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

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.

Detection of Multitemporal Changes with Artificial Neural Network-Based Change Detection Algorithm Using Hyperspectral Dataset

“…The proposed ANPC technique has also shown improved accuracy compared with other stated algorithms in the literature. Goswami et al [44] implemented a change detection technique for a multitemporal image with 91% accuracy, which is lesser than the proposed method and can detect fewer spectral bands only. Zhu et al [45] implemented continuous change detection and classification (CCDC) algorithm using a multispectral dataset and achieved an accuracy of 90%, which is less than the proposed technique and not applicable to higher resolution datasets, which is a part of the pre-classification technique.…”

“…Various authors have reported the work done over LULC using different change detection techniques. Goswami et al [44] implemented a change detection technique for multitemporal images. The decision tree algorithm with the PCC technique is proposed and compared with other change detection techniques, such as image differencing using a multispectral dataset.…”

Malware Analysis in IoT & Android Systems with Defensive Mechanism

“…The suggested system was tested on the ISCX botnet dataset, and the findings show that it works, with F-1 varying from 0.923 to 0.96, employing standard Machine-Learning techniques. Furthermore, employing an 80:20 train-test split and 10-fold cross-validation, the Extra Trees model [35] achieved up to 97.5 percent overall accuracy and 96 percent overall accuracy.…”

Monitoring of the Rehabilitation of the Historic World War II US Air Force Base in Greenland

“…We chose a quantitative approach of comparing raster pairs. Specifically, the classification-based change detection method [30,31] was used for this purpose. However, before comparing classification results, a sieve filter was run on each classified raster to remove isolated and obviously misclassified pixels or pixel chunks, as similar works suggest [32].…”