Remote Sensing

Remote Sensing


NovaSol Remote Sensing

Remote Sensing. Hyperspectral data from the ARCHER system.
NovaSol provides turnkey imaging and navigation solutions to government and industry. We specialize in the development and fabrication of visible and near-infrared hyperspectral imaging (HSI) systems. Applications for these systems range from surveillance, tracking, agriculture, homeland defense, and environmental monitoring to search and rescue, military reconnaissance, and urban planning.

Our work in this area covers all aspects of remote sensing, including the design of custom sensors, the fabrication of navigation/stabilization systems, and the development of application software and user interfaces.

NovaSol offers a variety of products and services to meet the remote sensing needs of various organizations.


What is Remote Sensing?

Remote sensing is the technique of acquiring information about an object without actually being in contact with it. This is usually done by sensing and recording electromagnetic radiation and processing, analyzing, and applying that information.

For example, in the ARCHER system, the radiation measured is visible light (radiant energy, a portion of the electromagnetic spectrum) and near-infrared light. Depending on the particular wavelengths studied, information can be gathered about a "target" that cannot be detected and discriminated by the human eye alone.


What is Hyperspectral Imaging?

Hyperspectral imaging, also known as imaging spectroscopy, is a key element in remote sensing. It is a type of multispectral imaging that records many tens of bands of imagery at very narrow bandwidths.

Spectrometers can "see" ranges of wavelengths greater than the human eye. Depending on the target of interest, sensors can be optimized to detect specific areas of the electromagnetic spectrum, such as the shorter ultraviolet wavelengths or the longer infrared wavelengths.

The main advantage of hyperspectral is the ability to measure many, contiguous bands of wavelengths simultaneously, which provides a broader base from which to analyze a scene. Early applications of multispectral analysis were primarily from satellites, telescopes and sensors used in astronomical or earth-science and intelligence studies.

Today, the technology of the sensors has made airborne remote sensing practical for many applications, e.g., environmental mapping and monitoring, agricultural assessment, urban planning and land use management, civil engineering, disaster management, surveillance. Real-time data processing is making such imaging practical for the needs of many organizations, such as the Civil Air Patrol.

Spectral resolution refers to the width or range of each spectral band being recorded. In hyperspectral imaging, each pixel of the image records a wavelength. In addition to two spatial dimensions, the hyperspectral image contains a third dimension: radiant intensity. Measuring the energy that is reflected (or emitted) by targets over a variety of different wavelengths results in a spectral response for that object. By comparing the response patterns of different features we may be able to distinguish between them, where we might not be able to, if we only compared them at one wavelength. For example, water and vegetation may reflect somewhat similarly in the visible wavelengths but are almost always separable in the infrared. Spectral response can be quite variable, even for the same target type, and can also vary with time and location. Knowing where to "look" spectrally and understanding the factors which influence the spectral response of the features of interest are critical to correctly interpreting the interaction of electromagnetic radiation with the surface.


What are Spectral Processing Algorithms?

Interpretation and analysis is required to make use of remote sensing data to extract meaningful information from the imagery. Interpretation and analysis of remote sensing imagery involves the identification and/or measurement of various targets in an image in order to extract useful information about them. Targets in remote sensing images may be any feature or object which can be observed in an image, and have the following characteristics:

  • Targets may be a point, line, or area feature. This means that they can have any form, from a bus in a parking lot or plane on a runway, to a bridge or roadway, to a large expanse of water or a field.
  • The target must be distinguishable; it must contrast with other features around it in the image.


Much interpretation and identification of targets in remote sensing imagery is performed manually or visually, i.e. by a human interpreter. In many cases this is done using imagery displayed in a pictorial or photograph-type format, independent of what type of sensor was used to collect the data and how the data were collected. In this case we refer to the data as being in analog format.

Remote sensing images can also be represented in a computer as arrays of pixels, with each pixel corresponding to a digital number, representing the brightness level of that pixel in the image. In this case, the data are in a digital format. Visual interpretation may also be performed by examining digital imagery displayed on a computer screen. Both analog and digital imagery can be displayed as black and white (also called monochrome) images, or as color images by combining different channels or bands representing different wavelengths.

When remote sensing data are available in digital format, digital processing and analysis may be performed using a computer. Digital processing may be used to enhance data as a prelude to visual interpretation. Digital processing and analysis may also be carried out to automatically identify targets and extract information completely without manual intervention by a human interpreter. Algorithms are developed to accomplish this.

Spectral processing algorithms are strategies for analyzing data, a computable set of steps to achieve a desired result. Digital analysis is based on the manipulation of digital numbers in a computer and is thus more objective, generally resulting in more consistent results.

NovaSol has several programs dedicated to the development of intelligent algorithms to support our remote sensing programs. Click here to learn more.


microHSI as seen in Laser Focus Magazine.
 
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