Change detection in Landsat images based on local neighbourhood information. Issue 11 (17th September 2018)

• Record Type: Journal Article
• Authors: Gupta, Neha; Pillai, Gargi V.; Ari, Samit
• Is Part Of: IET image processing. Volume 12:Issue 11(2018)
• Page Start: 2051
• Page End: 2058
• Publication Date: 2018-09-17
• Subjects: remote sensing -- pattern clustering -- geophysical image processing -- object detection -- image classification -- geophysical signal processing -- image segmentation; change detection -- local neighbourhood information -- bitemporal multispectral images -- image processing task -- Otsu's thresholding -- local information based approach -- bitemporal Landsat images -- bitemporal images -- overlapping image blocks -- pixel position -- concatenated block -- inter‐block information -- threshold calculation -- calculated threshold -- binary values -- unchanged pixel -- changed pixel -- binary change map -- binary descriptor based method -- noise immunity -- atmospheric sunlight conditions; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.5524
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : In this study, a novel technique is proposed to detect the changes in bitemporal multispectral images. Utilisation of the local neighbourhood information in any image processing task may provide good noise immunity and reduces false alarms. Motivated by this, Otsu's thresholding of local information based approach is proposed in this work. It shows the effective performance in change detection of bitemporal Landsat images which suffer from different atmospheric and sunlight conditions. To get the local information around each pixel, both bitemporal images are partitioned into overlapping image blocks. Every block of the first image is concatenated with the corresponding block of the second image for each pixel position. Thus, the information of the concatenated block is considered as inter‐block information. Further, Otsu's method is applied on the concatenated block for threshold calculation. Depending on the threshold, binary values are generated. Finally, binary values of both images for all bands are compared by XOR operation to detect it as the background i.e. unchanged pixel or foreground i.e. changed pixel. On the basis of majority class present in XOR output, binary change map is generated. Experiments conducted on Landsat images show that the proposed method provides better performance compared to reported techniques.