Change Detection in Remote Sensing Image Data Comparing Algebraic and Machine Learning Methods

Anjali Goswami,Harani Mathuku,Syam Machinathu Parambil Gangadharan,Saroj Kumar Sahu,Deepak Sharma,Hazra Imran,Chandra Shekhar Yadav,Manoj Kumar Pradhan,Jagendra Singh

doi:10.3390/electronics11030431

Abstract

Remote sensing technology has penetrated all the natural resource segments as it provides precise information in an image mode. Remote sensing satellites are currently the fastest-growing source of geographic area information. With the continuous change in the earth’s surface and the wide application of remote sensing, change detection is very useful for monitoring environmental and human needs. So, it is necessary to develop automatic change detection techniques to improve the quality and reduce the time required by manual image analysis. This work focuses on the improvement of the classification accuracy of the machine learning techniques by reviewing the training samples and comparing the post-classification comparison with the image differencing in the algebraic technique. Landsat data are medium spatial resolution data; that is why pixel-wise computation has been applied. Two change detection techniques have been studied by applying a decision tree algorithm using a separability matrix and image differencing. The first change detection, e.g., the separability matrix, is a post-classification comparison in which individual images are classified by a decision tree algorithm. The second change detection is, e.g., the image differencing change detection technique in which changed and unchanged pixels are determined by applying the corner method to calculate the threshold on the changing image. The performance of the machine learning algorithm has been validated by 10-fold cross-validation. The experimental results show that the change detection using the post-classification method produced better results when compared to the image differencing of the algebraic change detection technique.

Highlights

Introduction conditions of the Creative CommonsChange detection is the process of finding and evaluating access points in multi‐spectra images that have undergone spatial or spectral changes
As ground truthing is outside the scope of this study, the objective of the proposed study is to review the training of classifiers to improve the classifier accuracy of the machine learning techniques and compare them with the algebraic techniques for change detection in remote sensing images
The red color is for the water class, the green color is for the built‐up class, the blue color is for the sand class, and the yellow color is for the vegetation class

Summary

Introduction

Introduction conditions of the Creative CommonsChange detection is the process of finding and evaluating access points in multi‐spectra images that have undergone spatial or spectral changes. Change detection is the process of finding and evaluating access points in multi‐. Change detection is often defined as the comparison of two co‐registered views of the same geographic area captured at successive periods. The ability to recognize the possible label change on the ground is enabled by the availability of shots of the same geographic area taken by sensors Attribution (CC BY). Change detection is the process of identifying a group of pixels in a prior dataset that has changed significantly. Remote sensing plays a significant role in a variety of application areas, including land cover classification, deforestation, disaster monitoring, glacier management, urban expansion monitoring, and environmental study change detection. Understanding the linkages and interactions between human and natural phenomena and promoting improved decision making requires timely and precise change detection of the earth’s surface

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Conclusion