5.15. Types of Remote Sensing Images

Remote sensing images are available in two broad forms: hard copy or photographic products (B &W, colour), and soft copy (digital) product. The original product from the sensor is digital data acquired in individual band as B &W image.

Remote sensing images can be categorized into two main types: hard copies and soft copies. Hard copies include traditional photographs, which can be in black and white (B&W) or color. Soft copies refer to digital images that originate from sensors, capturing data in individual bands as black and white images. This means that before we can see these images, they go through a process of digitization. The digital format allows for easier manipulation and analysis of the images using software. Human perception of color varies; for example, we may only see a limited range of gray shades in black and white images but can identify more colors in colored images.

Think of remote sensing images like cooking recipes. A hard copy is like a printed book with various recipes (hard copy), while a digital version is like having a recipe app on your phone (soft copy). Just as it's easier to search and adjust a recipe on an app, digital remote sensing images allow for easier analysis and manipulation.

A Panchromatic image consists of a B&W image taken by the sensor a slightly larger range of visible part (refer to Figure 5.32). A panchromatic image may be interpreted and analysed in a similar way as B &W aerial photograph.

Panchromatic images are high-resolution black and white images that cover a wider range of the visible spectrum compared to standard black and white photos. They are created by sensors that capture more light, making the images appear clearer. These images are similar to aerial photographs in that they can be studied and analyzed in the same way. Depending on the context, they might be used for various analyses such as mapping or land use.

Imagine taking a photo of a landscape on a cloudy day. Everything appears in shades of gray, but you can still see the outlines of trees, buildings, and roads—the same way panchromatic images work. They provide enhanced detail, much like a clear photograph taken during the day as opposed to a dimly lit one.

A multispectral image may consist of several bands of data (3-10 bands), such as from Landsat TM, where each band image has a specific utility.

Multispectral images are created using multiple spectral bands (typically 3 to 10). Each band captures light at a different wavelength, allowing for diverse data collection. These images can't be fully appreciated by the human eye through grayscale variations but are essential for software analysis. The bands are often displayed in color composites, enhancing our ability to discern various land features.

Think of multispectral imaging as the different senses we use to perceive food: sight, smell, and taste. Each one contributes to our overall experience of what we eat. Similarly, each band in a multispectral image provides unique information that, when combined, allows scientists to gain a comprehensive understanding of land use, vegetation, and other features.

In displaying a colour composite image, basically three primary colours (red, green and blue) are used. When the three primary colours are mixed in various proportions, they can produce different colours in the visible spectrum.

False Colour Composite (FCC) images use RGB (red, green, blue) color mixing to represent various features differently from the actual colors we see. For example, in a common FCC scheme for LandSat images, the near-infrared band may appear red, allowing vegetation to stand out vibrantly. This technique enables users to analyze features more effectively, revealing patterns not visible in true color.

Visualize a beautifully arranged fruit salad. If you were to photograph it in normal light, the fruits would show their true colors. However, if you were to add a colored filter when taking the photo, the fruit colors might change! An FCC image alters how we perceive the landscape, revealing details hidden in normal viewing.

In a multispectral image, when the three primary colour bands are combined in a different way, the resultant image is called a true colour composite image.

True Colour Composite (TCC) images are created when the RGB bands of a multispectral image are combined in a way that closely resembles what humans see in nature. For example, red, green, and blue bands are assigned to red, green, and blue colors respectively, creating realistic images of landscapes where vegetation is green, water is blue, and soil can be brown or yellow.

Think of TCC images like a beautifully painted landscape. If an artist captures the actual colors of a scene, the painting gives a sense of realism much like a TCC image does with the Earth’s surface. Just like a viewer can appreciate the details of the landscape in a well-done painting, scientists analyze TCC images for accurate representations of land features.

Hyperspectral image consists of more than 100 of narrower bands (10-20 ηm) where images are recorded by an imaging spectrometer.

Hyperspectral images capture data across hundreds of narrow bands (10-20 nm), which helps in detailed analysis of surface materials. This technology merges imaging and spectral analysis, allowing scientists to collect a wealth of information regarding a wide range of environmental factors such as vegetation health and mineral presence. The data collected can be represented in a data cube format, which helps in thorough analysis.

Imagine a very detailed recipe book where each recipe is written on a separate page, denoting every ingredient and step clearly. In hyperspectral imagery, each 'page' corresponds to a specific band of data detailing a unique aspect of the Earth's surface, allowing for detailed analysis of diverse subjects like vegetation health, mineral compositions, and atmospheric conditions.

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