Improving the Accuracy of Multiple Algorithms for Crop Classification by Integrating Sentinel-1 Observations with Sentinel-2 Data
TL;DR: In this article, the authors evaluated the performance of 22 nonparametric classifiers on which most of these algorithms had not been tested before with SAR data and found that the best performing scenario was the one integrating VH and VV with normalized difference vegetation index (NDVI) and cubic support vector machine (SVM) as the classifier with the highest accuracy among all those tested.
Abstract: The availability of an unprecedented amount of open remote sensing data, such as Sentinel-1 and -2 data within the Copernicus program, has boosted the idea of combining the use of optical and radar data to improve the accuracy of agricultural applications such as crop classification. Sentinel-1’s Synthetic Aperture Radar (SAR) provides co- and cross-polarized backscatter, which offers the opportunity to monitor agricultural crops using radar at high spatial and temporal resolution. In this study, we assessed the potential of integrating Sentinel-1 information (VV and VH backscatter and their ratio VH/VV with Sentinel-2A data (NDVI) to perform crop classification and to define which are the most important input data that provide the most accurate classification results. Further, we examined the temporal dynamics of remote sensing data for cereal, horticultural, and industrial crops, perennials, deciduous trees, and legumes. To select the best SAR input feature, we tried two approaches, one based on classification with only SAR features and one based on integrating SAR with optical data. In total, nine scenarios were tested. Furthermore, we evaluated the performance of 22 nonparametric classifiers on which most of these algorithms had not been tested before with SAR data. The results revealed that the best performing scenario was the one integrating VH and VV with normalized difference vegetation index (NDVI) and cubic support vector machine (SVM) (the kernel function of the classifier is cubic) as the classifier with the highest accuracy among all those tested.
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TL;DR: In this paper, the authors presented the first continental crop type map at 10m spatial resolution for the EU based on S1A and S1B Synthetic Aperture Radar observations for the year 2018.
48 citations
TL;DR: In this article , an approach combining multispectral and Synthetic Aperture Radar (SAR) time series for the classification of 17 crop classes at 10 m spatial resolution for Germany in the year 2018 was presented.
Abstract: Nationwide and consistent information on agricultural land use forms an important basis for sustainable land management maintaining food security, (agro)biodiversity, and soil fertility, especially as German agriculture has shown high vulnerability to climate change. Sentinel-1 and Sentinel-2 satellite data of the Copernicus program offer time series with temporal, spatial, radiometric, and spectral characteristics that have great potential for mapping and monitoring agricultural crops. This paper presents an approach which synergistically uses these multispectral and Synthetic Aperture Radar (SAR) time series for the classification of 17 crop classes at 10 m spatial resolution for Germany in the year 2018. Input data for the Random Forest (RF) classification are monthly statistics of Sentinel-1 and Sentinel-2 time series. This approach reduces the amount of input data and pre-processing steps while retaining phenological information, which is crucial for crop type discrimination. For training and validation, Land Parcel Identification System (LPIS) data were available covering 15 of the 16 German Federal States. An overall map accuracy of 75.5% was achieved, with class-specific F1-scores above 80% for winter wheat, maize, sugar beet, and rapeseed. By combining optical and SAR data, overall accuracies could be increased by 6% and 9%, respectively, compared to single sensor approaches. While no increase in overall accuracy could be achieved by stratifying the classification in natural landscape regions, the class-wise accuracies for all but the cereal classes could be improved, on average, by 7%. In comparison to census data, the crop areas could be approximated well with, on average, only 1% of deviation in class-specific acreages. Using this streamlined approach, similar accuracies for the most widespread crop types as well as for smaller permanent crop classes were reached as in other Germany-wide crop type studies, indicating its potential for repeated nationwide crop type mapping.
15 citations
TL;DR: Identification of seasonal changes and their impact using collected geospatial images within a specific time frame to predict land utilized for agriculture.
14 citations
TL;DR: In this paper , a 3D-CNN framework was proposed for classifying crops that is based on the fusion of radar and optical time series and also fully exploits 3D spatial-temporal information.
Abstract: Abstract Remote sensing is a most promising technique for providing crop maps, thanks to the development of satellite images at various temporal and spatial resolutions. Three-dimensional (3D) convolutional neural networks (CNNs) have the potential to provide rich features that represent the spatial and temporal patterns of crops when applied to time series. This study presents a novel 3D-CNN framework for classifying crops that is based on the fusion of radar and optical time series and also fully exploits 3D spatial-temporal information. To extract deep convolutional maps, the proposed technique uses one separate sequence for each time series dataset. To determine the label of each pixel, the extracted feature maps are passed to the concatenating layer and subsequent transmitted to the sequential fully connected layers. The proposed approach not only takes advantage of CNNs, i.e. automatic feature extraction, but also discovers discriminative feature maps in both, spatial and temporal dimensions and preserves the growth dynamics of crop cycles. An overall accuracy of 91.3% and a kappa coefficient of 89.9% confirm the proposed method's potential. It is also shown that the suggested approach outperforms other methods.
7 citations
TL;DR: In this article , a multi-feature ensemble classifier was proposed to identify the cultivated products (wheat, barley, alfalfa, and rapeseed) in Shahriar's farmlands before cropping season ends using time-series of Sentinel-2 satellite images.
7 citations
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TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
Abstract: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.
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