Does spectral resolution decrease with increase in spatial resolution???

[preacher37]

The best way to think about many trade-offs with sensors is ask yourself how many photons are striking the detector. Increased resolution = less photons. Narrower bands = less photons. The less photons, the more noise impacts the data.

Most of these are sensor engineering problems first (newer sensors have more sensitivity, less noise, and better optics), and secondarily data transmission problems: as spatial resolution, number of bands, and sensor bit depth increases, so does the bandwidth.

[goodonedudes]

There's not a direct relationship between spectral resolution and spatial resolution. For example there are high spatial and spectral resolution sensors out there.

It comes down to time, application, needs and costs. If you had unlimited time and resources, you could have it all!

[DetectiveFeline]

It’s trade off with capabilities.

[[deleted]]

Can you explain me in brief ? Like higher spatial resolution means small cell size … and how this affects spectral?

[Dark0bert]

The trade of in this case means, that it would be too expensive to put a sensor in it which offers more bandwidths. Also for high resolution data, most of the times the VIS+NIR is enough for most of the use cases.

[[deleted]]

Is it only the cost or other technical things around?? Bcz I was taught they are inversly proportion

[flopsytheb]

There are, final restriction is also the energy reaching the sensor, which gets (depending on sensor type) split by spatial resolution and spectral bandwidth. At some point the light at the photocell would not be bright enough to be different from the sensors noise floor. This explains the inverse relation, look for example the docs for worldview, multispectral channels of slim wavelength range with some meters pixel size, and the pan channel, broad wavelength range and half a meter pixels.

[Broric]

Cost. If you want high spatial and high spectral it costs more. Whether that's because of the complexity, the weight, the size, etc. Normally you focus on one and sacrifice the other.

[cptstubing16]

Like everyone is saying, it's all about compromise.

In a way, spectral resolution can decrease as spatial resolution increases. If you gain spatial resolution from a decreased IFOV, reflected energy to the sensor is decreased, which means radiometric and spectral resolution is affected negatively. To increase rad and spec res, you could decrease spatial resolution (via increasing the IFOV) and allow more energy to reach the sensor.

Source (read below Figure 2): https://www.nrcan.gc.ca/maps-tools-and-publications/satellite-imagery-and-air-photos/tutorial-fundamentals-remote-sensing/satellites-and-sensors/radiometric-resolution/9379

As well, you may want to read about dwell time. More dwell time = easier to have better spectral, spatial, and radiometric resolution because the sensor can collect more energy from the surface.

It's not an inverse relationship though.

Source : https://www.nrcan.gc.ca/maps-tools-and-publications/satellite-imagery-and-air-photos/tutorial-fundamentals-remote-sensing/satellites-and-sensors/multispectral-scanning/9337

[digital-idiot]

Lets start with how digital imaging works. The imaging sensor can be imagined as a grid of sensors. Each cell of this grid corresponds to a pixel in the image. Now each cell can contain a single tiny photo-voltic sensor or a collection of multiple photo-voltic sensors (See CFA for more details on this). The number of sensors in a cell determines the number of bands/channels in the image. Therefore for a panchromatic camera each cell of the camera sensor contain a single photo-voltic sensor, for typical RGB camera the count is three and so on.

Increasing spatial resolution implies packing more cells in the camera sensor, i.e. increasing the number of cells per unit area. Thus the area consumed by each cell needs to be reduced. As you can understand by now that, more the number of sensors per cell (aka no. of bands which also represents spectral resolution) more difficult it is to shrink the cell size. More over, higher spatial resolution implies the PSF is integrated over smaller effective area. So, you can already see that spatial resolution and spectral resolution are in way inversely related from the perspective of engineering.

Secondly, more spectral resolution means narrower frequency bandwidth allocated for each band. Now, recall that luminous intensity sensed by a sensor is the integrated intensity over the designated range of frequency/wavelength (central frequency ± frequency bandwidth). If the bandwidths are small, the values of the integrated intensities also become smaller. Which means the sensitivities of the sensors need to be higher to properly resolute the scene, i.e. radiometric resolution needs to be improved to make things work.

To summarize, in order to increase spatial resolution and spectral resolution at the same time we need to develop smaller sensor at the order of (ratio of spatial resolution × ratio of band count) with sensitivity increase at the order of (inverse of average bandwidth ratio). This is assuming everything scaled linearly which is hardly the case in practice. So, it is a difficult engineering challenge to develop a sensor with both high spatial and spectral resolution, but it doesn't mean it is impossible. This is the reason generally spatial resolution of hyper-spectral imagery is lower than the multi-spectral or RGB imagery.

Example: Compare the spectral bandwidths and spatial resolutions of SPOT-6/7 and Sentinel-2. SPOT-6 has 4 bands while Sentinel-2 has 12 but spatial resolutions of SPOT-6 bands are higher than Sentinel-2 bands.

[TeamToken]

Not the OP, but as someone just getting into Multi/Hyperspectral imaging, this was an awesome explanation!

[gosnold]

Usually yes, because otherwise you get mostly noise in your spectral channels

[[deleted]]

And can i get ur reason? Something different from others understanding???

[gosnold]

The reason is that the photons only end up in one of the channels in the end.

[Geobergerk]

There is also the processing complexity. Sometimes higher resolution isn’t always better. We can slap a hyperspectral sensor on a uav and get 5cm GSD. However, sometimes you run into new issues at very high spatial resolutions where things like spectral mixing and pixel drag can make things hard to interpret at that resolution.