Monitoring land-cover changes by combining a detection step with a classification step

Abstract

CitationHarrou F, Zerrouki N, Sun Y, Hocini L (2018) Monitoring landcover changes by combining a detection step with a classification step. 2018 IEEE Symposium Series on Computational Intelligence (SSCI). Available: http://dx.Abstract-An approach merging the Hotelling T 2 control scheme with weighted random forest classifier is proposed and used in the context of detecting land cover changes via remote sensing and radiometric measurements. Hotelling T 2 procedure is introduced to identify features corresponding to changed areas. However, T 2 scheme is not able to separate real from false changes. To tackle this limitation, the weighted random forest algorithm, which is an efficient classification technique for unbalanced problems, has been successfully applied on features of the detected pixels to recognize the type of change. The performance of the algorithm is evaluated using SZTAKI AirChange benchmark data, results show that the proposed detection scheme succeeds to appropriately identify changes to land cover. Also, we compared the proposed approach to that of the conventional algorithms (i.e., neural network, random forest, support vector machine and k-nearest neighbors) and found improved performance.

Desertification Detection Using an Improved Variational Autoencoder-Based Approach Through ETM-Landsat Satellite Data

“…It has a vital role in improving the valuation of burned areas, shifting cultivation, monitoring pollution, assessing deforestation, urban growth, and desertification. All over the years, with the imminent need and the availability of data repositories, various methods for change detection have been devised in the remote sensing field [2][3][4]. This work focuses on desertification detection, which is one of the most challenging applications in the LCCD.…”

Change Detection Method of High Resolution Remote Sensing Image Based on D-S Evidence Theory Feature Fusion

“…The application of a single difference image may cause seriously missed detection and false detection [34][35][36]. With the progress and development of machine learning theory, various machine learning techniques such as support vector machine (SVM) [37][38][39][40][41] and random forest (RF) [42][43][44][45][46] have been applied for change detection. In recent years, with the rapid development of big data and artificial intelligence technology, deep learning methods such as deep belief networks, convolutional neural network and twin network [47][48][49][50][51][52] has been applied to change detection, which improves the accuracy of change detection greatly.…”