Ytukyg

I have an RGBN band .tif satellite image of PlanetScope which I would like to preprocess for a neural network. When I view the image in QGIS I get a nice RGB image, however when importing as a numpy array the image is very light. Some information on the image:

Type of the image :  <class 'numpy.ndarray'>



Shape of the image : (7327, 7327, 5)

Image Height 7327

Image Width 7327

Image Shape (7327, 7327, 5)

Dimension of Image 3

Image size 268424645

Maximum RGB value in this image 65535

Minimum RGB value in this image 1

The image is uint16 type. The last band (pic[:,:,5]) only shows a singular value (65535) in all instances. Hence, I think this band should be removed leaving the RGBN bands, of which the information is as follows:

Type of the image :  <class 'numpy.ndarray'>



Shape of the image : (7327, 7327, 4)

Image Height 7327

Image Width 7327

Image Shape (7327, 7327, 4)

Dimension of Image 3

Image size 214739716

Maximum RGB value in this image 19382

Minimum RGB value in this image 1

The maximum value (19382) of the RGBN image seems pretty low knowing that the range of uint16 images is 0-65535. Subsequently the function 'skimage.io.imshow(image)' shows a nearly white image. I do not understand why QGIS is able to show the image properly in real color but python does not.

The image is loaded by means of pic = skimage.io.imread("planetscope_20180502_43.tif")

I have tried scaling the image with img_scaled = pic / pic.max() and converting it to uint8 before viewing the image with img_as_ubyte(pic) without success. I view the image with skimage.io.imshow(pic).

If necessary the image can be downloaded here. I incorporate the image because somehow it seems not possible to import the image using certain packages (Tifffile for example does not work on this tif file).

The max values of the RGB channels are lower than that of the N channel:

>>> pic.max(axis=(0,1))

array([10300,  7776, 11530, 19382, 65535], dtype=uint16)

But look at the mean values of the RGB channels: they are much smaller than max/2:

>>> pic.mean(axis=(0,1))

array([  439.14001492,   593.17588875,   542.4638124 ,  3604.6826063 ,

   65535.        ])

You have a high dynamic range (HDR) image here and want to compress its high range to 8 bits for displaying. A linear scaling with the maximum value won't do as the highest peaks are an order of magnitude higher than the average image values. Plotting the histogram of the RGB values:

If you do a linear scaling with some factor that's a bit above the mean and just disregard clipping the rest (now overexposed) values you can display it to see you have valid data:

rgb = pic[..., :3].astype(np.float32) / 2000

rgb = np.clip(rgb, 0.0, 1.0)

But to get a proper image, you will need to look into what the camera response of your data is, and how these HDR images are usually compressed into 8 bits for displaying (I'm not familiar with satellite imaging).

Thank you w-m, I was able to built on that and figured it out. Since w-m already did a neat job to elaborate on the problem, I will just leave the code here that I wrote to resolve the issue:

for i in range(0,4):

    min_ = int(np.percentile(image[:,:,i],2))

    max_ = int(np.percentile(image[:,:,i],98))



    np.maximum(image[:,:,i])

    np.minimum(image[:,:,i])



    image[:,:,i] = np.interp(image[:,:,i], image[:,:,i].min(), image[:,:,i].max(), (0,255))



image_8bit_scaled = skimage.img_as_ubyte(image)