scholarly journals One-Dimensional Convolutional Neural Network Land-Cover Classification of Multi-Seasonal Hyperspectral Imagery in the San Francisco Bay Area, California

2017 ◽  
Vol 9 (6) ◽  
pp. 629 ◽  
Author(s):  
Daniel Guidici ◽  
Matthew Clark
Keyword(s):  
Neural Network ◽  
Land Cover ◽  
Convolutional Neural Network ◽  
San Francisco ◽  
San Francisco Bay Area ◽  
Hyperspectral Imagery ◽  
Land Cover Classification ◽  
One Dimensional ◽  
Bay Area

Automatic land cover classification of multi-resolution dualpol data using convolutional neural network (CNN)

2021 ◽  
Vol 22 ◽  
pp. 100491
Author(s):  
Nimrabanu Memon ◽  
Hemani Parikh ◽  
Samir B. Patel ◽  
Dhruvesh Patel ◽  
Vibha D. Patel
Keyword(s):  
Neural Network ◽  
Land Cover ◽  
Convolutional Neural Network ◽  
Land Cover Classification

scholarly journals A hybrid deep convolutional neural network for accurate land cover classification

2021 ◽  
Vol 103 ◽  
pp. 102515
Author(s):  
Naftaly Wambugu ◽  
Yiping Chen ◽  
Zhenlong Xiao ◽  
Mingqiang Wei ◽  
Saifullahi Aminu Bello ◽  
...  
Keyword(s):  
Neural Network ◽  
Land Cover ◽  
Convolutional Neural Network ◽  
Land Cover Classification ◽  
Deep Convolutional Neural Network

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.

scholarly journals A Spectral Feature Based Convolutional Neural Network for Classification of Sea Surface Oil Spill

2019 ◽  
Vol 8 (4) ◽  
pp. 160 ◽  
Author(s):  
Bingxin Liu ◽  
Ying Li ◽  
Guannan Li ◽  
Anling Liu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Oil Film ◽  
One Dimensional

Spectral characteristics play an important role in the classification of oil film, but the presence of too many bands can lead to information redundancy and reduced classification accuracy. In this study, a classification model that combines spectral indices-based band selection (SIs) and one-dimensional convolutional neural networks was proposed to realize automatic oil films classification using hyperspectral remote sensing images. Additionally, for comparison, the minimum Redundancy Maximum Relevance (mRMR) was tested for reducing the number of bands. The support vector machine (SVM), random forest (RF), and Hu’s convolutional neural networks (CNN) were trained and tested. The results show that the accuracy of classifications through the one dimensional convolutional neural network (1D CNN) models surpassed the accuracy of other machine learning algorithms such as SVM and RF. The model of SIs+1D CNN could produce a relatively higher accuracy oil film distribution map within less time than other models.

scholarly journals Integrating Multitemporal Sentinel-1/2 Data for Coastal Land Cover Classification Using a Multibranch Convolutional Neural Network: A Case of the Yellow River Delta

2019 ◽  
Vol 11 (9) ◽  
pp. 1006 ◽  
Author(s):  
Quanlong Feng ◽  
Jianyu Yang ◽  
Dehai Zhu ◽  
Jiantao Liu ◽  
Hao Guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Deep Learning ◽  
Land Cover ◽  
Convolutional Neural Network ◽  
Classification Accuracy ◽  
Yellow River ◽  
Rapid Development ◽  
Land Cover Classification ◽  
Coastal Land

Coastal land cover classification is a significant yet challenging task in remote sensing because of the complex and fragmented nature of coastal landscapes. However, availability of multitemporal and multisensor remote sensing data provides opportunities to improve classification accuracy. Meanwhile, rapid development of deep learning has achieved astonishing results in computer vision tasks and has also been a popular topic in the field of remote sensing. Nevertheless, designing an effective and concise deep learning model for coastal land cover classification remains problematic. To tackle this issue, we propose a multibranch convolutional neural network (MBCNN) for the fusion of multitemporal and multisensor Sentinel data to improve coastal land cover classification accuracy. The proposed model leverages a series of deformable convolutional neural networks to extract representative features from a single-source dataset. Extracted features are aggregated through an adaptive feature fusion module to predict final land cover categories. Experimental results indicate that the proposed MBCNN shows good performance, with an overall accuracy of 93.78% and a Kappa coefficient of 0.9297. Inclusion of multitemporal data improves accuracy by an average of 6.85%, while multisensor data contributes to 3.24% of accuracy increase. Additionally, the featured fusion module in this study also increases accuracy by about 2% when compared with the feature-stacking method. Results demonstrate that the proposed method can effectively mine and fuse multitemporal and multisource Sentinel data, which improves coastal land cover classification accuracy.

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