High-Resolution Land Cover Change Detection Using Low-Resolution Labels via a Semi-Supervised Deep Learning Approach - 2021 IEEE Data Fusion Contest Track MSD

2021 ◽  
Author(s):  
Lilin Tu ◽  
Jiayi Li ◽  
Xin Huang
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Land Cover ◽  
Data Fusion ◽  
Change Detection ◽  
Land Cover Change ◽  
Learning Approach ◽  
Low Resolution ◽  
Land Cover Change Detection

scholarly journals Shrub decline and expansion of wetland vegetation revealed by very high resolution land cover change detection in the Siberian lowland tundra

2021 ◽  
pp. 146877
Author(s):  
Rúna Í. Magnússon ◽  
Juul Limpens ◽  
David Kleijn ◽  
Ko van Huissteden ◽  
Trofim C. Maximov ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Wetland Vegetation ◽  
Very High ◽  
Land Cover Change Detection

Novel Adaptive Histogram Trend Similarity Approach for Land Cover Change Detection by Using Bitemporal Very-High-Resolution Remote Sensing Images

2019 ◽  
Vol 57 (12) ◽  
pp. 9554-9574 ◽  
Author(s):  
Zhi Yong Lv ◽  
Tong Fei Liu ◽  
Penglin Zhang ◽  
Jon Atli Benediktsson ◽  
Tao Lei ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Remote Sensing Images ◽  
Very High ◽  
Land Cover Change Detection

scholarly journals Image Fusion-Based Land Cover Change Detection Using Multi-Temporal High-Resolution Satellite Images

2017 ◽  
Vol 9 (8) ◽  
pp. 804 ◽  
Author(s):  
Biao Wang ◽  
Jaewan Choi ◽  
Seokeun Choi ◽  
Soungki Lee ◽  
Penghai Wu ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Change Detection ◽  
Image Fusion ◽  
Land Cover Change ◽  
Satellite Images ◽  
High Resolution Satellite Images ◽  
Multi Temporal ◽  
Land Cover Change Detection

scholarly journals Deep Learning for Land Cover Change Detection

2020 ◽  
Vol 13 (1) ◽  
pp. 78
Author(s):  
Oliver Sefrin ◽  
Felix M. Riese ◽  
Sina Keller
Keyword(s):  
Deep Learning ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Short Term Memory ◽  
Image Sequences ◽  
Land Cover Changes ◽  
Water Classification ◽  
Land Cover Change Detection ◽  
Sentinel 2

Land cover and its change are crucial for many environmental applications. This study focuses on the land cover classification and change detection with multitemporal and multispectral Sentinel-2 satellite data. To address the challenging land cover change detection task, we rely on two different deep learning architectures and selected pre-processing steps. For example, we define an excluded class and deal with temporal water shoreline changes in the pre-processing. We employ a fully convolutional neural network (FCN), and we combine the FCN with long short-term memory (LSTM) networks. The FCN can only handle monotemporal input data, while the FCN combined with LSTM can use sequential information (multitemporal). Besides, we provided fixed and variable sequences as training sequences for the combined FCN and LSTM approach. The former refers to using six defined satellite images, while the latter consists of image sequences from an extended training pool of ten images. Further, we propose measures for the robustness concerning the selection of Sentinel-2 image data as evaluation metrics. We can distinguish between actual land cover changes and misclassifications of the deep learning approaches with these metrics. According to the provided metrics, both multitemporal LSTM approaches outperform the monotemporal FCN approach, about 3 to 5 percentage points (p.p.). The LSTM approach trained on the variable sequences detects 3 p.p. more land cover changes than the LSTM approach trained on the fixed sequences. Besides, applying our selected pre-processing improves the water classification and avoids reducing the dataset effectively by 17.6%. The presented LSTM approaches can be modified to provide applicability for a variable number of image sequences since we published the code of the deep learning models. The Sentinel-2 data and the ground truth are also freely available.

scholarly journals Post-Processing Approach for Refining Raw Land Cover Change Detection of Very High-Resolution Remote Sensing Images

2018 ◽  
Vol 10 (3) ◽  
pp. 472 ◽  
Author(s):  
Zhiyong Lv ◽  
Tongfei Liu ◽  
Yiliang Wan ◽  
Jón Atli Benediktsson ◽  
Xiaokang Zhang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Post Processing ◽  
Remote Sensing Images ◽  
Processing Approach ◽  
Very High ◽  
Land Cover Change Detection

Land Cover Change Detection Techniques: Very-High-Resolution Optical Images: A Review

2021 ◽  
pp. 2-21
Author(s):  
Lv ZhiYong ◽  
Tongfei Liu ◽  
Jon Atli Benediktsson ◽  
Nicola Falco
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Detection Techniques ◽  
Optical Images ◽  
Very High ◽  
Land Cover Change Detection
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