博碩士論文 110522036 完整後設資料紀錄

DC 欄位 語言
DC.contributor資訊工程學系zh_TW
DC.creator洪梓為zh_TW
DC.creatorZi-Wei Hungen_US
dc.date.accessioned2023-7-27T07:39:07Z
dc.date.available2023-7-27T07:39:07Z
dc.date.issued2023
dc.identifier.urihttp://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=110522036
dc.contributor.department資訊工程學系zh_TW
DC.description國立中央大學zh_TW
DC.descriptionNational Central Universityen_US
dc.description.abstract研究土地利用有助於規劃和管理土地資源,傳統方式進行國土利用現況調查需要花費大量人力與時間成本來維護。本篇論文以區塊為單位,對台灣本島地區之 SPOT-7 衛星影像進行人工標註,收集台灣本島地區之建物道路、樹林、草地草原、農作物、水體、一般裸露地、農地裸露地七種類別的標註資料集。並應用地理物件影像分析 (GEOBIA) 的方法,將區塊化資料之影像直方圖(histogram image)作為資料輸入修改過之 ViT-B/16 模型,訓練一個基於自注意力機制 (self-attentioin) 的分類模型。本論文使用 2013 與 2021 兩年台灣本島地區的 SPOT-7 衛星影像,分別訓練兩個模型來預測各自的土地使用分類,並針對兩年的土地使用變遷進行研究與分析。zh_TW
dc.description.abstractStudying land use contributes to the planning and management of land resources. Traditional methods of conducting land use surveys require significant human and time resources for maintenance. In this paper, using the block as the unit, we manually labeled SPOT-7 satellite imagery of Taiwan, collecting labeled datasets for seven categories: buildings/roads, forests, grasslands, crops, water bodies, general bare land and agricultural bare land. By applying the method of Geographic Object-Based Image Analysis (GEOBIA), we used the histogram distribution of the block-level data as input to the modified ViT-B/16 model, which is based on self-attention mechanism, to train a classification model. Two models were trained using SPOT-7 satellite imagery of Taiwan for the years 2013 and 2021, respectively, to predict land use classifications for each year. The land use changes between the two years were studied and analyzed.en_US
DC.subject衛星影像zh_TW
DC.subject深度學習zh_TW
DC.subject自注意力機制zh_TW
DC.subject分類模型zh_TW
DC.subject土地利用zh_TW
DC.subjectSatellite imageryen_US
DC.subjectDeep learningen_US
DC.subjectSelf-attention mechanismen_US
DC.subjectClassification modelen_US
DC.subjectLand useen_US
DC.title基於SPOT-7衛星影像之台灣土地使用分析zh_TW
dc.language.isozh-TWzh-TW
DC.titleLand use analysis of Taiwan based on SPOT-7 satellite imageryen_US
DC.type博碩士論文zh_TW
DC.typethesisen_US
DC.publisherNational Central Universityen_US

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