Unsupervised Land Cover Classification of Hybrid and Dual-Polarized Images Using Deep Convolutional Neural Network

2020 ◽  
pp. 1-5
Author(s):  
Ankita Chatterjee ◽  
Jayasree Saha ◽  
Jayanta Mukherjee ◽  
Subhas Aikat ◽  
Arundhati Misra
Keyword(s):  
Neural Network ◽  
Land Cover ◽  
Convolutional Neural Network ◽  
Land Cover Classification ◽  
Deep Convolutional Neural Network ◽  
Dual Polarized

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 Deep Convolution Neural Network Method for Land Cover Mapping: A Case Study of Qinhuangdao, China

2018 ◽  
Vol 10 (12) ◽  
pp. 2053 ◽  
Author(s):  
Yunfeng Hu ◽  
Qianli Zhang ◽  
Yunzhi Zhang ◽  
Huimin Yan
Keyword(s):  
Neural Network ◽  
Land Cover ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Training Dataset ◽  
Support Vector ◽  
Landsat 8 ◽  
Generalization Ability ◽  
New Type

Land cover and its dynamic information is the basis for characterizing surface conditions, supporting land resource management and optimization, and assessing the impacts of climate change and human activities. In land cover information extraction, the traditional convolutional neural network (CNN) method has several problems, such as the inability to be applied to multispectral and hyperspectral satellite imagery, the weak generalization ability of the model and the difficulty of automating the construction of a training database. To solve these problems, this study proposes a new type of deep convolutional neural network based on Landsat-8 Operational Land Imager (OLI) imagery. The network integrates cascaded cross-channel parametric pooling and average pooling layer, applies a hierarchical sampling strategy to realize the automatic construction of the training dataset, determines the technical scheme of model-related parameters, and finally performs the automatic classification of remote sensing images. This study used the new type of deep convolutional neural network to extract land cover information from Qinhuangdao City, Hebei Province, and compared the experimental results with those obtained by traditional methods. The results show that: (1) The proposed deep convolutional neural network (DCNN) model can automatically construct the training dataset and classify images. This model performs the classification of multispectral and hyperspectral satellite images using deep neural networks, which improves the generalization ability of the model and simplifies the application of the model. (2) The proposed DCNN model provides the best classification results in the Qinhuangdao area. The overall accuracy of the land cover data obtained is 82.0%, and the kappa coefficient is 0.76. The overall accuracy is improved by 5% and 14% compared to the support vector machine method and the maximum likelihood classification method, respectively.

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

LENS CLASSIFICATION ACCORDING TO THE TYPE OF LIGHT SPOT USING A NEURAL NETWORK

2020 ◽  
Vol 2020 (4) ◽  
pp. 4-14
Author(s):  
Vladimir Budak ◽  
Ekaterina Ilyina
Keyword(s):  
Neural Network ◽  
Light Intensity ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Beam Angle ◽  
New Model ◽  
Technical Parameters ◽  
Transfer Training ◽  
Trained Network

The article proposes the classification of lenses with different symmetrical beam angles and offers a scale as a spot-light’s palette. A collection of spotlight’s images was created and classified according to the proposed scale. The analysis of 788 pcs of existing lenses and reflectors with different LEDs and COBs carried out, and the dependence of the axial light intensity from beam angle was obtained. A transfer training of new deep convolutional neural network (CNN) based on the pre-trained GoogleNet was performed using this collection. GradCAM analysis showed that the trained network correctly identifies the features of objects. This work allows us to classify arbitrary spotlights with an accuracy of about 80 %. Thus, light designer can determine the class of spotlight and corresponding type of lens with its technical parameters using this new model based on CCN.

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