The indices that are available by default depend on:
- The number of image groups,
- The number and the names of band(s).
When there is only one group of images:
- There is one index per band. The index name is the band name found in the camera model. For example:
- For 3 band RGB images: red, green, and blue.
- For 4 band TIFF image: band1, band2, band3, and band4.
- There is one grayscale index that combines the different bands by scaling them.
- If the images have the red and the nir bands, the NDVI index will be available. If these bands do not exist, the index is not available.
When there is more than one group of images:
- There is one index per band and per group. The index name is the group name + "_" + the band name found in the camera model. For example:
- For 2 groups of 3 bands RGB images, where the groups are called group1 and group2: group1_red, group1_green, group1_blue, group2_red, group2_green, and group2_blue.
- There is one grayscale index per group which combines the different bands of the group by scaling them. The index name is the group name + "_grayscale". For example:
- For 2 groups of 3 bands RGB images, where the groups are called group1 and group2, there are 2 grayscale indices: group1_grayscale and group2_grayscale.
- If the images have the red and the nir bands in any band, the NDVI index will be available. If these bands do not exist, the index is not available.
For example, when there is only one group of 3 bands RGB images:
| Name | Formula |
|---|---|
| red | red |
| green | green |
| blue | blue |
| grayscale | 0.2126 * red + 0.7152 * green + 0.0722 * blue
Note: The weight values depend on the camera model.
|
For example, when there is only one group of 3 bands NIRGB images:
| Name | Formula |
|---|---|
| nir | nir |
| green | green |
| blue | blue |
| grayscale |
0.7152* nir+ 0.2126* green + 0.0722 * blue
Note: The weight values depend on the camera model.
|
For example, when there is only one group of 4 bands images with nir, red, green, and blue information:
| Name | Formula |
|---|---|
| nir | nir |
| red | red |
| green | green |
| blue | blue |
| grayscale |
A*nir+ B*red + C*green + D* blue
Note: The values A, B, C and D depend on the camera model.
|
| ndvi | (nir - red) / (nir + red) |
For example, when there are n groups of 3 bands RGB images:
| Name | Formula |
|---|---|
| group_1_red | group_1_red |
| group_1_green | group_1_green |
| group_1_blue | group_1_blue |
| group_1_grayscale |
0.2126 * group_1_red + 0.7152 * group_1_green + 0.0722 * group_1_blue
Note: The weight values depend on the camera model.
|
| ... | ... |
| group_n_red | group_n_red |
| group_n_green | group_n_green |
| group_n_blue | group_n_blue |
| group_n_grayscale |
0.2126 * group_n_red + 0.7152 * group_n_green + 0.0722 * group_n_blue)
Note: The weight values depend on the camera model.
|
In the list about Pix4Dmapper indexs, I can see one grayscale index that combines the different bands by scaling them. I have some images capture with a camera Zenmuse XT2, how will be a mathematic model to generate a reflectance map with this information?
Hi, Since zenmuse XT2 has a thermal camera and a RGB camera, you will need to process them as separate projects. For the thermal camera you will get grayscale/thermal_ir (both are the same). You will need to use rjpeg images and you will get absolute temperature in the reflectance map/index map. For RGB, you will get a RGB orthomosaic and also 3 reflectance maps for red, green and blue.You will also get a grayscale index there which combines R, G, and B with weights