What is Bit Depth

Bit depth is the measure of detail in every pixel communicated in units of bits.

A 1-bit raster contains two qualities (zero and one). While an 8-bit raster ranges from 0-255 (256 qualities altogether).

A 1-bit raster gives two shades – basically high contrast – or yes and no. An 8-bit raster would give 256 shades of grey.

A wide range of values gives the ability for pixel values to discriminate very slight differences in energy. Got it? Let’s study this a bit more:

8-Bit versus 4-Bit versus 2-Bit Imagery

The 8-bit model underneath shows the shore of Tokyo in two distinctive piece profundities. Each band (red, green and blue) has 256 hues and a pixel profundity of 8. 28=256

8-Bit vs 4-Bit vs 2-Bit Imagery

And here is that same image in 4-bit with only 16 colors. 24=16

4-bit with only 16 colors. 24=16

The 4-bit the picture has less assortment in pixels like an exemplary Nintendo game. While the 8-piece picture conveys more shades in each band with 256 altogether.

You lose a lot of quality without the range of shading as seen in this 2-bit picture:

2-bit image:

Bit Depth Examples for Satellites

The exact range of digital numbers (DN) that a sensor uses relies upon its radiometric goals.

For instance:

 Landsat Multispectral Sensor (MSS) measures radiation on a 0-63 DN scale.

 Landsat Thematic Mapper (TM) measures it on a 0-255 scale.

 The Landsat-8 picture is in 16-bit radiometric goals (ranges from 0-65535).

As a trend, bit depth has increased over the years as the quality of sensors has improved.

Spectral, Spatial and Radiometric Resolution

You won’t necessarily increase the quality of an image with higher radiometric resolution.

It will result in a greater range of values for each pixel. But it also depends on the spatial and spectral resolution.

A sensor should have a balance between spectral, spatial, and radiometric resolution.

With finer spatial goals, less vitality starting from the earliest stage identified per pixel. Littler pixels implies ground territory diminishes. There will be less upwelling vitality back to the sensor. For this situation, you’d need to widen the frequency range to build the measure of vitality distinguished.

Every sensor has a particular target. For instance, littler spatial goals (greater pixels) repays pixel size however gets more noteworthy otherworldly and radiometric resolution.

Bit Depth and File Size

Higher radiometric resolution implies exchange offs. As you increment the pixel profundity, document size additionally increases.

 The 8-Bit Sentinel picture of Tokyo is 355 MB

 The 4-Bit Sentinel picture of Tokyo is 46 MB

 And the 2-Bit Sentinel picture of Tokyo is only 12 MB

In the event that document stockpiling is a worry, at that point think about the bit profundity in a picture. A lower scope of qualities in a picture implies less memory devoured (yet in addition less quality).

On a separate note, you can reduce the file size by choosing lossy and lossless compression methods.

Lossy pressure (like JPEG) for all time wipes out certain data (particularly repetitive data) (despite the fact that the client may not see it). Nonetheless, lossless pressure (like LZ77) holds esteems during pressure, and record size is likewise diminished.

What’s Next?

Know that you have the basic knowledge of how radiometric goals functions…

See with your own eyes exactly how much detail is in a pixel…

Take a satellite picture and convert it from 8-piece to 4-piece. In ArcGIS, select Data Management Tools > Raster > Raster Dataset > Copy Raster . Make a point to scale the raster, so pixel esteems will scale from bigger to littler piece profundity.


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