How can you handle remote sensing outliers?

Remote sensing outliers are values that deviate significantly from the expected range or distribution of a variable, such as reflectance, temperature, or elevation. They can be caused by various factors, such as sensor errors, atmospheric interference, cloud cover, shadows, or land cover changes. Outliers can affect the accuracy and reliability of remote sensing analysis and interpretation, so it is important to identify and handle them properly. In this article, you will learn some common methods and tools for dealing with remote sensing outliers in Geographic Information Systems (GIS).

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1 Identify outliers

The first step in handling outliers is to identify them. There are different ways to do this, depending on the type and scale of your data. For example, you can use descriptive statistics, such as mean, standard deviation, or quartiles, to check for values that are too high or too low compared to the rest of the data. You can also use graphical methods, such as histograms, box plots, or scatter plots, to visualize the distribution and variation of your data. Another option is to use spatial methods, such as zonal statistics, spatial autocorrelation, or hot spot analysis, to detect spatial patterns and clusters of outliers.

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Abdul Khaleq Al-Qasaily

GeoAI & Spatial Data Science Leader | 21+ Years in Geospatial Industry | International Project Manager | Global Business Development & Tech Sales Strategist | Business Mentor for Startups & Entrepreneurs
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Handling outliers in remote sensing data is essential and some strategies for dealing with it: FIRST Identify Outliers, Visual Inspection: Visualize the remote sensing data using plots, histograms, or scatterplots to identify any data points that appear unusual or unexpected. Statistical Methods: Use statistical methods such as z-score analysis, box plots, or Tukey's method to detect outliers based on their deviation from the mean or median of the dataset. Data Transformation : help stabilize the distribution of the data and improve the performance of analysis algorithms. Robust Statistical Methods: Use robust statistical methods that are less sensitive to outliers, such as robust regression or robust estimation techniques.

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2 Remove outliers

One way to handle outliers is to remove them from your data. This can be done by applying a threshold or a filter to exclude values that are beyond a certain range or criterion. For example, you can use the Clip tool in ArcGIS or the Mask tool in QGIS to remove values that are outside a predefined boundary or mask. You can also use the Raster Calculator tool in both software to apply a conditional expression to remove values that are above or below a certain value. However, removing outliers can also reduce the amount and quality of your data, so you should be careful not to lose important information or introduce bias.

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3 Replace outliers

Another way to handle outliers is to replace them with more reasonable values. This can be done by using interpolation, imputation, or smoothing techniques to estimate values based on the surrounding or related data. For example, you can use the Fill tool in ArcGIS or the Fill NoData tool in QGIS to replace missing or no data values with interpolated values from neighboring cells. You can also use the Con tool in ArcGIS or the Raster Calculator tool in QGIS to replace outliers with the mean, median, or mode of the data. Alternatively, you can use the Focal Statistics tool in ArcGIS or the Raster Smoothing tool in QGIS to replace outliers with the average, minimum, or maximum of a moving window.

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4 Adjust outliers

A third way to handle outliers is to adjust them to reduce their impact on the data. This can be done by using transformation, normalization, or standardization techniques to change the scale or distribution of the data. For example, you can use the Logarithmic tool in ArcGIS or the Logarithmic Scaling tool in QGIS to transform skewed data into a more normal distribution. You can also use the Rescale tool in ArcGIS or the Normalize tool in QGIS to normalize data into a common range, such as 0 to 1 or -1 to 1. Furthermore, you can use the Zonal tool in ArcGIS or the Z-Score tool in QGIS to standardize data by subtracting the mean and dividing by the standard deviation.

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5 Evaluate outliers

The final step in handling outliers is to evaluate them. This means to assess the effect and significance of outliers on your data and analysis. You can do this by using quality assessment and validation methods, such as error matrices, accuracy measures, or error propagation, to compare the original and modified data and to quantify the uncertainty and error introduced by outliers. You can also use sensitivity analysis, such as Monte Carlo simulation or scenario analysis, to test the robustness and reliability of your results and conclusions under different assumptions and conditions.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Clement K. J. Aidoo

• Christian • GI and EO Science • Fisheries and Watershed Management • Sustainable Cities •
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Documentation: Document all steps taken to handle outliers, including the rationale behind outlier detection and the methods used for correction or removal. This ensures transparency and reproducibility of the data processing workflow.

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Juliet Ibenegbu

Co-President African Women in GIS || GIS Specialist || ESRI YPN Ambassador || Speaker || Brand Ambassador GetKidsIntoSurvey || Researcher
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Handling remote sensing outliers involves conducting data quality assessment, preprocessing, outlier detection, filtering techniques, thresholding, data fusion, expert judgment, and error propagation analysis to effectively identify, analyze, and mitigate outliers, ensuring the reliability and accuracy of remote sensing data for various applications.

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How can you handle remote sensing outliers?

1

2

3

4

5

6

1 Identify outliers

2 Remove outliers

3 Replace outliers

4 Adjust outliers

5 Evaluate outliers

6 Here’s what else to consider

Geographic Information Systems (GIS)

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How can you handle remote sensing outliers?

1

2

3

4

5

6

1 Identify outliers

2 Remove outliers

3 Replace outliers

4 Adjust outliers

5 Evaluate outliers

6 Here’s what else to consider

Geographic Information Systems (GIS)

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