Abstract
Variation in the format and classification requirements for remote sensing data makes establishing a standard remote sensing sample dataset difficult. As a result, few remote sensing deep neural network models have been widely accepted. We propose a hybrid deep neural network model based on a convolutional auto-encoder and a complementary convolutional neural network to solve this problem. The convolutional auto-encoder supports feature extraction and data dimension reduction of remote sensing data. The extracted features are input into the convolutional neural network and subsequently classified. Experimental results show that in the proposed model, the classification accuracy increases from 0.916 to 0.944, compared to a traditional convolutional neural network model; furthermore, the number of training runs is reduced from 40,000 to 22,000, and the number of labelled samples can be reduced by more than half, all while ensuring a classification accuracy of no less than 0.9, which suggests the effectiveness and feasibility of the proposed model.
Highlights
Deep learning is an increasingly popular approach in remote sensing classification
convolutional neural network (CNN) are typically applied in remote sensing classification efforts in two ways: pixel-based classifications [3,4,5] and scene classifications [6,7]
While there are only a few studies that address object-oriented remote sensing classification based on CNNs, the first-place winner of Dstl’s Satellite
Summary
While traditional remote sensing classification methods require users to manually design features, deep learning, a new branch of machine learning, provides an effective framework for the automatic extraction of features [1,2]. A convolutional neural network (CNN) is a supervised deep learning algorithm, while an auto-encoder is a typical unsupervised learning algorithm. CNNs are typically applied in remote sensing classification efforts in two ways: pixel-based classifications [3,4,5] and scene classifications [6,7]. While there are only a few studies that address object-oriented remote sensing classification based on CNNs, the first-place winner of Dstl’s Satellite. Object-oriented remote sensing classification takes into account the spectral information of objects and considers the statistical, shape, texture, etc., information, which has helped improve classification accuracy [10] and is gaining increased attention from many researchers
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
