| DC 欄位 |
值 |
語言 |
| DC.contributor | 水文與海洋科學研究所 | zh_TW |
| DC.creator | 陳冠羽 | zh_TW |
| DC.creator | Kuan-Yu Chen | en_US |
| dc.date.accessioned | 2017-11-24T07:39:07Z | |
| dc.date.available | 2017-11-24T07:39:07Z | |
| dc.date.issued | 2017 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:444/thesis/view_etd.asp?URN=104626004 | |
| dc.contributor.department | 水文與海洋科學研究所 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 本研究分析無人空拍機所產出之正射影像，並應用影像處理技術來辨識桃園藻礁的礁體範圍。在辨識礁體的部分，本研究主要利用k-means分類法進行礁體分類辨識，並探討不同影像技術對辨識率的改善效果及演算效率；利用色彩強度、紋理分析及直方圖等化的方式來濾除非礁體成分，並調整影像亮度分佈，達到改善辨識率的目的。本研究隨機抽取兩影像區塊並分別三種方法：(一) 色彩強度分類、(二)以色彩強度和紋理分類、(三)以色彩強度、紋理和調整影像亮度，來測試三個方法的藻礁辨識以及演算時間。結果顯示單只以色彩強度對影像進行分類時，礁體與濕沙不容易被區分。若以色彩和紋理特徵進行分類時其兩影像的結果平均分類準確率可達83%，但此方法在影像亮度不均勻時準確率較差。方法三在改善影像亮度不均勻的問題後，其平均分類準確率為89%，為三個方法中最佳。本研究也對不同解析度影像進行分類測試時，發現解析度為9.5公分時具有較佳的分類準確率和較短的演算時間。最後本研究也利用2016年與2017年的影像，來比較在同樣區域內礁體範圍的差異。結果顯示，與先前單以人工標註的方式相比本研究所提出的方法，在劃分藻礁範圍時可以有效的減少人工成本並提高分類的準確率。 | zh_TW |
| dc.description.abstract | The purpose of the present study is to analyze UAV-images and apply image processing techniques to identify the regions of algal reefs. In this study, we applied the K-means cluster method for reef classification. We also investigated the accuracy and computational efficiency of different image techniques in reef identification. Color intensity, texture analysis, and histogram equalization were used to filter out the non-reef components. We also adjusted the image’s brightness to improve the recognition rate. Two images were randomly selected for testing the computational efficiency. Three combinations of image analysis methods based on the following three main groups were tested: a) the color intensity only, b) the color intensity and texture of image, and c) the color intensity, texture of image and brightness. Our results show that the reef and wet sand cannot be easily distinguished when only the color intensity is used for classification. The accuracy of the reef identification using group b has significantly improved to 83%, with slightly low accuracy when the brightness distribution is uneven. After the brightness is adjusted, the accuracy of applying group c increases to 89%, which is the best method among the three groups. We also found that images with resolution of 9.5 cm can significantly reduce the computational cost with better accuracy. Finally, we applied the improved image processing technique to compare the change of the algal-reef region along Taoyuan coast between year 2016 and 2017. Our result proved that the improved image processing technique can significantly reduce the labor cost and increased the classification accuracy, as compare to the previous manually annotation of the reef region. | en_US |
| DC.subject | 影像辨識 | zh_TW |
| DC.subject | 藻礁 | zh_TW |
| DC.subject | 影像分類 | zh_TW |
| DC.title | 利用影像處理技術辨識藻礁範圍 | zh_TW |
| dc.language.iso | zh-TW | zh-TW |
| DC.title | Identification of algae reefs using image processing techniques | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |