Abstract
Abstract. The automatic classification technology of remote sensing images is the key technology to extract the rich geo-information in remote sensing images and to monitor the dynamic changes of land use and ecological environment. Remote sensing images have the characteristics of large amount of information and many dimensions. Therefore, how to classify and extract the information in remote sensing images has become a crucial issue in the field of remote sensing science. With the development of neural network theory, many scholars have carried out research on the application of neural network models in remote sensing image classification. However, there are still some problems to be solved in artificial neural network methods. In this study, considering the problem of large-scale land classification for medium resolution and multi-spectral remote sensing imagery, an improved machine learning algorithm based on extreme learning machine for remote sensing classification has been developed via regularization theory. The improved algorithm is more suitable for the application of post-classification change monitoring of features in large scale imaging. In this study, our main job is to evaluate the performance of ELM with A-optimal design regularization (here we call it simply as A-optimal RELM). So the accuracy and efficiency of A-optimal RELM algorithm for remote sensing imagery classification, as well as the algorithms of support vector machine (SVM) and original ELM are compared in the experiments. The experimental results show that A-optimal RELM performs the best on all three different images with overall accuracy of 97.27% and 95.03% respectively. Besides, the A-optimal RELM performs better on the details of distinguish similar and confusing terrestrial object pixels.
Highlights
The remote sensing technology has been applied in many fields such as environment or urban monitoring
From the precision statistics table of Wuhan image, the A-optimal RELM method reaches the best result with the overall accuracy of 97.27% and the kappa coefficient of 0.9504, the standard extreme learning machine (ELM) has the lowest overall accuracy of 84.11%
The experimental results show that A-optimal RELM performs the best on two different images with overall accuracy of 97.27% and 95.03% respectively
Summary
The remote sensing technology has been applied in many fields such as environment or urban monitoring. Feng et al used dynamic BP algorithm to train the MLP learning model for mail classification study and showed a significant improvement in learning efficiency and classification accuracy compared to the traditional MLP model(Feng and Daqi, 2013). These methods such as neuron network algorithms have gone through a process ranging from simple to complex, from specific to extensive and from single method to multi-combined. An optimal algorithm with A-optimized design regularization ELM is proposed and applied to real remote sensing classification experiments
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
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 call
