التفاصيل البيبلوغرافية
| العنوان: |
利用深度神經網路於高光譜影像物件式分類 ; Utilizing Deep Neural Networks for object-based image classification on hyperspectral images |
| المؤلفون: |
洪奕瑾, Yi-Chin Hung |
| المساهمون: |
土木工程學系所, 楊明德, Ming-Der Yang |
| سنة النشر: |
2018 |
| المجموعة: |
National Chung Hsing University Institutional Repository - NCHUIR / 國立中興大學 |
| مصطلحات موضوعية: |
高光譜影像, 最小噪聲轉換, 簡單的線性迭代聚類, 深度神經網路, hyperspectral image, Minimum Noise Fraction, Simple Linear Iterative Clustering, Deep Neural Network |
| الوصف: |
高光譜影像具有圖譜合一的特性，每個像元蘊含了數百個波段資訊，對地物的分類精度提升有很大的幫助，但數百個波段資料量導致處理速率降低。以傳統的逐像元式分類 (pixel-based classification) 會具有龐大資料量不易呈現地物間之空間關聯性的問題。 為有效的解決上述高光譜影像所帶來之問題， 本研究提出基於物件化及深度類神經網路 (Deep Neural Network, DNN) 演算法對高光譜影像進行分類。研究資料取自AVIRIS(Airborne Visible/Infrared Imaging Spectrometer)的Indian Pines、Salinas影像及其地面真實資料。首先將高光譜影像進行最小噪聲轉換 (Minimum Noise Fraction, MNF) 分離數據中的噪聲並提出有用的資訊，減少隨後處理的計算量，再以物件式分類(Object-Based Image Analysis, OBIA)，利用光譜值考慮空間相關性，也因為物件含有多個像元其分類速度快，此方式適合高解析度與內容較複雜影像，採用簡單的線性迭代聚類 (Simple Linear Iterative Clustering, SLIC)方法，進行分割不同大小及緊湊度物件，再計算每個物件空間與光譜訊息，選取訓練樣本並給予類別標籤，最後以深度神經網路進行物件化影像分類。 DNN於高光譜影像物件化分類，確實能解決椒鹽效應，物件式運算時間皆較像元式分類來得快速，且當實驗影像區域越大時，分類準確度與kappa值亦會越高，且高於像元式分類，實驗驗證此套流程於Salinas影像分類準確度可達94.62%，也提升了分類速度。 ; Hyperspectral images have the feature of combining picture and spectrum. Each pixel contains hundreds of bands of information, which is very helpful for improving the classification accuracy of ground objects, but leads to a decrease in processing rate. In the traditional pixel-based classification, the number of data is rarely present the spatial correlation between objects. To effectively solve the problems caused by the hyperspectral imagery, this study proposes to classify hyperspectral images based on object-based and Deep Neural Network (DNN) algorithms. The research data include Indian Pines, Salinas images, which were taken from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), and their ground truth data. First, the hyperspectral image is subjected to Minimum Noise Fraction (MNF) to separate the noise in the data and provide useful information to reduce the number of calculations, then the spectral value of Object-Based Image Analysis (OBIA) us used to explore the spatial correlation. This method is suitable for high-resolution and complex images, because of the multiple pixels of the object and its fast classification. Simple Linear Iterative Clustering (SLIC) is used to divide the objects into various sizes and compactness based on the space and spectral information of each object, and ... |
| نوع الوثيقة: |
thesis |
| اللغة: |
Chinese |
| Relation: |
http://hdl.handle.net/11455/97484 |
| الاتاحة: |
http://hdl.handle.net/11455/97484 |
| Rights: |
restricted |
| رقم الانضمام: |
edsbas.901A5BA0 |
| قاعدة البيانات: |
BASE |