Novel Land Cover Change Detection Method Based on k-Means Clustering and Adaptive Majority Voting Using Bitemporal Remote Sensing Images

Abstract

Land cover change detection (LCCD) based on bitemporal remote sensing images has become a popular topic in the field of remote sensing. Despite numerous methods promoted in recent decades, an improvement on the usability and performance of these methods has remained necessary. In this paper, a novel LCCD approach based on the integration of k-means clustering and adaptive majority voting (k-means_AMV) techniques have been developed. The proposed k-means_AMV method consists of three major techniques. First, to utilize the contextual information in an adaptive manner, an adaptive region around a central pixel is constructed by detecting the spectral similarity between the central pixel and its eight neighboring pixels. Second, when the extension for the adaptive region is terminated, the k-means clustering method is applied to determine the label of each pixel within the adaptive region. Finally, an existing AMV technique is used to refine the label of the central pixel of the adaptive region. When change magnitude image (CMI) is scanned and processed in this manner, the label of each pixel in the CMI can be refined and the binary change detection map can be generated. Three image scenes related to different land cover change events are adapted to test the effectiveness and performance of the proposed k-means_AMV approach. The results show that the proposed k-means_AMV approach demonstrates better detection accuracies and visual performance than that of the several extensively used methods. INDEX TERMS Adaptive majority voting, k-means clustering, land cover change detection, remote sensing images.

A Modeling and Measurement Approach for the Uncertainty of Features Extracted from Remote Sensing Images

“…The proposed approach mainly involves three parameters, namely, the neighborhood window size K when calculating the GSU, the number m of adjacent feature points when calculating the FSU, and the adjustment coefficient λ when calculating the FUI. In the discussion, we use a control variable method [47] to analyze the sensitivity between the three parameters and the measurement accuracy of feature uncertainty in accordance with the designed verification scheme I. Moreover, the correlation coefficient R between the feature uncertainty levels and classification error rates is used to represent the measurement accuracy of feature uncertainty.…”

Change Detection in Multitemporal High Spatial Resolution Remote-Sensing Images Based on Saliency Detection and Spatial Intuitionistic Fuzzy C-Means Clustering

“…Remote-sensing image change detection is the main mean to identify and monitor the information on land-covered change caused by human activities and natural processes. At present, change detection has been widely used in forest cover monitoring [1,2], land use/land cover monitoring [3,4], disaster assessment [5], urban expansion analysis [6,7], and other fields.…”

Spatial–Spectral Feature Fusion Coupled with Multi-Scale Segmentation Voting Decision for Detecting Land Cover Change with VHR Remote Sensing Images

“…In these methods, a binary threshold is adopted to separate the pixels of the change magnitude image (CMI) into "changed" and "unchanged". Some advantages of binary change detection are that it is straightforward and operational; however, the limitation of this method is that it can only provide the size and distribution of the change target without providing more details on the change information [25,26]. In contrast, the "from-to" change method can directly recognize the kinds of changes "from one to another".…”