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Used to capture and interpret Earth's surface data, Remote Sensing (RS) is a technological field connected to Computer Vision. When aligned, both optimize image processing stages and extract specific information and patterns, boosting processes that were previously time-consuming. Image: Uses of remote sensing: definitions and applications
For a long time, Remote Sensing (RS) was summarized as a system that captures images through satellites. However, the definition goes much further: images obtained through Unmanned Aerial Vehicles (UAVs) or airplanes are also classes of remote sensors, just like the first aerial photographs that were recorded from balloons. When compared, the main characteristic advantage of using satellites lies in acquiring images systematically and on a global scale. Obtaining information by remote sensing is done remotely, that is, without physical contact between the sensor and the target object. The process happens through unconventional cameras, the sensors, which detect and measure the electromagnetic radiation (EMR) that interacts with Earth's surface materials. The use of technology allows extending RS applications to multiple sectors. It is very common to use Remote Sensing (RS) data in mineral resource prospecting and agriculture, as well as for environmental monitoring of deforestation, wildfires, and oil spills at sea. Likewise, they are used to prevent risks of dam breaks, land subsidence, and the collapse of slopes and infrastructure. There are many examples in daily life and, from a deeper analysis of the theme, the existence of a constantly evolving technological universe is perceived. Countless satellites are placed in orbit every year and the integration between RS and Computer Vision is not just part of the future: it is in the present. Together, these techniques obtain and analyze Big Data like never before and further enhance applications and interpretations. Want to know more about the application of Computer Vision? See here .
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Uses of Orbital Remote Sensing and their applications
Artificial Remote Sensing satellites have sensors that capture images of the Earth's surface. Just like the moon, which is a natural satellite, these devices orbit and are positioned in such a way that they can rotate around the Earth and capture information in various formats and for different purposes. This image acquisition happens through the sensors that have the function of capturing data from the surface. The information is stored through characteristics within a pixel - the name given to the smallest possible unit that makes up an image, whether it is a photo or a frame. Images captured by sensors contain many types of information, which are specific to each one of them. Multispectral sensors in the visible and infrared range, for example, have responses that depend on the chemical composition of the material, while sensors that operate in the microwave region are associated with the textural properties of the targets, and are used for differentiating landforms and estimating biomass in forestry studies.
Understand the types of image resolution
Image resolution in Remote Sensing goes beyond common sense. There are four types of resolutions, which represent different forms of measurement and act in determining which objects will be identified in the images. The resolutions are divided into: spatial, spectral, radiometric, and temporal. The most well-known of these is spatial resolution, which refers to the pixel size. The others represent, respectively, the wavelengths of the bands, the numerical values of information levels at which the image is obtained, and the temporal frequency for the same point to be revisited by the satellite. Understanding the different resolutions of an image is one of the first important points in the construction of any RS project, as well as the different types of sensors.
Understand the difference between Multispectral Sensors and Synthetic Aperture Radar Sensors
Image: Uses of remote sensing: definitions and applications
Commonly, two types of sensors are on board satellites: Multispectral in the Optical Range and SAR (Synthetic Aperture Radar). The imaging of an object is recorded through measurements of its interaction with electromagnetic radiation (EMR). Both sensors are directly related to EMR, but are located in different intervals of the spectrum. The most well-known branch of remote sensing refers to the spectral regions of the optical range (0.45 – 2.5μm). Optical sensors use sunlight as a natural source of EMR and, because of this, are categorized as passive sensors. Their basic principle is the detection of the flow of solar EMR reflected by the surfaces of terrestrial objects. On the other hand, images obtained in the microwave spectral range (wavelength interval from 1.0 to 100 cm) are called radar images. Here, the EMR source is generated artificially, characterizing it as an active sensor, whose image acquisition processes can occur independently of sunlight. Another characteristic of this spectrum range is the relatively large sizes of its wavelengths, which allows operations even in the presence of clouds.
The importance of AI in the industry
It is important to realize that sensors applied in monitoring devices have the capacity to capture a large amount of information, which makes the compilation and understanding of this data difficult for the human mind, no matter how qualified and experienced it may be. In this sense, Artificial Intelligence (AI) algorithms are a powerhouse in the analysis of spatial big data, as they enable the identification of patterns and quantitative relationships among the data never done before, increasing the possibilities of associations and interpretations for the expert. Learn more about Artificial Intelligence and its applications through Computer Vision here . Machine Learning and Deep Learning techniques are promising for the analysis of spatial data and act as enhancers so that companies from various segments can grow in a solid and exponential way. To demonstrate the importance of Artificial Intelligence (AI), Pix Force created Pix University , a campaign built to transmit knowledge about Computer Vision to industry leaders. Continue learning: understand the DataSets present in Machine Learning here.
Fabio Caraça
Fábio Caraça is the Chief Growth Officer at Pix Force. He leads Pix Force's transformation into a scalable SaaS operation, combining strategic vision, culture, and high-impact execution.
