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Open accessJournal ArticleDOI: 10.3390/RS13050956

Outlier detection at the parcel-level in wheat and rapeseed crops using multispectral and SAR time series

04 Mar 2021-Remote Sensing (Multidisciplinary Digital Publishing Institute)-Vol. 13, Iss: 5, pp 956
Abstract: This paper studies the detection of anomalous crop development at the parcel-level based on an unsupervised outlier detection technique. The experimental validation is conducted on rapeseed and wheat parcels located in Beauce (France). The proposed methodology consists of four sequential steps: (1) preprocessing of synthetic aperture radar (SAR) and multispectral images acquired using Sentinel-1 and Sentinel-2 satellites, (2) extraction of SAR and multispectral pixel-level features, (3) computation of parcel-level features using zonal statistics and (4) outlier detection. The different types of anomalies that can affect the studied crops are analyzed and described. The different factors that can influence the outlier detection results are investigated with a particular attention devoted to the synergy between Sentinel-1 and Sentinel-2 data. Overall, the best performance is obtained when using jointly a selection of Sentinel-1 and Sentinel-2 features with the isolation forest algorithm. The selected features are co-polarized (VV) and cross-polarized (VH) backscattering coefficients for Sentinel-1 and five Vegetation Indexes for Sentinel-2 (among us, the Normalized Difference Vegetation Index and two variants of the Normalized Difference Water). When using these features with an outlier ratio of 10%, the percentage of detected true positives (i.e., crop anomalies) is equal to 94.1% for rapeseed parcels and 95.5% for wheat parcels.

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5 results found


Open accessPosted Content
Abstract: Missing data is a recurrent problem in remote sensing, mainly due to cloud coverage for multispectral images and acquisition problems. This can be a critical issue for crop monitoring, especially for applications relying on machine learning techniques, which generally assume that the feature matrix does not have missing values. This paper proposes a Gaussian Mixture Model (GMM) for the reconstruction of parcel-level features extracted from multispectral images. A robust version of the GMM is also investigated, since datasets can be contaminated by inaccurate samples or features (e.g., wrong crop type reported, inaccurate boundaries, undetected clouds, etc). Additional features extracted from Synthetic Aperture Radar (SAR) images using Sentinel-1 data are also used to provide complementary information and improve the imputations. The robust GMM investigated in this work assigns reduced weights to the outliers during the estimation of the GMM parameters, which improves the final reconstruction. These weights are computed at each step of an Expectation-Maximization (EM) algorithm by using outlier scores provided by the isolation forest algorithm. Experimental validation is conducted on rapeseed and wheat parcels located in the Beauce region (France). Overall, we show that the GMM imputation method outperforms other reconstruction strategies. A mean absolute error (MAE) of 0.013 (resp. 0.019) is obtained for the imputation of the median Normalized Difference Index (NDVI) of the rapeseed (resp. wheat) parcels. Other indicators (e.g., Normalized Difference Water Index) and statistics (for instance the interquartile range, which captures heterogeneity among the parcel indicator) are reconstructed at the same time with good accuracy. In a dataset contaminated by irrelevant samples, using the robust GMM is recommended since the standard GMM imputation can lead to inaccurate imputed values.

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Topics: Imputation (statistics) (55%), Missing data (53%), Outlier (52%) ... read more


Open accessJournal ArticleDOI: 10.3390/AGRICULTURE11111083
02 Nov 2021-Agriculture
Abstract: Smart agriculture technologies are effective instruments for increasing farm sustainability and production. They generate many spatial, temporal, and time-series data streams that, when analysed, can reveal several issues on farm productivity and efficiency. In this context, the detection of anomalies can help in the identification of observations that deviate from the norm. This paper proposes an adaptation of an ensemble anomaly detector called enhanced locally selective combination in parallel outlier ensembles (ELSCP). On this basis, we define an unsupervised data-driven methodology for smart-farming temporal data that is applied in two case studies. The first considers harvest data including combine-harvester Global Positioning System (GPS) traces. The second is dedicated to crop data where we study the link between crop state (damaged or not) and detected anomalies. Our experiments show that our methodology achieved interesting performance with Area Under the Curve of Precision-Recall (AUCPR) score of 0.972 in the combine-harvester dataset, which is 58.7% better than that of the second-best approach. In the crop dataset, our analysis showed that 30% of the detected anomalies could be directly linked to crop damage. Therefore, anomaly detection could be integrated in the decision process of farm operators to improve harvesting efficiency and crop health.

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Open accessJournal ArticleDOI: 10.1515/GEO-2020-0272
Lingyu Wang1, Quan Chen1, Zhongfa Zhou1, Xin Zhao1  +6 moreInstitutions (2)
01 Jan 2021-Open Geosciences
Topics: Karst (53%)

Open accessJournal ArticleDOI: 10.1016/J.JAG.2021.102535
Abstract: To feed the world increasing population, expansion in the area under arable cultivation is expected, with the majority projected to occur in Sub-Sahara Africa and Latin American countries. However, many existing Precision Agriculture (PA) techniques are difficult to transfer to agricultural systems in these regions as they rely on prohibitively expensive crop monitoring systems. Satellite Earth Observation (EO) has the ability to provide affordable solutions, particularly to identify yield-limiting conditions within site-specific management zones (MZs). This paper presents the Earth Observation-based Anomaly Detection (EOAD) approach, a novel system for the detection of in-field anomalies through automatic thresholding of optical Vegetation Index data, based on their deviation from a normal distribution. The EOAD sets dynamic thresholds for the pixel values within a parcel by removing the atypical values in increments from the tails towards the median until the distribution is normal. The distribution normality is assessed based upon measures of skewness and kurtosis for each iteration. The anomaly detection approach demonstrated a strong agreement, 80% overall accuracy, with identified in-field anomalies when applied to rice plots in the Ibague Plateau, Colombia, using both Sentinel-2 and PlanetScope imagery. Areas identified as anomalous during the booting stage were shown to be significantly (p ⩽ 0.005) associated with a decrease in final yield. Additionally, the percentage of anomalies detected with the EOAD improved the detection of underperforming plots in early growth stages. Using freely available data and software, this automated approach demonstrates an exciting potential for use in improving agricultural practices in low-resource regions.

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Topics: Anomaly detection (53%), Population (52%), Earth observation (50%) ... read more
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55 results found


Open accessJournal Article
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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33,540 Citations


Journal ArticleDOI: 10.1145/1541880.1541882
Abstract: Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.

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Topics: Anomaly detection (56%), Local outlier factor (52%)

7,894 Citations


Open accessJournal ArticleDOI: 10.1038/S41592-019-0686-2
Pauli Virtanen1, Ralf Gommers, Travis E. Oliphant, Matt Haberland2  +33 moreInstitutions (15)
03 Feb 2020-Nature Methods
Abstract: SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.

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6,244 Citations


Journal ArticleDOI: 10.1016/S0034-4257(96)00067-3
Bo-cai Gao1Institutions (1)
Abstract: The normalized difference vegetation index (NDVI) has been widely used for remote sensing of vegetation for many years. This index uses radiances or reflectances from a red channel around 0.66 μm and a near-IR channel around 0.86 μm. The red channel is located in the strong chlorophyll absorption region, while the near-IR channel is located in the high reflectance plateau of vegetation canopies. The two channels sense very different depths through vegetation canopies. In this article, another index, namely, the normalized difference water index (NDWI), is proposed for remote sensing of vegetation liquid water from space. NDWI is defined as (ϱ(0.86 μm) − ϱ(1.24 μm))(ϱ(0.86 μm) + ϱ(1.24 μm)), where ϱ represents the radiance in reflectance units. Both the 0.86-μm and the 1.24-μm channels are located in the high reflectance plateau of vegetation canopies. They sense similar depths through vegetation canopies. Absorption by vegetation liquid water near 0.86 μm is negligible. Weak liquid absorption at 1.24 μm is present. Canopy scattering enhances the water absorption. As a result, NDWI is sensitive to changes in liquid water content of vegetation canopies. Atmospheric aerosol scattering effects in the 0.86–1.24 μm region are weak. NDWI is less sensitive to atmospheric effects than NDVI. NDWI does not remove completely the background soil reflectance effects, similar to NDVI. Because the information about vegetation canopies contained in the 1.24-μm channel is very different from that contained in the red channel near 0.66 μm, NDWI should be considered as an independent vegetation index. It is complementary to, not a substitute for NDVI. Laboratory-measured reflectance spectra of stacked green leaves, and spectral imaging data acquired with Airborne Visible Infrared Imaging Spectrometer (AVIRIS) over Jasper Ridge in California and the High Plains in northern Colorado, are used to demonstrate the usefulness of NDWI. Comparisons between NDWI and NDVI images are also given.

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3,569 Citations


Journal ArticleDOI: 10.1080/01431169608948714
Stuart K. McFeeters1Institutions (1)
Abstract: The Normalized Difference Water Index (NDWI) is a new method that has been developed to delineate open water features and enhance their presence in remotely-sensed digital imagery. The NDWI makes use of reflected near-infrared radiation and visible green light to enhance the presence of such features while eliminating the presence of soil and terrestrial vegetation features. It is suggested that the NDWI may also provide researchers with turbidity estimations of water bodies using remotely-sensed digital data.

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3,140 Citations