How can you design a remote sensing project that accounts for shadows?

Shadows can present a hurdle for remote sensing projects that rely on optical data, such as satellite or aerial imagery, as they can reduce the contrast, visibility, and accuracy of the features of interest, particularly in urban or mountainous areas. Fortunately, there are some strategies to design a remote sensing project that takes shadows into account and minimizes their effect on your analysis. This article will explain how to pick the right data sources and parameters, apply pre-processing techniques to enhance the image quality, use shadow detection and correction methods to reduce bias and errors, and incorporate ancillary data and information to improve the interpretation and validation.

1 Data sources and parameters

When designing a remote sensing project that accounts for shadows, the first step is to select the appropriate data sources and parameters that are suited to your objectives and area of interest. Factors like the spatial, spectral, temporal, and radiometric resolution of the data, as well as the sun angle, azimuth, and elevation at the time of acquisition should be taken into account. Additionally, you should consider cloud cover, atmospheric conditions, seasonality, availability, accessibility, and cost of the data. To ensure sufficient detail, contrast, and coverage of your features of interest while avoiding excessive shadows or noise, you should aim to choose data sources and parameters that provide high-resolution imagery with multiple bands acquired at low sun angles or near-nadir angles during clear and dry seasons.

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Kashan Muneer

Digital Solutions Architect | WordPress & Front End Developer | GIS Specialist | Social Media Strategist
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Design a remote sensing project that accounts for shadows by scheduling data collection during specific times when shadows are minimized, such as mid-morning or mid-afternoon when the sun is at a higher angle. Use multi-temporal satellite imagery to capture the area at different times and dates, reducing the impact of shadows on any single image. Leverage advanced remote sensing techniques and algorithms designed to detect and correct shadow effects in the imagery. Additionally, combine optical and radar data, as radar sensors are less affected by shadows. Regularly validate and adjust your methods to ensure accurate results, considering the unique shadow patterns of the study area and the specific goals of your remote sensing project.

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2 Pre-processing techniques

The second step in designing a remote sensing project that accounts for shadows is to apply pre-processing techniques to enhance the image quality and prepare the data for further analysis. These techniques include radiometric correction, which adjusts pixel values to account for sensor characteristics, atmospheric effects, and illumination conditions; geometric correction, which eliminates distortions, errors, and gaps in the data; and image enhancement, which modifies contrast, brightness, color, and sharpness of the image to highlight features of interest and reduce noise. You should tailor the pre-processing techniques to your data sources, parameters, and objectives. For instance, dark object subtraction, atmospheric correction, or topographic correction may be used to reduce shadows on pixel values.

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Arkadiusz Szadkowski

🧭 Shaping the Reality Capture, Reality Mapping, GIS, Imagery, and Remote sensing sectors.
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The HS-Log curve uses the levels information (highlight and shadow) and defines a logarithmic curve between these values for each channel individually. Applying it can brighten shadows without much change to global image brightness.

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3 Shadow detection and correction

The third step in designing a remote sensing project that accounts for shadows is to use shadow detection and correction methods to identify and remove the pixels affected by shadows. There are various methods for shadow detection and correction, depending on the type, complexity, and extent of the shadows. Thresholding involves setting a threshold value for the pixel values or indices to separate the shadowed pixels from the non-shadowed pixels, while classification uses supervised or unsupervised algorithms to classify the pixels into shadowed and non-shadowed classes based on spectral, spatial, or contextual information. Restoration is a more sophisticated method that involves replacing the shadowed pixels with estimated values based on neighboring pixels, ancillary data, or statistical models. You should use the shadow detection and correction methods that are effective and efficient for your data sources, parameters, and objectives; for example, decision trees, neural networks, or support vector machines may be used to detect shadows in urban areas where they are complex and variable.

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4 Ancillary data and information

The fourth step in designing a remote sensing project that accounts for shadows is to incorporate ancillary data and information to improve the interpretation and validation of your results. Ancillary data and information are additional sources of data and information that are relevant and complementary to your remote sensing data, such as digital elevation models (DEMs), land cover and land use maps, or field data and ground truthing. These sources can help you understand the topographic effects, identify features of interest, assess accuracy, and validate your results. Moreover, you should ensure that the ancillary data and information you use are reliable, relevant, and compatible with your remote sensing data. For instance, you may use DEMs to estimate the height and shape of the buildings and trees that cast shadows on the surface.

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Jon White

Technical Director @ Vantage UAV and aetha | All things drone and data!
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We have been increasingly involved with providing supplementary data and validation data for satellite systems using localised UAV flights. Multispectral, hyperspectral, thermal, and RGB imagery captured by UAV in known low/tricky coverage areas can save time and money on the number of satellite passes required for satisfactory coverage, but also may even provide coverage of existing gaps or "missing edge" data.

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5 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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Ali Oufrid

Géomètre Expert - Gérant et Fondateur de la société ELLIPSOIDE
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Pour concevoir un projet de télédétection qui prend en compte les ombres, il faut choisir le type de capteur, la résolution spatiale, la résolution spectrale, la résolution temporelle et la résolution radiométrique adaptés à l'objectif du projet. Il faut également tenir compte des facteurs qui peuvent affecter la qualité des images, comme la couverture nuageuse, l'atmosphère, le relief et l'angle solaire.

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How can you design a remote sensing project that accounts for shadows?

1

2

3

4

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1 Data sources and parameters

2 Pre-processing techniques

3 Shadow detection and correction

4 Ancillary data and information

5 Here’s what else to consider

Geographic Information Systems (GIS)

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How can you design a remote sensing project that accounts for shadows?

1

2

3

4

5

1 Data sources and parameters

2 Pre-processing techniques

3 Shadow detection and correction

4 Ancillary data and information

5 Here’s what else to consider

Geographic Information Systems (GIS)

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