Please tell me how to convert a remotely sensed data to color image? ,
or please let me know how to calculate the Infrared value from the remotely sensed data?
the best approach for you is to use ENVI. this software would easily handles Satellite images.band near infrared is just alike to gray images. the value are radiance at that bands.
As the other member had said, ENVI, is an option. So is ArcGIS, GRASS, etc.
However, I mostly use MATLAB. the function imagesc does the trick. Mind it this function cannot simultaneously scale more than 3 bands. So is the case with ENVI, which would ask you to select the bands...
There is a function in MATLAB - imagesc, which will scale the values in your matrix to RGB values. If you data is made up of mutliple spectral bands, select only those bands you want to see and use the imagesc. In this case, it would be quite similar in usage to that in ENVI...!
I mostly use this, than ENVI... in my work.
Related
I am new to image processsing i want to detect crack please any one can help me.
enter image description here
From the description that you're presenting, I believe that the best way to do this is by using a binary mask with the cv2.binary_and() function.
You can do this color segmentation by using 2 thresholds that are going to be the minimum and the maximum color values for the color of the cracks.
Another sollution may be the usage of the Otsu's threshold method. This probably will generate the best values for your mask.
After the masking of the image, you'll have to try to create the contours of those cracks in the image. You can use the cv2.findContours() function. (Check this link that describes the way you can implement this function)
I am using Kinect2 with Matlab; however, the depth images shown in the video stream are much brighter than when I saved it in Matlab?
do you know the solution for this problem
Firstly, you should provide the code that you are using at the moment so we can see where you are going wrong.. this is a generic advice for posting on any forum; to provide with all your information, so others can help.
If you use the histogram to check your depth values, you will see that the image is a uint8 image with values from 0 to 255. And since the depth distances are scaled to grayscale value, the values are scaled to new values and using imshow, will not provide enough contrast.
An easy workaround for displaying images is to use any type of
histogram equalization such as
figure(1);
C= adapthisteq(A, 'clipLimit',0.02,'Distribution','rayleigh');
imshow(C);
The image will be contrast adjusted for display.
I used mat2gray and it solved the problem.
I am working on detection of dental issues using thermography. I need to separate the all the given colours in the image into separate clusters (4-7 in number) so that the high-temperature zones (seen white in the image) are seen separately, which can be followed by thresholding if need be.
I am also attaching a sample of the images I will be working on. I am looking for a suitable program to carry out the execution in MATLAB.
I've already worked on the same, the program being attached in the previous question, which gives 3 clusters only.Since I'm a beginner, I need help in establishing more clusters.
image obtained using thermal camera on which clustering is to be carried out
the closest I could get to the sort of clustering I want to carry out. here, in this image green-blue cluster and white cluster are in the same image, which i want to have in separate clusters, hence the need of more clusters
expected result after clustering and thresholding
Rather than hoping that by chance clustering does what you need, I'd rather just use the ground truth you have...
In case you haven't noticed: there is a color index to the right. That is an easy to use, ordered (this is extremely beneficial over clustering, in particular to set thresholds) easy to detect key to interpreting these images without hoping on a random generator.
Note that you will likely also need to read off the numbers that give the color scale, in order to compare images.
You can read your image file in Matlab and then convert the data from RGB format to HSL or HSV with the function rgb2hsl() or rgb2hsv().
They are two alternative representations of the RGB color model. Then you can easily make your discrimination with the value of H which is abbreviated for hue.
For more information take a look at the following link: HLS and HSV
I have a .png image that has been created from some grayscale numbers using Matlab's imagesc tool using the standard color map.
For some reason, I am unable to recover the raw data. Is there a way of recovering the raw data from the image? I tried rgb2gray which more or less worked, but if I replug the new image into imagesc, it gives me a slightly different result. Also, the pixel with the most intensity differs in both images.
So, to clarify: I would love to know, how Matlab applies the rgb colormap to the grayscale values, when using the standard colormap.
This is the image we are talking about:
http://imgur.com/qFsGrWw.png
Thank you!
No, you will not get the right data if you are using the standard colormap, or jet.
Generally, its a very bad thing to try to reverse engineer plots, as they will never contain the entirety of the information. This is true in general, but even more if you use colormaps that are do not change accordingly with the data. The amount of blue in jet is massively bigger in range than the amount of orange, or another color. The color changes are non-linear with the data changes, and this will make you miss a lot of resolution. You may know what value orange corresponds to, but blue will be a very wide range of possible values.
In short:
Triying to get data from representation of data (i.e. plots) is a terrible idea
jet is a terrible idea
I'm attempting to create a true mosaic application. At the moment I have one mosaic image, ie the one the mosaic is based on and about 4000 images from my iPhoto library that act as the image library. I have already done my research and analysed the mosaic image. I've converted it into 64x64 slices each of 8 pixels. I've calculated the average colour for each slice and assertain the r, g, b and brightness (Luminance (perceived option 1) = (0.299*R + 0.587*G + 0.114*B)) value. I have done the same for each of the image library photos.
The mosaic slices table looks like so.
slice_id, slice_image_id, slice_slice_id, slice_image_column, slice_image_row, slice_colour_hex, slice_rgb_red, slice_rgb_blue, slice_rgb_green, slice_rgb_brightness
The image library table looks like so.
upload_id, upload_file, upload_colour_hex, upload_rgb_red, upload_rgb_green, upload_rgb_blue, upload_rgb_brightness
So basically I'm reading the image slices from the slices table into PHP and then pulling out the appropriate images from the library table based on the colour hexs. My trouble is that I've been on this too long and probably had too many energy drinks so am not concentrating properly, I can't figure out the way to pick out the nearest colour neighbor if the appropriate hex code doesn't exist.
Any ideas on the perfect query?
NB: I know pulling out the slices one by one is not ideal however the mosaic is only rebuilt periodically so a sudden burst in the mysql load doesn't really bother me, however if there us a way to pull the images out all at once that would also be a massive bonus.
Update Brightness Comparisons.
With Brightness
(source: buggedcom.co.uk)
Without Brightness
(source: buggedcom.co.uk)
One way to minimize the difference between the colours (in terms of their RGB components) is you would individually minimize the difference in each component. Thus you're looking for the entry with lowest
(targetRed - rowRed)^2 + (targetGreen - rowGreen)^2 + (targetBlue - rowBlue)^2
I think that you may be better off using HSL instead of RGB as color space. Formulas to compute HSL from RGB are available on the internet (and in the linked Wikipedia article), they may give you what you need to compute the best match.