Evaluation of Sentinel 2 Red Edge Channel for Enhancing Land Use Classification

Klaten Regency is one of the regencies in Central Java Province that has an increasing population every year. This can cause an increase in built-up land for human activities. The built-up land needs to be monitored so that the construction is in accordance with the regional development plan so that it does not cause problems such as the occurrence of critical land. Therefore, it is necessary to monitor land use regularly. One method for monitoring land use is the remote sensing method. The remote sensing method is much more efficient in mapping land use because without having to survey the field. The remote sensing method utilizes satellite imagery data that can be processed for land use classification. This study uses the sentinel 2 satellite image data with the Object-Based Image Analysis (OBIA) algorithm to obtain land use classification. Sentinel 2 satellite imagery is a medium resolution image category with a spatial resolution of 10 meters. The land use classification can be used to see the distribution of built-up land in Klaten Regency without having to conduct a field survey. The results of the study obtained a segmentation scale parameter value of 60 and a merge scale parameter value of 85. The classification results obtained by 5 types of land use with OBIA. Agricultural land use dominates with an area of 50% of the total area.

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Understanding deep learning in land use classification based on Sentinel-2 time series

Scientific Reports ◽

10.1038/s41598-020-74215-5 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Manuel Campos-Taberner ◽

Francisco Javier García-Haro ◽

Beatriz Martínez ◽

Emma Izquierdo-Verdiguier ◽

Clement Atzberger ◽

...

Keyword(s):

Neural Network ◽

Land Use ◽

Time Series ◽

Deep Learning ◽

Agricultural Policy ◽

Near Infrared ◽

Land Use Classification ◽

Policy Compliance ◽

Economic Return ◽

Sentinel 2

Abstract The use of deep learning (DL) approaches for the analysis of remote sensing (RS) data is rapidly increasing. DL techniques have provided excellent results in applications ranging from parameter estimation to image classification and anomaly detection. Although the vast majority of studies report precision indicators, there is a lack of studies dealing with the interpretability of the predictions. This shortcoming hampers a wider adoption of DL approaches by a wider users community, as model’s decisions are not accountable. In applications that involve the management of public budgets or policy compliance, a better interpretability of predictions is strictly required. This work aims to deepen the understanding of a recurrent neural network for land use classification based on Sentinel-2 time series in the context of the European Common Agricultural Policy (CAP). This permits to address the relevance of predictors in the classification process leading to an improved understanding of the behaviour of the network. The conducted analysis demonstrates that the red and near infrared Sentinel-2 bands convey the most useful information. With respect to the temporal information, the features derived from summer acquisitions were the most influential. These results contribute to the understanding of models used for decision making in the CAP to accomplish the European Green Deal (EGD) designed in order to counteract climate change, to protect biodiversity and ecosystems, and to ensure a fair economic return for farmers.

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Towards a Multi-Temporal Deep Learning Approach for Mapping Urban Fabric Using Sentinel 2 Images

Remote Sensing ◽

10.3390/rs12030423 ◽

2020 ◽

Vol 12 (3) ◽

pp. 423 ◽

Cited By ~ 1

Author(s):

Lamiae El Mendili ◽

Anne Puissant ◽

Mehdi Chougrad ◽

Imane Sebari

Keyword(s):

Land Use ◽

Deep Learning ◽

Urban Land ◽

Urban Land Use ◽

Land Use Classification ◽

Urban Fabric ◽

Temporal Dimension ◽

The Future ◽

Multi Temporal ◽

Sentinel 2

The major part of the population lives in urban areas, and this is expected to increase in the future. The main challenges faced by cities currently and towards the future are the rapid urbanization, the increase in urban temperature and the urban heat island. Mapping and monitoring urban fabric (UF) to analyze the environmental impact of these phenomena is more necessary than ever. This coupled with the increased availability of Earth observation data and their growing temporal capabilities leads us to consider using temporal features for improving land use classification, especially in urban environments where the spectral overlap between classes makes it challenging. Urban land use classification thus remains a central question in remote sensing. Although some research studies have successfully used multi-temporal images such as Landsat-8 or Sentinel-2 to improve land cover classification, urban land use mapping is rarely carried using the temporal dimension. This paper explores the use of Sentinel-2 data in a deep learning framework, by firstly assessing the temporal robustness of four popular fully convolutional neural networks (FCNs) trained over single-date images for the classification of the urban footprint, and secondly, by proposing a multi-temporal FCN. A performance comparison between the proposed framework and a regular FCN is also conducted. In this study, we consider four UF classes typical of many European Western cities. Results show that training the proposed multi-date model on Sentinel 2 multi-temporal data achieved the best results with a Kappa coefficient increase of 2.72% and 6.40%, respectively for continuous UF and industrial facilities. Although a more definitive conclusion requires further testing, first results are promising because they confirm that integrating the temporal dimension with a high spatial resolution into urban land use classification may be a valuable strategy to discriminate among several urban categories.

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Landsat-8 vs. Sentinel-2: Landuse Landcover Change Analysis and Differences in Gudur Municipality

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.32711 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 160-166

Author(s):

R. Sanjeeva Reddy

Keyword(s):

Land Use ◽

Spatial Resolution ◽

Landsat Imagery ◽

European Space Agency ◽

Landsat 8 ◽

Landsat Data ◽

Red Edge ◽

Edge Band ◽

Sentinel 2

With the recent free availability of moderate to high spatial resolution data (10m-30m), land use analysis became more robust. The launch of Sentinel-2a by the European Space Agency, coupled with the availability of free Landsat data, availed more analysis capabilities for the science community with a wide variety of temporal, spatial, and spectral capabilities. This study compares the synergetic use of Landsat and Sentinel-2 in mapping Land Use Land cover themes in Gudur, explicitly utilizing the red edge band of Sentinel-2. A combination of both sentinel and Landsat data results in higher spatial resolution. Classification of the red edge band produces better resolution than the classification of Landsat Imagery.

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Land-Cover and Land-Use Classification Based on Multitemporal Sentinel-2 Data

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2018.8519301 ◽

2018 ◽

Author(s):

Martin Weinmann ◽

Uwe Weidner

Keyword(s):

Land Use ◽

Land Cover ◽

Land Use Classification ◽

Sentinel 2

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Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data

GIScience & Remote Sensing ◽

10.1080/15481603.2019.1650447 ◽

2019 ◽

Vol 57 (1) ◽

pp. 1-20 ◽

Cited By ~ 30

Author(s):

Abdulhakim Mohamed Abdi

Keyword(s):

Machine Learning ◽

Land Use ◽

Land Cover ◽

Learning Algorithms ◽

Classification Performance ◽

Machine Learning Algorithms ◽

Land Use Classification ◽

Boreal Landscape ◽

Sentinel 2

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Evaluation of Semantic Segmentation Methods for Land Use with Spectral Imaging Using Sentinel-2 and PNOA Imagery

Remote Sensing ◽

10.3390/rs13122292 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2292

Author(s):

Oscar D. Pedrayes ◽

Darío G. Lema ◽

Daniel F. García ◽

Rubén Usamentiaga ◽

Ángela Alonso

Keyword(s):

Land Use ◽

Satellite Imagery ◽

State Of The Art ◽

Semantic Segmentation ◽

Aerial Imagery ◽

Land Use Classification ◽

Cost Performance ◽

Sampling Distance ◽

Segmentation Methods ◽

Sentinel 2

Land use classification using aerial imagery can be complex. Characteristics such as ground sampling distance, resolution, number of bands and the information these bands convey are the keys to its accuracy. Random Forest is the most widely used approach but better and more modern alternatives do exist. In this paper, state-of-the-art methods are evaluated, consisting of semantic segmentation networks such as UNet and DeepLabV3+. In addition, two datasets based on aircraft and satellite imagery are generated as a new state of the art to test land use classification. These datasets, called UOPNOA and UOS2, are publicly available. In this work, the performance of these networks and the two datasets generated are evaluated. This paper demonstrates that ground sampling distance is the most important factor in obtaining good semantic segmentation results, but a suitable number of bands can be as important. This proves that both aircraft and satellite imagery can produce good results, although for different reasons. Finally, cost performance for an inference prototype is evaluated, comparing various Microsoft Azure architectures. The evaluation concludes that using a GPU is unnecessarily costly for deployment. A GPU need only be used for training.

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