Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications

By Zhe Zhu. 2017

775 citations recorded by the source corpus.

Subfields: Atmospheric Science, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/8d2db0697217e4e2239ece12c6ed0f1b2389ec86

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Citations in the corpus

Deep learning for change detection in remote sensing: a review (2022, background): “2017 ) R e c u rr e n t M e t h o d ( R M ) U s e s r e c u rr e n t c o nn e c t i o n s b e t w ee n m u l t i - d a t e i m a g e s t o l e a r n d ee p f e a t u r e s .”
Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art (2019, neutral): “(% ) [1 40 ] a LW m JK Sr c”
A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 Data Revisit Intervals and Implications for Terrestrial Monitoring (2017, background): “This will benefit a large number of remote sensing applications, particularly land cover and change monitoring applications [5,6,18,38].”
Detecting Ecological Changes with a Remote Sensing Based Ecological Index (RSEI) Produced Time Series and Change Vector Analysis (2019, methodology): “To understand the attributes of ecological changes, a simple image differencing method [60] was employed using the 5-leveled RSEI images with the formula:”
Current status of Landsat program, science, and applications (2019, methodology): “Reviews of recent algorithm development and change products are available in Zhu (2017), Wulder et al. (2012), and Wulder et al. (2018).”
A cloud detection algorithm for satellite imagery based on deep learning (2019, methodology): “Existing machine learning based methods, such as the neural networks approach investigated in (Hughes and Hayes, 2014), were found to perform sub par compared to the Fmask algorithm (Zhu, 2017).”
Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine (2020, methodology): “The Landsat Collection 1 Tier 1 was orthorectified accounting for pixel co-registration and the correction of displacement errors with the pixel spatial resolution of 30 m, and is normalized to TOA reflectance, making it suitable for LULC change analysis [47].”
Implementation of the LandTrendr Algorithm on Google Earth Engine (2018, methodology): “After the opening of the Landsat image archive in 2008 [1], time series analysis of Landsat imagery has blossomed, with rapid development of new algorithms and capabilities for change detection [2].”