關係的生態因子提高可轉移性。

Abstract

This study aims to investigate the impact of different algorithms on the temporal transferability of species distribution model (SDM) and the feasibility of using deep learning techniques to build SDM. The study focuses on Brainea insignis as the target species and utilizes two sets of samples collected with a 15-year interval. Experiments were conducted using the maximum entropy (MAXENT), random forest (RF), support vector machine (SVM), and U-net—a deep learning approach. The results indicate that MAXENT and SVM exhibit the best temporal transferability, while U-net also shows promising results. This highlights the research potential of deep learning, and future studies should consider incorporating a wider range of deep learning methods and continue experimentation. However, concerning environmental variables, relying solely on topographic factors may constrain the model's transferability, necessitating the identification of ecological factors with more direct causal relationships with the species to enhance transferability.

關鍵字

時間可轉移性、深度學習、物種分布模型、蘇鐵蕨

Keywords

Temporal Transferability, Deep Learning, Species Distribution Model, Brainea insignis

附件檔名

華芸線上圖書館

https://www.airitilibrary.com/Publication/Index?DocID=10218661-N202403300014-00003

備註說明

N / A

Pages:

51-64

論文名稱

應用四物候日無人機影像空間外推入侵外來種—小花蔓澤蘭之空間型態

Title

Extrapolating the Spatial Patterns of Invasive Alien Species—Mikania micrantha Based on Four Phenological-Date Drone Images

作者

洪浩源、郭慶津、邵寶嬅、羅南璋、黃凱易

Author

Hao-Yuan Hung, Chin-Jin Kuo, Bao-Hua Shao, Nan-Chang Lo, Kai-Yi Huang

中文摘要

本研究以無人機獲取外來入侵種小花蔓澤蘭 (Mikania micrantha; bitter vine, BV) 兩試區A與B的四物候日多光譜影像,續以最大概似法、隨機森林與U-net空間外推BV的空間型態。建、驗模採兩組取樣策略:(1) 兩試區以各自訓練樣本推測本身所在地、(2) 雙向外推至另一無訓練樣本之試區。結果顯示,第一組三模型之kappa值皆高於0.75。第二組由試區A正向外推至B較試區B反向外推至A成效差,乃兩區植被型態及BV花況不同而致,尤以U-net更能掌握BV的空間型態,外推之kappa值最高達0.73。為改善此情況並更完整探討模型之效能及穩定性,後續將結合兩區樣本執行空間外推,期能改善目前外推之結果。

Abstract

This study utilized unmanned aerial vehicle to obtain multispectral imagery of Mikania micrantha (bitter vine, BV) in two study areas, Plot A and Plot B. Employing maximum likelihood classification, random forest, and U-net, the study aimed to assess the model's performance in spatial extrapolation of spatial patterns of BV, with the goal of discovering new populations of previously unidentified species. Two sampling designs were employed for model training and testing, Set 1, conducting individual plot classification using training data from it’s owned; Set 2, performing spatial extrapolation from one plot with training data to another without training data, whose validation data there represent previously unidentified new populations. The results indicate that the three models performed well in Set 1. The kappa values for all models exceeded 0.75. However, the performance in plot B was slightly lower compared to plot A, possibly due to the more complex vegetation patterns in plot B. In Set 2, extrapolation from plot A to B showed fewer effective results compared to extrapolation from plot B to A. This discrepancy can be attributed to the differences in vegetation patterns and the flowering conditions of BV between the two areas. In terms of model performance, U-net demonstrated a better ability to capture the spatial patterns of BV, achieving the highest kappa value of up to 0.73 among the three models in Set 2. To comprehensively examine the model's performance in spatial extrapolation, future work will involve combining training data from both plots for extrapolation, and testing with other deep learning models such as convolution neural network (CNN) and environmental variables.

關鍵字

空間外推、小花蔓澤蘭、物候、無人機

Keywords

Spatial Extrapolation, Mikania micrantha, Phenology, Unmanned Aerial Vehicle

附件檔名

華芸線上圖書館

https://www.airitilibrary.com/Publication/Index?DocID=10218661-N202403300014-00004

備註說明

N / A

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