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<p class="StyleTitleLeft005cm"><span lang="EN-GB">Scene Based Classification of Aerial Images using Convolution Neural Networks</span></p><br />

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Title Statement <p class="StyleTitleLeft005cm"><span lang="EN-GB">Scene Based Classification of Aerial Images using Convolution Neural Networks</span></p><br />
 
Added Entry - Uncontrolled Name Mahajan, Palak ; University of Jammu
Abrol, Pawanesh ; University of Jammu
Lehana, Parveen K; University of Jammu
 
Uncontrolled Index Term Deep learning; Convolution Neural Networks,;Feature extraction; Image classification
 
Summary, etc. The advent of computer vision and evolution of high-end computing in remote sensing images have embellish various researchers for unprecedented development in remotely sensed aerial images. The requirement to extract essential information stimulated anatomization of aerial images for its usefulness. Deep learning provides state of the art solutions for widely explored visual recognition system and has emerged as an evolutionary area, being applicable to large scale image processing applications. Convolutional Neural Networks (CNNs), an essential component of deep learning algorithms consists of increasing the depth and connections in the processing layers to learn various features of data at different abstract levels. . In this paper, we present an outlook for classifying and extracting the features of aerial images using CNN. We propose a CNN architecture based on various parameters and layers for classification. CNN has been evaluated on two publicly available aerial data sets: UCMerced Land Use and RSSCN7. Experimental results show that the proposed CNN architecture is competent and efficient in terms of accuracy as performance evaluation parameter in comparison with conventional classifiers like Bag of Visual Words (BOVW).
 
Publication, Distribution, Etc. Journal of Scientific and Industrial Research (JSIR)
2021-01-11 11:19:19
 
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http://op.niscair.res.in/index.php/JSIR/article/view/36671
 
Data Source Entry Journal of Scientific and Industrial Research (JSIR); ##issue.vol## 79, ##issue.no## 12 (20)
 
Language Note en