Classification of Very High Resolution Aerial Photos Using Spectral-Spatial Convolutional Neural Networks
المؤلفون المشاركون
Pradhan, Biswajeet
Sameen, Maher Ibrahim
Aziz, Omar Saud
المصدر
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-06-26
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص EN
Classification of aerial photographs relying purely on spectral content is a challenging topic in remote sensing.
A convolutional neural network (CNN) was developed to classify aerial photographs into seven land cover classes such as building, grassland, dense vegetation, waterbody, barren land, road, and shadow.
The classifier utilized spectral and spatial contents of the data to maximize the accuracy of the classification process.
CNN was trained from scratch with manually created ground truth samples.
The architecture of the network comprised of a single convolution layer of 32 filters and a kernel size of 3 × 3, pooling size of 2 × 2, batch normalization, dropout, and a dense layer with Softmax activation.
The design of the architecture and its hyperparameters were selected via sensitivity analysis and validation accuracy.
The results showed that the proposed model could be effective for classifying the aerial photographs.
The overall accuracy and Kappa coefficient of the best model were 0.973 and 0.967, respectively.
In addition, the sensitivity analysis suggested that the use of dropout and batch normalization technique in CNN is essential to improve the generalization performance of the model.
The CNN model without the techniques above achieved the worse performance, with an overall accuracy and Kappa of 0.932 and 0.922, respectively.
This research shows that CNN-based models are robust for land cover classification using aerial photographs.
However, the architecture and hyperparameters of these models should be carefully selected and optimized.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Sameen, Maher Ibrahim& Pradhan, Biswajeet& Aziz, Omar Saud. 2018. Classification of Very High Resolution Aerial Photos Using Spectral-Spatial Convolutional Neural Networks. Journal of Sensors،Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1201941
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Sameen, Maher Ibrahim…[et al.]. Classification of Very High Resolution Aerial Photos Using Spectral-Spatial Convolutional Neural Networks. Journal of Sensors No. 2018 (2018), pp.1-12.
https://search.emarefa.net/detail/BIM-1201941
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Sameen, Maher Ibrahim& Pradhan, Biswajeet& Aziz, Omar Saud. Classification of Very High Resolution Aerial Photos Using Spectral-Spatial Convolutional Neural Networks. Journal of Sensors. 2018. Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1201941
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1201941
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر