Convolutional neural network architecture for digital surface model estimation from single remote sensing image

2019 ◽  
Vol 13 (01) ◽  
pp. 1 ◽  
Author(s):  
Hamed Amini Amirkolaee ◽  
Hossein Arefi
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Remote Sensing Image ◽  
Surface Model ◽  
Model Estimation ◽  
Digital Surface Model ◽  
Neural Network Architecture

scholarly journals LAND COVER CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK WITH REMOTE SENSING DATA AND DIGITAL SURFACE MODEL

2020 ◽  
Vol V-3-2020 ◽  
pp. 39-43
Author(s):  
B. Liu ◽  
S. Du ◽  
X. Zhang
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Land Cover ◽  
Convolutional Neural Network ◽  
Conditional Random Field ◽  
Remote Sensing Data ◽  
Land Cover Classification ◽  
Surface Model ◽  
Digital Surface Model ◽  
Object Based

Abstract. Land cover map is widely used in urban planning, environmental monitoring and monitoring of the changing world. This paper proposes a framework with convolutional neural network (CNN), object-based voting and conditional random field (CRF) for land cover classification. Both very-high-resolution (VHR) remote sensing images and digital surface model (DSM) are inputs of this CNN model. To solve the “salt and pepper” effect caused by pixel-based classification, an object-based voting classification is performed. And to capture accurate boundary of ground objects, a CRF optimization using spectral information, DSM and deep features extracted through CNN is applied. Area one of Vaihingen datasets is used for experiment. The experimental results show that method proposed in this paper achieve an overall accuracy of 95.57%, which demonstrate the effectiveness of proposed method.

scholarly journals BengaliNet: A Low-Cost Novel Convolutional Neural Network for Bengali Handwritten Characters Recognition

2021 ◽  
Vol 11 (15) ◽  
pp. 6845
Author(s):  
Abu Sayeed ◽  
Jungpil Shin ◽  
Md. Al Mehedi Hasan ◽  
Azmain Yakin Srizon ◽  
Md. Mehedi Hasan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Character Recognition ◽  
High Performance ◽  
Low Cost ◽  
Traditional Learning ◽  
Neural Network Architecture ◽  
Handwritten Character Recognition ◽  
Handwritten Character

As it is the seventh most-spoken language and fifth most-spoken native language in the world, the domain of Bengali handwritten character recognition has fascinated researchers for decades. Although other popular languages i.e., English, Chinese, Hindi, Spanish, etc. have received many contributions in the area of handwritten character recognition, Bengali has not received many noteworthy contributions in this domain because of the complex curvatures and similar writing fashions of Bengali characters. Previously, studies were conducted by using different approaches based on traditional learning, and deep learning. In this research, we proposed a low-cost novel convolutional neural network architecture for the recognition of Bengali characters with only 2.24 to 2.43 million parameters based on the number of output classes. We considered 8 different formations of CMATERdb datasets based on previous studies for the training phase. With experimental analysis, we showed that our proposed system outperformed previous works by a noteworthy margin for all 8 datasets. Moreover, we tested our trained models on other available Bengali characters datasets such as Ekush, BanglaLekha, and NumtaDB datasets. Our proposed architecture achieved 96–99% overall accuracies for these datasets as well. We believe our contributions will be beneficial for developing an automated high-performance recognition tool for Bengali handwritten characters.

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