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  1. Hello, This is a very simple question, but I'm having trouble finding any information about this. Am I able to download the CRISM spectral library in order to perform spectral matching for spectra I have no prior knowledge about? I see that crism library data can be downloaded as a zip or tar file, but I am not sure how to handle the .tab files so that ENVI can see them as a library.
  2. Hello, I have just downloaded t0902_mrrsu_05s138_0256_1.img covering Gale crater, and just have some general questions: - Is the coverage for all mrrsu* products typically about 40% of a tile? I wasn't expecting such a large amount of missing data. - Is there any reason why each image strip seems to be individually normalised, but only in some products? For example, the IRAC product seems to have consistent values across the mosaic (I can clearly see the crater from the image), however, other products (mainly single band images, eg R440) appear to have each image strip with different ranges across the mosaic, as do the mrrif* images.
  3. Hello, I'm trying to locate mineral clusters in CRISM I/F data using a Self-Organizing Map, but ultimately, even though I normalize the data prior to running a SOM my output clusters correspond with bright and dark areas. I am working with GRASS and R, so here are my processing steps: - import CRISM data into R, each pixel contains a vector of 254 values corresponding to the I/F values in the first 254 image bands. I am now working with a matrix, of n rows (number of pixels in image) by 254. This CRISM data has previously been though the CAT pre-preprocessing pipeline. - Calculate the mean value of each pixel, so that each pixel is now a residual vector (difference of each band from the mean spectrum of the pixel) - This means that I can better compare pixels to one another, as they are not affected by differences in brightness (are not offset from one another, as they would be if I used the mean spectrum of the image). - normalize each vector component [0,1] - use this new residual matrix as input into a 50x50 SOM. - The output codebook data from the SOM I simply classify with hierarchical clustering (for the moment) so I can see basic separable regions. (This basic number of clusters I estimate using a distance/similarity matrix.) Unfortunately, despite the above steps these clusters still correspond to bright/shadowed areas, in the majority of cases. I was hoping someone might be able to offer some suggestions as to why this might be?
  4. Hello, I've created some CRISM IR summary products using the CAT and am visualising them in combination with a HiRISE image. To highlight areas with the largest spectral response, in OLINDEX for example, I have read that typically stretching is performed using zero as the minimum realistic value and the 99th percentile as the maximum. The range of the OLINDEX parameter is very small: Minimum -0.107, Maximum: 0.084, so Adjusted Min: 0, Adjusted Max: 0.043. I was wondering what the typical expected values for these parameters are. What constitutes a significant response? For example, my 99th percentile value is 0.043 but this might still be a very small response compared with other areas. Regards, Elyse
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