Automatic Detection and Classification of Oil Tanks in Optical Satellite Images Based on Convolutional Neural Network

2016 ◽  
pp. 304-313 ◽  
Author(s):  
Qingquan Wang ◽  
Jinfang Zhang ◽  
Xiaohui Hu ◽  
Yang Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Satellite Images ◽  
Automatic Detection ◽  
Optical Satellite Images ◽  
Oil Tanks

scholarly journals Deep Convolutional Neural Network for Mapping Smallholder Agriculture Using High Spatial Resolution Satellite Image

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2398 ◽  
Author(s):  
Bin Xie ◽  
Hankui K. Zhang ◽  
Jie Xue
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Land Cover ◽  
Convolutional Neural Network ◽  
Satellite Images ◽  
Spatial Information ◽  
Deep Convolutional Neural Network ◽  
Crop Types ◽  
Pooling Strategy

In classification of satellite images acquired over smallholder agricultural landscape with complex spectral profiles of various crop types, exploring image spatial information is important. The deep convolutional neural network (CNN), originally designed for natural image recognition in the computer vision field, can automatically explore high level spatial information and thus is promising for such tasks. This study tried to evaluate different CNN structures for classification of four smallholder agricultural landscapes in Heilongjiang, China using pan-sharpened 2 m GaoFen-1 (meaning high resolution in Chinese) satellite images. CNN with three pooling strategies: without pooling, with max pooling and with average pooling, were evaluated and compared with random forest. Two different numbers (~70,000 and ~290,000) of CNN learnable parameters were examined for each pooling strategy. The training and testing samples were systematically sampled from reference land cover maps to ensure sample distribution proportional to the reference land cover occurrence and included 60,000–400,000 pixels to ensure effective training. Testing sample classification results in the four study areas showed that the best pooling strategy was the average pooling CNN and that the CNN significantly outperformed random forest (2.4–3.3% higher overall accuracy and 0.05–0.24 higher kappa coefficient). Visual examination of CNN classification maps showed that CNN can discriminate better the spectrally similar crop types by effectively exploring spatial information. CNN was still significantly outperformed random forest using training samples that were evenly distributed among classes. Furthermore, future research to improve CNN performance was discussed.

Classification of EgyptSat-1 Images Using Deep Learning Methods

2020 ◽  
Vol 10 (1) ◽  
pp. 37-46 ◽  
Author(s):  
Hatem Keshk ◽  
Xu-Cheng Yin
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
High Performance ◽  
Satellite Images ◽  
Satellite Image ◽  
Aerial Photographs ◽  
Average Accuracy

Background: Deep Learning (DL) neural network methods have become a hotspot subject of research in the remote sensing field. Classification of aerial satellite images depends on spectral content, which is a challenging topic in remote sensing. Objective: With the aim to accomplish a high performance and accuracy of Egyptsat-1 satellite image classification, the use of the Convolutional Neural Network (CNN) is raised in this paper because CNN is considered a leading deep learning method. CNN is developed to classify aerial photographs into land cover classes such as urban, vegetation, desert, water bodies, soil, roads, etc. In our work, a comparison between MAXIMUM Likelihood (ML) which represents the traditional supervised classification methods and CNN method is conducted. Conclusion: This research finds that CNN outperforms ML by 9%. The convolutional neural network has better classification result, which reached 92.25% as its average accuracy. Also, the experiments showed that the convolutional neural network is the most satisfactory and effective classification method applied to classify Egyptsat-1 satellite images.

scholarly journals ENHANCED PIXEL BASED URBAN AREA CLASSIFICATION OF SATELLITE IMAGES USING CONVOLUTIONAL NEURAL NETWORK

2021 ◽  
Vol 0 (0) ◽  
pp. 1-16
Author(s):  
Noureldin Laban ◽  
Bassam Abdellatif ◽  
Hala Moushier ◽  
howida shedeed ◽  
Mohamed Tolba
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Urban Area ◽  
Satellite Images ◽  
Area Classification

scholarly journals Automatic Detection and Classification of Rib Fractures on Thoracic CT Using Convolutional Neural Network: Accuracy and Feasibility

2020 ◽  
Vol 21 (7) ◽  
pp. 869 ◽  
Author(s):  
Qing-Qing Zhou ◽  
Jiashuo Wang ◽  
Wen Tang ◽  
Zhang-Chun Hu ◽  
Zi-Yi Xia ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Automatic Detection ◽  
Rib Fractures ◽  
Thoracic Ct

scholarly journals Study on automatic detection and classification of breast nodule using deep convolutional neural network system

2020 ◽  
Vol 12 (9) ◽  
pp. 4690-4701
Author(s):  
Feiqian Wang ◽  
Xiaotong Liu ◽  
Na Yuan ◽  
Buyue Qian ◽  
Litao Ruan ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Automatic Detection ◽  
Deep Convolutional Neural Network ◽  
Network System ◽  
Breast Nodule ◽  
Neural Network System
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