Integration of Sentinel-1 and Sentinel-2 Data for Land Cover Mapping Using W-Net

Gargiulo, Massimiliano; Dell'Aglio, Domenico A. G.; Iodice, Antonio; Riccio, Daniele; Ruello, Giuseppe

doi:10.3390/s20102969

Cited by 35 publications

(29 citation statements)

References 50 publications

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“…U-Net is an extension of FCN and is currently a widely used semantic segmentation network with good scalability [52]. The excellent characteristics of U-Net make it widely used in remote sensing image classification and change detection and have achieved good results [50,52]. However, the number of convolutional layers is small, and the Batch Normalization layer is lacking, which causes problems such as low learning efficiency, learning effect greatly affected by the initial data distribution, and gradient explosion in the backpropagation process [3,47,52].…”

Section: Methodsmentioning

confidence: 99%

“…We named it squeeze-and-excitation W-Net. Although there have been related studies on network transformation based on U-Net [50,51], as far as we know, we are the first to transform U-Net into a more valuable network. It has two-sided input and single-output, independent weights on both sides can take into account the data on both sides (homogeneous and heterogeneous data) and can be used for change detection tasks in the field of remote sensing.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Squeeze-and-Excitation W-Net for 2D and 3D Building Change Detection with Multi-Source and Multi-Feature Remote Sensing Data

Zhang

Wang

et al. 2021

Remote Sensing

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Building Change Detection (BCD) is one of the core issues in earth observation and has received extensive attention in recent years. With the rapid development of earth observation technology, the data source of remote sensing change detection is continuously enriched, which provides the possibility to describe the spatial details of the ground objects more finely and to characterize the ground objects with multiple perspectives and levels. However, due to the different physical mechanisms of multi-source remote sensing data, BCD based on heterogeneous data is a challenge. Previous studies mostly focused on the BCD of homogeneous remote sensing data, while the use of multi-source remote sensing data and considering multiple features to conduct 2D and 3D BCD research is sporadic. In this article, we propose a novel and general squeeze-and-excitation W-Net, which is developed from U-Net and SE-Net. Its unique advantage is that it can not only be used for BCD of homogeneous and heterogeneous remote sensing data respectively but also can input both homogeneous and heterogeneous remote sensing data for 2D or 3D BCD by relying on its bidirectional symmetric end-to-end network architecture. Moreover, from a unique perspective, we use image features that are stable in performance and less affected by radiation differences and temporal changes. We innovatively introduced the squeeze-and-excitation module to explicitly model the interdependence between feature channels so that the response between the feature channels is adaptively recalibrated to improve the information mining ability and detection accuracy of the model. As far as we know, this is the first proposed network architecture that can simultaneously use multi-source and multi-feature remote sensing data for 2D and 3D BCD. The experimental results in two 2D data sets and two challenging 3D data sets demonstrate that the promising performances of the squeeze-and-excitation W-Net outperform several traditional and state-of-the-art approaches. Moreover, both visual and quantitative analyses of the experimental results demonstrate competitive performance in the proposed network. This demonstrates that the proposed network and method are practical, physically justified, and have great potential application value in large-scale 2D and 3D BCD and qualitative and quantitative research.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Squeeze-and-Excitation W-Net for 2D and 3D Building Change Detection with Multi-Source and Multi-Feature Remote Sensing Data

Zhang

Wang

et al. 2021

Remote Sensing

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“…Remote sensing technology has been the topic of another three papers of this Special Issue. Paper [ 15 ] presented an approach that combines Sentinel 1 and Sentinel 2 data for land cover mapping, to overcome the limitation of the methods based on Sentinel 2 data, that are unusable in presence of cloud covers.…”

Section: Contributionsmentioning

confidence: 99%

Metrology for Agriculture and Forestry 2019

Chirico

Bonavolontà

2020

Sensors

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“…Furthermore, this architecture is designed to work with a small sample size, a common problem for LULC classification [25,40]. Nevertheless, the U-net has rarely been trained using multispectral bands (MS) besides RGB ones or in combination with synthetic aperture radar images (SAR) [31,32,37,49], even though the combination of MS and SAR imagery has provided more accurate results to generate LULC maps [7,[50][51][52][53]. For example, the information of MS images can be very useful to differentiate among certain LULC classes (e.g., water, bare soil, vegetation); however, SAR data can interact with the structure of vegetation (i.e., branches, leaves, stems) and therefore can potentially discriminate between forests and plantations [54].…”

Section: Introductionmentioning

confidence: 99%

Land Use Land Cover Classification with U-Net: Advantages of Combining Sentinel-1 and Sentinel-2 Imagery

et al. 2021

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The U-net is nowadays among the most popular deep learning algorithms for land use/land cover (LULC) mapping; nevertheless, it has rarely been used with synthetic aperture radar (SAR) and multispectral (MS) imagery. On the other hand, the discrimination between plantations and forests in LULC maps has been emphasized, especially for tropical areas, due to their differences in biodiversity and ecosystem services provision. In this study, we trained a U-net using different imagery inputs from Sentinel-1 and Sentinel-2 satellites, MS, SAR and a combination of both (MS + SAR); while a random forests algorithm (RF) with the MS + SAR input was also trained to evaluate the difference in algorithm selection. The classification system included ten classes, including old-growth and secondary forests, as well as old-growth and young plantations. The most accurate results were obtained with the MS + SAR U-net, where the highest overall accuracy (0.76) and average F1-score (0.58) were achieved. Although MS + SAR and MS U-nets gave similar results for almost all of the classes, for old-growth plantations and secondary forest, the addition of the SAR band caused an F1-score increment of 0.08–0.11 (0.62 vs. 0.54 and 0.45 vs. 0.34, respectively). Consecutively, in comparison with the MS + SAR RF, the MS + SAR U-net obtained higher F1-scores for almost all the classes. Our results show that using the U-net with a combined input of SAR and MS images enabled a higher F1-score and accuracy for a detailed LULC map, in comparison with other evaluated methods.

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Integration of Sentinel-1 and Sentinel-2 Data for Land Cover Mapping Using W-Net

Cited by 35 publications

References 50 publications

A Novel Squeeze-and-Excitation W-Net for 2D and 3D Building Change Detection with Multi-Source and Multi-Feature Remote Sensing Data

A Novel Squeeze-and-Excitation W-Net for 2D and 3D Building Change Detection with Multi-Source and Multi-Feature Remote Sensing Data

Metrology for Agriculture and Forestry 2019

Land Use Land Cover Classification with U-Net: Advantages of Combining Sentinel-1 and Sentinel-2 Imagery

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