Change detection from remotely sensed images: From pixel-based to object-based approaches

By Masroor Hussain, Dongmei Chen, Angela Cheng, Huimin Wei, D. Stanley. 2013

1,511 citations recorded by the source corpus.

Subfields: Atmospheric Science, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/fedf0d43fbef3d14ad5bf0703ab93aa52f449977

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Citations in the corpus

Multitask learning for large-scale semantic change detection (2018, methodology): “Many algorithms use out-of-the-box registration algorithms and focus on the other main steps for change detection (Hussain et al., 2013).”
DASNet: Dual Attentive Fully Convolutional Siamese Networks for Change Detection in High-Resolution Satellite Images (2020, methodology): “Traditional change detection methods can be divided into two categories according to the research objects: pixel-based change detection methods and object-based change detection methods [11].”
Deep learning for change detection in remote sensing: a review (2022, background): “However, the extraction accuracy is heavily dependent on the accuracy of the initial segmentation (Cao et al. 2014; Tewkesbury et al. 2015; Hussain et al. 2013).”
Optical Remote Sensing Image Change Detection Based on Attention Mechanism and Image Difference (2021, methodology): “In general, change detection plays an important role in urban planning [1], forest monitoring [2], disaster assessment [3]–[5], and the dynamic detection of land use [6].”
Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring (2019, neutral): “[143].”
Fully Convolutional Siamese Networks for Change Detection (2018, background): “Its study has a long history, and it has evolved alongside the areas of image processing and computer vision [1, 2].”
SemiCDNet: A Semisupervised Convolutional Neural Network for Change Detection in High Resolution Remote-Sensing Images (2020, background): “An extensive summary and reviews of CD algorithms have been made in [5]–[7], where it has been shown that CD techniques evolved with the development of machine learning and pattern recognition.”
How do you find the green sheep? A critical review of the use of remotely sensed imagery to detect and count animals (2018, background): “Despite the lower resolution requirements, cost may be a constraint because two sets of images are required and should be obtained from the same sensor, at the same time of day, less than a week apart to minimise changes in sun angle and vegetation (Hussain et al., 2013; Terletzky & Ramsey, 2014).”
Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks (2018, methodology): “The first techniques that were proposed used manually crafted processes to identify changes, while later methods have proposed using machine learning algorithms in different ways [1, 2, 3, 4].”
Deep Learning-Based Change Detection in Remote Sensing Images: A Review (2022, background): “Review papers [23] discussed pixel-based to object-based change detection methods and highlight some issues.”
CHANGE DETECTION IN REMOTE SENSING IMAGES USING CONDITIONAL ADVERSARIAL NETWORKS (2018, background): “A lot of change detection techniques are developed for remote sensing applications (Singh et al., 1989; Lu et al. 2004; Chen et al. 2013; Hussain et al. 2013).”
A critical synthesis of remotely sensed optical image change detection techniques (2015, methodology): “In the broadest sense image pixels and image-objects are the two main categories of analysis 56 unit presented in the change detection literature (G. Chen et al., 2012; Hussain et al., 2013).”
Developments in Landsat Land Cover Classification Methods: A Review (2017, methodology): “OBIA has an advantage because it incorporates various sources of information like texture, shape and position as the basis for classification [19,48,80].”