Unsupervised change detection methods for remote sensing images

2002 ◽  
Author(s):  
Farid Melgani ◽  
Gabriele Moser ◽  
Sebastiano B. Serpico
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Detection Methods ◽  
Remote Sensing Images

scholarly journals SUNet: Change Detection for Heterogeneous Remote Sensing Images from Satellite and UAV Using a Dual-Channel Fully Convolution Network

2021 ◽  
Vol 13 (18) ◽  
pp. 3750
Author(s):  
Ruizhe Shao ◽  
Chun Du ◽  
Hao Chen ◽  
Jun Li
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Auxiliary Information ◽  
Image Data ◽  
Detection Task ◽  
Detection Methods ◽  
Change Detection Task ◽  
Remote Sensing Images ◽  
Data Set ◽  
Dual Channel

Change Detection in heterogeneous remote sensing images plays an increasingly essential role in many real-world applications, e.g., urban growth tracking, land use monitoring, disaster evaluation and damage assessment. The objective of change detection is to identify changes of geo-graphical entities or phenomena through two or more bitemporal images. Researchers have invested a lot in the homologous change detection and yielded fruitful results. However, change detection between heterogenous remote sensing images is still a great challenge, especially for change detection of heterogenous remote sensing images obtained from satellites and Unmanned Aerial Vehicles (UAV). The main challenges in satellite-UAV change detection tasks lie in the intensive difference of color for the same ground objects, various resolutions, the parallax effect and image distortion caused by different shooting angles and platform altitudes. To address these issues, we propose a novel method based on dual-channel fully convolution network. First, in order to alleviate the influence of differences between heterogeneous images, we employ two different channels to map heterogeneous remote sensing images from satellite and UAV, respectively, to a mutual high dimension latent space for the downstream change detection task. Second, we adopt Hough method to extract the edge of ground objects as auxiliary information to help the change detection model to pay more attention to shapes and contours, instead of colors. Then, IoU-WCE loss is designed to deal with the problem of imbalanced samples in change detection task. Finally, we conduct extensive experiments to verify the proposed method using a new Satellite-UAV heterogeneous image data set, named HTCD, which is annotated by us and has been open to public. The experimental results show that our method significantly outperforms the state-of-the-art change detection methods.

scholarly journals Land Cover Change Detection Based on Adaptive Contextual Information Using Bi-Temporal Remote Sensing Images

2018 ◽  
Author(s):  
Zhiyong Lv ◽  
Tongfei Liu ◽  
Penglin Zhang ◽  
Jón Atli Benediktsson ◽  
Yixiang Chen
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Contextual Information ◽  
Detection Methods ◽  
Remote Sensing Images ◽  
Central Pixel ◽  
Inventory Map ◽  
Land Cover Change Detection

Land cover change detection (LCCD) based on bi-temporal remote sensing images plays an important role in the inventory of land cover change. Due to the benefit of having spatial dependency properties within the image space while using remote sensing images for detecting land cover change, many contextual information based change detection methods have been proposed during past decades. However, there is still a space for improvement in accuracies and usability of LCCD. In this paper, a LCCD method based on adaptive contextual information is proposed. First, an adaptive region is constructed by gradually detecting the spectral similarity surrounding a central pixel. Second, the Euclidean distance between pairwise extended regions is calculated to measure the change magnitude between the pairwise central pixels of bi-temporal images. While the whole bi-temporal images are scanned pixel-by-pixel, the change magnitude image (CMI) can be generated. Then, the Otsu or a manual threshold is employed to acquire the binary change detection map (BCDM). The detection accuracies of the proposed approach are investigated by two land cover change cases with Landsat bi-temporal remote sensing images. In comparison to several widely used change detection methods, the proposed approach can achieve a land cover change inventory map with a competitive accuracy.

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