[GRASS-user] low radiance values after i.atcor and i.topo.corr

[maning sambale]

On 6/19/08, Nikos Alexandris <nikos.alexandris at felis.uni-freiburg.de> wrote:
> On Wed, 2008-06-18 at 22:41 +0100, Glynn Clements wrote:
>> Glynn Clements wrote:
>>
>> > > Thanks!  Multiplying by 1000 with r.mapcalc gives better results.  Any
>> > > chance adding floating points (FCELL) to i.cluster?
>> >
>> > I had a brief look at the code[1], and cannot see any obvious reason
>> > why the values would need to be integers, so I'm assuming that it's
>> > just a legacy of the days before FP support was added.
>> >
>> > [1] Most of the code is actually in the lib/imagery/c_*.c files. That
>> > should either be made part of i.cluster (nothing else uses it), or at
>> > least split into a separate library.
>>
>> I have committed these changes (splitting the cluster code off to a
>> separate library, and changing it to use DCELL instead of CELL) to the
>> SVN trunk.
>>
>> I would appreciate it if someone who understands i.cluster could test
>> the current version.
>>
>
> I've compiled and run it yesterday. How can I tell that the changes are
> effective? Should i.cluster NOT work before the changes with FP maps?
>From my previous run of i.cluster
Very low values like this:
r.info -r tcor.lsat7_2000.toar.1
min=0.039443
max=0.442334

Gives out low separability:
means and standard deviations for 3 bands

 means     0.00   0.00   0.00
 stddev    0.06   0.00   0.00


initial means for each band

class 1     -0.05   0.00   0.00
class 2     -0.05   0.00   0.00
class 3     -0.05   0.00   0.00
class 4     -0.05   0.00   0.00
class 5     -0.04   0.00   0.00
class 6     -0.04   0.00   0.00
class 7     -0.04   0.00   0.00
class 8     -0.04   0.00   0.00
class 9     -0.03   0.00   0.00
class 10    -0.03   0.00   0.00

...
1 classes (convergence=100.0%)

class separability matrix


         1

  1      0


class means/stddev for each band


class 1 (2161)
  means    0.00   0.00   0.00
  stddev   0.00   0.00   0.00

Whereas recalculating using the same layers multiplied by 1000 with
r.mapcalc gives better results:
means and standard deviations for 3 bands

 means   316.19  82.61 -78.36
 stddev   63.13  54.24  41.51


initial means for each band

class 1    253.06  28.38 -119.87
class 2    255.64  30.59 -118.18
class 3    258.21  32.80 -116.49
class 4    260.79  35.02 -114.79
class 5    263.37  37.23 -113.10
class 6    265.94  39.44 -111.40

....

lass distribution
        172         15         20         21         41
         22         20         27         27         28
         29         36         27         37         45
         35         35         44         53         53
         52         50         57         54         45
         56         48         58         61         47
         58         59         65         50         47
         52         35         34         35         41
         41         34         27         25         27
         24         29         10         15        145

######## iteration 1 ###########
50 classes, 19.37% points stable
class distribution
         60         45         19         98         10
         18         71         13          8        112
        176          8         10         15          7
          6          9         23        124         22
          8         53         13        125        134
         10         14         10         52          8
        101         12         19         39         45
         12          4        178        119         11
          8         11         29         77         18

Can you report back your results?  I hope I dont have to do r.mapcalc
and run i.cluster from the original layer values.

cheers,
maning
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