as that 
ut by 
istance 
1e Sun 
2d with 
1 cloud 
'e. 
proper 
Tassel 
zy area 
greater 
011). 
(1) 
d that 
NIR-2, 
nt that 
1g one. 
in the 
nalized 
yed by 
ue and 
ulated, 
Haze 
(2) 
nm) of 
al to be 
"NIR-2 
ing the 
ehazed 
xels of 
nearest 
m first 
hen, it 
tion 1, 
tion 2, 
jn. For 
| 2 and 
  
M-4,N-3 | M-4,14-3 | M-EN-2 | NIHLN-1 | M-4 HD | MIU NHE | NIHA,MT2 | MÁS. | Md td 
  
  
  
  
  
  
  
  
  
  
  
  
  
    
  
M-2,N-4 una - - - am ae - M-3, PHA 
M-2,N-4 ^ M-2,N-2 | M-2,N-1 | M-2,N40 | M-2,N*1 | M-2,8*2 
M-1 N44 - M-1,N-2 PR - - M-1,H+2 
MHO PS - M+0, 1-2 E BLM - WHE, HAZ 
M+1,N-£ vos M+1,N-2 - - m Mel fa ve AH, ME 
M+2.A+4 a MIE2,N-2 | INH2,PI-3 | M+2,N+0 | M+2,$H1 | M+2,M+2 wor M+2.N+4 
M43, Hed pa - - - M+3,N+4 
M+EN+4 | M+4,4-3 | M+4,N-2 | P44,N-1 | M+4,MH$ MHS N42 | MASS | MH, HH 
   
  
  
  
  
  
  
  
Figure 2. Schematic Representation of Neighborhood pixels of 
Pixel (M, N) for Iteration 2 and Iteration 4 
At each iteration, the center pixel value in PALSAR is 
compared with searching pixel value and when it meets, the 
pixel that satisfy the condition as the below equation (3) (Hoan 
and Tateishi, 2008) and it is outside of cloud/shadow area in 
optical image, the searching is ended. 
abs(DNi - DNj)) Xa (3) 
where, 
a — Threshold value 
DN: = Digital number of Center Pixel 
DN; = Digital number of Searching Pixel 
After getting the value in PALSAR, it finds the value in Optical 
satellite data (e.g. ALOS AVNIR-2) at corresponding location 
and replaces the cloud/shadow pixel. 
3.2 Land Cover Classification 
Considering that Pixel value as Top-Of-Atmosphere (TOA) 
radiance for a feature is more consistence compared to DN, the 
processing such as NDVI estimation, LC classification has been 
carried out employing this value, refer to Figure 3. LC 
classification has been carried out using Decision tree 
Classifiers followed by “Unsupervised classification 
(Clustering)” to get finally 6 classes as per IPCC requirements; 
Forest Land, Cropland, Grassland, Wetlands, Settlements, and 
Other Land (Bickel et al., 2006) were finally achieved. 
Moreover, while processing, the Forest Land was categorized 
considering the canopy cover into two; Forest (Closed) and 
Forest (Open). Also, Water body was initially extracted 
separately than other Wetlands. 
   
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
  
    
Optical Satellite / 
Image. DN 
     
Radiance Image 
TOA Reflectance Image | | NDVI ] 
   
     
   
  
     
  
    
  
| NDWVT of Non- 
| Vegetation Area 
NDVTof 
Water body 
   
      
     
Band 3 
(% reflectance) 
Threshold 
Un-supervised [ Other Non-vegetation Ares | 
Classification ici wd 
Clipping TOA Reflectance 
image with this area, 
Clustering, & recoding 
  
| Vegetation B' Area 
   
Vegetation A’ Ares 
  
  
  
  
  
  
3 
Clipping TOA Reflectance Clipping TOA Reflectance a 
image with this area, image with this area, | and | ee } 0 
Clustering, d recoding Clustering, & recoding 1 Eve | ressens) | Seulement D One s 
Aere ep 
3 Y Un-supervised 
Land Use Classes: Land Use Classes: Classification 
- Forest (Closed) - Forest (Closed) Clipping TOA Reflectance} 
+ Forest (Open) - Forest (Open) 
- Grassland (if present) || - Grassland 
- Cropland (if present) 
image with this area, 
Clustering, & recoding 
  
  
  
  
  
Y 
ar Ua Ÿ 
ee tes 
X 
Figure 3. General Workflow of LC Classification 
  
  
  
  
3.2.1 Conversion of DN to TOA Reflectance and 
Estimation of NDVI: The conversion of DN to radiance and 
then to TOA Reflectance was carried out using the equation 
mentioned in “Landsat 7 Science Data Users Handbook-Data 
Products”. And, for NDVI, its general equation was used. 
i) Conversion of DN to Radiance: The general equation for 
DN to Radiance is as follows: 
LA = Grescale * QCAL + Brescale 4) 
Where, 
LA = Spectral Radiance at the sensor's aperture 
(W/m2/sr/um) 
Grescale = Rescaled gain 
QCAL = Digital Number (DN) 
Brescale = Rescaled bias 
For Landsat, the above equation is also expressed as: 
(Luax — Lviv) 
a KT ee (OCA FOCAL am) Lus C? 
QCAL max — QCAL mn 
Where, 
QCALy 7 Minimum quantized calibrated pixel value 
OCAL yux = Maximum quantized calibrated pixel value 
Luv = Spectral radiance that is scaled to QCALMIN 
Lmax = Spectral radiance that is scaled to QCALMAX 
The Lmax. Lmmn, QCALmm; and QCALmax information from 
attached metadata file were used. During conversion, equation 
(4) was applied to ALOS AVNIR-2 and equation (5) to Landsat 
TM and Landsat ETM+. 
ii) Conversion of Radiance to TOA Reflectance: Equation 
(6) was used to convert radiance to TOA reflectance: