Data Science is a “long-emerging” science in a world of various emerging organizations with new ideas and problems, leading to large amounts of data requiring solutions and insight for better decision-making.
“In our world of Big Data, businesses rely on data scientists to glean insight from their large, ever-expanding, diverse data set. While many people think of data science as a profession, it’s better to think of it as a way of thinking, a way to extract insights using the scientific method.” — Bob’s Perspective.
These large datasets can include text, images, audio, or video, handled using various machine-learning algorithms and libraries. This article considers an ‘image-type’ of data called SAR data.
What is the meaning of SAR and SAR Data?
While most remote sensing professionals are familiar with passive, optical images from the US Geological Survey’s Landsat, NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS), and the European Space Agency’s Sentinel-2, another sort of data is making waves: SAR stands for Synthetic Aperture Radar.
SAR is a sort of active data gathering in which a sensor generates energy and measures how much is reflected after interacting with the Earth. While understanding optical imaging is comparable to photograph analysis, SAR data requires a distinct mindset since the signal is susceptible to surface properties such as structure and wetness (Hall, 2023).
SAR data refers to the information gathered by radar systems that use radio waves to create detailed images of objects on the Earth’s surface. SAR technology is commonly used in remote sensing, allowing for various applications such as monitoring environmental changes, detecting terrain features, and even observing the Earth’s surface through clouds and at night.
How to gather SAR Data
Synthetic Aperture Radar (SAR) gathers information through a process involving the transmission and reception of radio waves. Here’s a simplified overview of how it works:
- Transmitting Radar Signals: SAR systems are mounted on satellites, aircraft, or other platforms. They emit microwave radar signals towards the Earth’s surface.
- Signal Interaction with the Surface: These radar signals interact with the objects and features on the Earth’s surface. Some signals are reflected back to the SAR antenna, while others may be scattered or absorbed.
- Receiving Radar Returns: The SAR antenna collects the radar signals that are reflected back. The time it takes for the signals to return and the amplitude and phase of these returned signals contain information about the surface properties.
- Multiple Measurements: SAR systems collect a series of radar measurements as the platform moves along its path. These measurements are typically taken from slightly different positions and angles.
- Data Processing: The collected radar data is processed using complex algorithms. By analyzing the phase and amplitude of the radar returns from different angles and positions, a high-resolution, detailed image of the Earth’s surface is generated. This is known as a SAR image.
- Image Formation: The SAR image provides information about the topography, vegetation, buildings, and other surface features. It can even penetrate clouds and provide data day or night.
In summary, SAR data is gathered by emitting radar signals and measuring their interaction with the Earth’s surface. The data is then processed to create detailed images used for various applications in remote sensing and Earth observation.
The Usefulness of SAR Data
Synthetic Aperture Radar (SAR) data has a wide range of applications across various fields due to its ability to provide all-weather, day-and-night imaging capabilities with high spatial resolution (“Synthetic Aperture Radar (SAR) Data Applications,” 2022). Here are some of the key applications of SAR data:
- Earth observation
- Object detection and recognition
- Change detection
- Interference mitigation, etc.
Conclusion: The use of SAR data imagery cannot be overemphasized, as it has roots in security, agriculture, and the environment. However, there is a need for state-of-the-art and robust machine learning algorithms to classify these images.