دورية أكاديمية
Fully Connected Conditional Random Fields for High-Resolution Remote Sensing Land Use/Land Cover Classification with Convolutional Neural Networks
| العنوان: | Fully Connected Conditional Random Fields for High-Resolution Remote Sensing Land Use/Land Cover Classification with Convolutional Neural Networks |
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| المؤلفون: | Bin Zhang, Cunpeng Wang, Yonglin Shen, Yueyan Liu |
| المصدر: | Remote Sensing, Vol 10, Iss 12, p 1889 (2018) |
| بيانات النشر: | MDPI AG, 2018. |
| سنة النشر: | 2018 |
| المجموعة: | LCC:Science |
| مصطلحات موضوعية: | remote sensing, image classification, fully connected conditional random fields (FC-CRF), convolutional neural networks (CNN), Science |
| الوصف: | The interpretation of land use and land cover (LULC) is an important issue in the fields of high-resolution remote sensing (RS) image processing and land resource management. Fully training a new or existing convolutional neural network (CNN) architecture for LULC classification requires a large amount of remote sensing images. Thus, fine-tuning a pre-trained CNN for LULC detection is required. To improve the classification accuracy for high resolution remote sensing images, it is necessary to use another feature descriptor and to adopt a classifier for post-processing. A fully connected conditional random fields (FC-CRF), to use the fine-tuned CNN layers, spectral features, and fully connected pairwise potentials, is proposed for image classification of high-resolution remote sensing images. First, an existing CNN model is adopted, and the parameters of CNN are fine-tuned by training datasets. Then, the probabilities of image pixels belong to each class type are calculated. Second, we consider the spectral features and digital surface model (DSM) and combined with a support vector machine (SVM) classifier, the probabilities belong to each LULC class type are determined. Combined with the probabilities achieved by the fine-tuned CNN, new feature descriptors are built. Finally, FC-CRF are introduced to produce the classification results, whereas the unary potentials are achieved by the new feature descriptors and SVM classifier, and the pairwise potentials are achieved by the three-band RS imagery and DSM. Experimental results show that the proposed classification scheme achieves good performance when the total accuracy is about 85%. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2072-4292 10121889 |
| Relation: | https://www.mdpi.com/2072-4292/10/12/1889; https://doaj.org/toc/2072-4292 |
| DOI: | 10.3390/rs10121889 |
| URL الوصول: | https://doaj.org/article/53f60680ede4473fb2305e2dee18ad95 |
| رقم الأكسشن: | edsdoj.53f60680ede4473fb2305e2dee18ad95 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 20724292 10121889 |
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| DOI: | 10.3390/rs10121889 |