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ESTARFM_FAST.pro
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ESTARFM_FAST.pro
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;---------------------------------------------------------------------------------
; estarfm program
; using two pairs of fine and coarse images
; the program can be used for whole tm scene and vi index product
; a fast version of estarfm code: improve the effeciency of original estarfm
; through controling the number of similar pixels
; developed by (1) zhu xiaolin,email: [email protected]
; department of land surveying and geo-informatics
; the hong kong polytechnic university
;
; debugging history:
; 1)5/12/2012 correct the abnormal prediction
; 2)9/29/2013 correct the spatial distance calculation for integrate window
; 3)7/10/2014 correct the abnormal value of spectral distance and use all bands to indentify background
; 4)2/13/2017 add one parameter to specify the value of pixels of background or missing
; 5)1/1/2018 improve efficiency and modified the weight for fusing vi index
; 6)3/11/2008 correct a bug in spatial distance caculation
; 7)7/27/2018 correct a bug of abnormal coversion coefficent estimation when the two input pairs are too similar
; 8)7/27/2018 improve the prediction when no enough simular pixels are selected
;
;please cite the reference: xiaolin zhu, jin chen, feng gao, & jeffrey g masek.
;an enhanced spatial and temporal adaptive reflectance fusion model for complex
;heterogeneous regions. remote sensing of environment,2010,114,2610-2623
;
; copyright belongs to xiaolin zhu
;---------------------------------------------------------------------------------
;function for open the file
pro getdata,imgdata = imgdata,ns = ns,nl = nl,nb = nb,data_type = data_type,$
filename = filename,map_info = map_info, fid = fid
filter = ['all file;*.*']
envi_open_file,filename,r_fid = fid
envi_file_query,fid,ns = ns,nl = nl,nb = nb,data_type = data_type
map_info = envi_get_map_info(fid=fid)
dims = [-1,0,ns - 1 ,0,nl - 1]
case data_type of
1:imgdata = bytarr(ns,nl,nb) ; byte byte
2:imgdata = intarr(ns,nl,nb) ; int integer
3:imgdata = lonarr(ns,nl,nb) ; long longword integer
4:imgdata = fltarr(ns,nl,nb) ; float floating point
5:imgdata = dblarr(ns,nl,nb) ; double double-precision floating
6:imgdata = complexarr(ns,nl,nb); complex, single-precision, floating-point
9:imgdata = dcomplexarr(ns,nl,nb);complex, double-precision, floating-point
12:imgdata = uintarr(ns,nl,nb) ; unsigned integer vector or array
13:imgdata = ulonarr(ns,nl,nb) ; unsigned longword integer vector or array
14:imgdata = lon64arr(ns,nl,nb) ;a 64-bit integer vector or array
15:imgdata = ulon64arr(ns,nl,nb) ;an unsigned 64-bit integer vector or array
endcase
for i = 0,nb-1 do begin
dt = envi_get_data(fid = fid,dims = dims,pos=i)
imgdata[*,*,i] = dt[*,*]
endfor
end
;-------------------------------------------------------------------
; main program
;-------------------------------------------------------------------
pro estarfm_fast
;please set the following parameters
;----------------------------------------------------------------------
w=25.0 ;set the haif window size, if 25, the window size is 25*2+1=51 fine pixels
num_class=4.0 ;set the estimated number of classes, please set a larger value if blending images with very few bands
num_similar_pixel=20 ;set number of similar pixels, a smaller value is faster but accuracy may be lower
dn_min=0 ;set the range of dn value of the image,if byte, 0 and 255
dn_max=10000.0
background=-9999 ;the value of background and missng pixels in both modis and landsat images
patch_long=400 ;set the size of each block,if process whole etm scene, set 500-1000
temp_file='f:\temp' ;set the temporary file location, temporary files will be deleted after the work
;------------------------------------------------------------------------
;open the fine image of the first pair
filename1=dialog_pickfile(title='打开第一张landsat')
;filename1 = 'f:\20170719blend\landsat6.5.dat'
;open the coase image of the first pair
filename2=dialog_pickfile(title='打开第一张modis')
;filename2 = 'f:\20170719blend\modis6.5.dat'
;open the fine image of the second pair
filename3=dialog_pickfile(title='打开第二张landsat')
;filename3 = 'f:\20170719blend\landsat7.23.dat'
;open the coase image of the second pair
filename4=dialog_pickfile(title='打开第二张modis')
;filename4 = 'f:\20170719blend\modis7.23.dat'
;open the coase image of the prediction time
filename5=dialog_pickfile(title='打开第三张modis')
;filename5 = 'f:\20170719blend\modis7.9.dat'
;open the fine image of the first pair
; filename1 = dialog_pickfile(title = 'open the fine image of the first pair:')
envi_open_file,filename1,r_fid=fid
envi_file_query,fid,ns=ns,nl=nl,nb=nb,dims=dims
map_info = envi_get_map_info(fid=fid)
orig_ns=ns
orig_nl=nl
n_ns=ceil(float(ns)/patch_long)
n_nl=ceil(float(nl)/patch_long)
ind_patch=intarr(4,n_ns*n_nl)
for i_ns=0,n_ns-1,1 do begin
for i_nl=0,n_nl-1,1 do begin
ind_patch[0,n_ns*i_nl+i_ns]=i_ns*patch_long
ind_patch[1,n_ns*i_nl+i_ns]=min([ns-1,(i_ns+1)*patch_long-1])
ind_patch[2,n_ns*i_nl+i_ns]=i_nl*patch_long
ind_patch[3,n_ns*i_nl+i_ns]=min([nl-1,(i_nl+1)*patch_long-1])
endfor
endfor
tempoutname=temp_file+'\temp_f1'
pos=indgen(nb)
for isub=0,n_ns*n_nl-1,1 do begin
dims=[-1,ind_patch[0,isub],ind_patch[1,isub],ind_patch[2,isub],ind_patch[3,isub]]
envi_doit, 'resize_doit', fid=fid, pos=pos, dims=dims, interp=0, rfact=[1,1], $
out_name=tempoutname+strtrim(isub+1,1), r_fid=r_fid1
envi_file_mng, id=r_fid1, /remove
endfor
envi_file_mng, id=fid, /remove
;open the coarse image of the first pair
;-----------------------------------------------------------
; filename2 = dialog_pickfile(title = 'open the coarse image of the first pair:')
envi_open_file,filename2,r_fid=fid
tempoutname=temp_file+'\temp_c1'
pos=indgen(nb)
for isub=0,n_ns*n_nl-1,1 do begin
dims=[-1,ind_patch[0,isub],ind_patch[1,isub],ind_patch[2,isub],ind_patch[3,isub]]
envi_doit, 'resize_doit', fid=fid, pos=pos, dims=dims, interp=0, rfact=[1,1], $
out_name=tempoutname+strtrim(isub+1,1), r_fid=r_fid1
envi_file_mng, id=r_fid1, /remove
endfor
envi_file_mng, id=fid, /remove
;open the fine image of the second pair
;-----------------------------------------------------------
; filename3 = dialog_pickfile(title = 'open the fine image of the second pair:')
envi_open_file,filename3,r_fid=fid
tempoutname=temp_file+'\temp_f2'
pos=indgen(nb)
for isub=0,n_ns*n_nl-1,1 do begin
dims=[-1,ind_patch[0,isub],ind_patch[1,isub],ind_patch[2,isub],ind_patch[3,isub]]
envi_doit, 'resize_doit', fid=fid, pos=pos, dims=dims, interp=0, rfact=[1,1], $
out_name=tempoutname+strtrim(isub+1,1), r_fid=r_fid1
envi_file_mng, id=r_fid1, /remove
endfor
envi_file_mng, id=fid, /remove
;open the coarse image of the second pair
;-----------------------------------------------------------
; filename4 = dialog_pickfile(title = 'open the coarse image of the second pair:')
envi_open_file,filename4,r_fid=fid
tempoutname=temp_file+'\temp_c2'
pos=indgen(nb)
for isub=0,n_ns*n_nl-1,1 do begin
dims=[-1,ind_patch[0,isub],ind_patch[1,isub],ind_patch[2,isub],ind_patch[3,isub]]
envi_doit, 'resize_doit', fid=fid, pos=pos, dims=dims, interp=0, rfact=[1,1], $
out_name=tempoutname+strtrim(isub+1,1), r_fid=r_fid1
envi_file_mng, id=r_fid1, /remove
endfor
envi_file_mng, id=fid, /remove
;open the coarse image of the prediction time
;-----------------------------------------------------------
; filename5 = dialog_pickfile(title = 'open the coarse image of the prediction time:')
envi_open_file,filename5,r_fid=fid
tempoutname=temp_file+'\temp_c0'
pos=indgen(nb)
for isub=0,n_ns*n_nl-1,1 do begin
dims=[-1,ind_patch[0,isub],ind_patch[1,isub],ind_patch[2,isub],ind_patch[3,isub]]
envi_doit, 'resize_doit', fid=fid, pos=pos, dims=dims, interp=0, rfact=[1,1], $
out_name=tempoutname+strtrim(isub+1,1), r_fid=r_fid1
envi_file_mng, id=r_fid1, /remove
endfor
envi_file_mng, id=fid, /remove
;------------------------------------------------------------------
;process each block
;-------------------------------------------------------------------
t0=systime(1) ;the initial time of program running
print,'there are total',n_ns*n_nl,' blocks'
for isub=0,n_ns*n_nl-1,1 do begin
;open each block image
filename = temp_file+'\temp_f1'
getdata,imgdata=fine1,ns = ns,nl = nl,nb = nb,data_type = data_type,filename = filename+strtrim(isub+1,1),fid = fid1
fine1=float(fine1)
filename = temp_file+'\temp_c1'
getdata,imgdata=coarse1,filename = filename+strtrim(isub+1,1),fid = fid2
coarse1=float(coarse1)
filename = temp_file+'\temp_f2'
getdata,imgdata=fine2,filename = filename+strtrim(isub+1,1),fid = fid3
fine2=float(fine2)
filename = temp_file+'\temp_c2'
getdata,imgdata=coarse2,filename = filename+strtrim(isub+1,1),fid = fid4
coarse2=float(coarse2)
filename = temp_file+'\temp_c0'
getdata,imgdata=coarse0,filename = filename+strtrim(isub+1,1),fid = fid5
coarse0=float(coarse0)
fine0=fltarr(ns,nl,nb) ;place the blended result
;row index of images
row_index=intarr(ns,nl)
for i=0,nl-1,1 do begin
row_index[*,i]=i
endfor
;column index of images
col_index=intarr(ns,nl)
for i=0,ns-1,1 do begin
col_index[i,*]=i
endfor
;compute the uncertainty,0.2% of each band is uncertain
uncertain=(dn_max*0.002)*(2^0.5)
similar_th=fltarr(nb,2) ;compute the threshold of similar pixel seeking
for iband=0,nb-1,1 do begin
similar_th[iband,0]=stddev(fine1[*,*,iband])*2.0/num_class ;pair 1
similar_th[iband,1]=stddev(fine2[*,*,iband])*2.0/num_class ;pair 2
endfor
;compute the distance of each pixel in the window with the target pixel (integrate window)
d_d_all=1.0+((w-indgen(w*2+1)#(intarr(1,w*2+1)+1))^2+(w-(intarr(w*2+1)+1)#indgen(1,w*2+1))^2)^0.5/float(w)
;find interaction of valid pixels of all input images: exclude missing pixels and background
valid_index=bytarr(ns,nl)
ind_valid=where(fine1[*,*,0] ne background and fine2[*,*,0] ne background and coarse1[*,*,0] ne background $
and coarse2[*,*,0] ne background and coarse0[*,*,0] ne background,num_valid)
if (num_valid gt 0) then valid_index[ind_valid]=1 ;mark good pixels in all images
for j=0,nl-1,1 do begin ;retieve each target pixel
for i=0,ns-1,1 do begin
if (valid_index[i,j] eq 1) then begin ;do not process the background
ai=max([0,i-w]) ; the window location
bi=min([ns-1,i+w])
aj=max([0,j-w])
bj=min([nl-1,j+w])
ind_wind_valid=where(valid_index[ai:bi,aj:bj] eq 1)
position_cand=intarr((bi-ai+1)*(bj-aj+1))+1 ;place the location of each similar pixel
similar_cand=fltarr((bi-ai+1)*(bj-aj+1)) ;pleace the similarity measure between each pixel and the target pixel
row_wind=row_index[ai:bi,aj:bj]
col_wind=col_index[ai:bi,aj:bj]
;searching for similar pixels
for ipair=0,1,1 do begin
for iband=0,nb-1,1 do begin
cand_band=intarr((bi-ai+1)*(bj-aj+1))
case ipair of
0:s_s=abs(fine1[ai:bi,aj:bj,iband]-fine1[i,j,iband])
1:s_s=abs(fine2[ai:bi,aj:bj,iband]-fine2[i,j,iband])
endcase
similar_cand=similar_cand+s_s/(similar_th[iband,ipair]+0.00000001)
ind_cand=where(s_s lt similar_th[iband,ipair])
cand_band[ind_cand]=1
position_cand=position_cand*cand_band
endfor
endfor
cand_band=0
indcand0=where(position_cand ne 0 and valid_index[ai:bi,aj:bj] eq 1,number_cand0) ;select similar pixel initially
order_dis=sort(similar_cand[indcand0])
number_cand=min([number_cand0,num_similar_pixel])
indcand=indcand0[order_dis[0:number_cand-1]] ; select the n most similar samples
if (number_cand gt 5) then begin
s_d_cand=fltarr(number_cand) ;compute the correlation
x_cand=col_wind[indcand]
y_cand=row_wind[indcand]
finecand=fltarr(number_cand,nb*2)
coasecand=fltarr(number_cand,nb*2)
for ib=0,nb-1, 1 do begin
finecand[*,ib]=(fine1[ai:bi,aj:bj,ib])[indcand]
finecand[*,ib+nb]=(fine2[ai:bi,aj:bj,ib])[indcand]
coasecand[*,ib]=(coarse1[ai:bi,aj:bj,ib])[indcand]
coasecand[*,ib+nb]=(coarse2[ai:bi,aj:bj,ib])[indcand]
endfor
if (nb eq 1) then begin ; for images with one band, like ndvi
s_d_cand=1.0-0.5*(abs((finecand[*,0]-coasecand[*,0])/(finecand[*,0]+coasecand[*,0]))+abs((finecand[*,1]-coasecand[*,1])/(finecand[*,1]+coasecand[*,1])))
endif else begin
; for images with multiple bands
sdx=stddev(finecand,dimension=2)
sdy=stddev(coasecand,dimension=2)
meanx=mean(finecand,dimension=2)
meany=mean(coasecand,dimension=2)
x_meanx=fltarr(number_cand,nb*2)
y_meany=fltarr(number_cand,nb*2)
for ib=0,nb*2-1, 1 do begin
x_meanx[*,ib]=finecand[*,ib]-meanx
y_meany[*,ib]=coasecand[*,ib]-meany
endfor
s_d_cand=nb*2.0*mean(x_meanx*y_meany,dimension=2)/(sdx*sdy)/(nb*2.0-1)
endelse
ind_nan=where(s_d_cand ne s_d_cand,num_nan)
if (num_nan gt 0) then s_d_cand[ind_nan]=0.5 ;correct the nan value of correlation
d_d_cand=fltarr(number_cand) ;spatial distance
if ((bi-ai+1)*(bj-aj+1) lt (w*2.0+1)*(w*2.0+1)) then begin ;not an integrate window
d_d_cand=1.0+((i-x_cand)^2+(j-y_cand)^2)^0.5/float(w)
endif else begin
d_d_cand[0:number_cand-1]=d_d_all[indcand] ;integrate window
endelse
c_d=(1.0-s_d_cand)*d_d_cand+0.0000001 ;combined distance
weight=(1.0/c_d)/total(1.0/c_d)
for iband=0,nb-1,1 do begin ;compute v
fine_cand=[(fine1[ai:bi,aj:bj,iband])[indcand],(fine2[ai:bi,aj:bj,iband])[indcand]]
corse_cand=[(coarse1[ai:bi,aj:bj,iband])[indcand],(coarse2[ai:bi,aj:bj,iband])[indcand]]
coarse_change=abs(mean((coarse1[ai:bi,aj:bj,iband])[indcand])-mean((coarse2[ai:bi,aj:bj,iband])[indcand]))
if ( coarse_change ge dn_max*0.02) then begin ;to ensure changes in coarse image large enough to obtain the conversion coefficient
regress_result=regress(corse_cand,fine_cand,ftest=fvalue)
sig=1.0-f_pdf(fvalue,1,number_cand*2-2)
;correct the result with no significancy or inconsistent change or too large value
if (sig le 0.05 and regress_result[0] gt 0 and regress_result[0] le 5) then begin
v_cand=regress_result[0]
endif else begin
v_cand=1.0
endelse
endif else begin
v_cand=1.0
endelse
; compute the temporal weight
difc_pair1=abs(mean((coarse0[ai:bi,aj:bj,iband])[ind_wind_valid])-mean((coarse1[ai:bi,aj:bj,iband])[ind_wind_valid]))+0.01^5
difc_pair2=abs(mean((coarse0[ai:bi,aj:bj,iband])[ind_wind_valid])-mean((coarse2[ai:bi,aj:bj,iband])[ind_wind_valid]))+0.01^5
t_weight1=(1.0/difc_pair1)/(1.0/difc_pair1+1.0/difc_pair2)
t_weight2=(1.0/difc_pair2)/(1.0/difc_pair1+1.0/difc_pair2)
;predict from pair1
coase0_cand=(coarse0[ai:bi,aj:bj,iband])[indcand]
coase1_cand=(coarse1[ai:bi,aj:bj,iband])[indcand]
fine01=fine1[i,j,iband]+total(weight*v_cand*(coase0_cand-coase1_cand))
;predict from pair2
coase2_cand=(coarse2[ai:bi,aj:bj,iband])[indcand]
fine02=fine2[i,j,iband]+total(weight*v_cand*(coase0_cand-coase2_cand))
;the final prediction
fine0[i,j,iband]=t_weight1*fine01+t_weight2*fine02
;revise the abnormal prediction
if (fine0[i,j,iband] le dn_min or fine0[i,j,iband] ge dn_max) then begin
fine01=total(weight*(fine1[ai:bi,aj:bj,iband])[indcand])
fine02=total(weight*(fine2[ai:bi,aj:bj,iband])[indcand])
fine0[i,j,iband]=t_weight1*fine01+t_weight2*fine02
endif
endfor
endif else begin ;for the case of no enough similar pixel selected
for iband=0,nb-1,1 do begin
; compute the temporal weight
difc_pair1=mean((coarse0[ai:bi,aj:bj,iband])[ind_wind_valid])-mean((coarse1[ai:bi,aj:bj,iband])[ind_wind_valid])+0.01^5
difc_pair1_a=abs(difc_pair1)
difc_pair2=mean((coarse0[ai:bi,aj:bj,iband])[ind_wind_valid])-mean((coarse2[ai:bi,aj:bj,iband])[ind_wind_valid])+0.01^5
difc_pair2_a=abs(difc_pair2)
t_weight1=(1.0/difc_pair1_a)/(1.0/difc_pair1_a+1.0/difc_pair2_a)
t_weight2=(1.0/difc_pair2_a)/(1.0/difc_pair1_a+1.0/difc_pair2_a)
fine0[i,j,iband]=t_weight1*(fine1[i,j,iband]+difc_pair1)+t_weight2*(fine2[i,j,iband]+difc_pair2)
endfor
endelse
endif
endfor
endfor
; change the type of prediction into the type same as the input image
case data_type of
1:fine0 = byte(fine0) ; byte byte
2:fine0 = fix(fine0) ; int integer
3:fine0 = long(fine0) ; long longword integer
4:fine0 = float(fine0) ; float floating point
5:fine0 = double(fine0) ; double double-precision floating
6:fine0 = complex(fine0); complex, single-precision, floating-point
9:fine0 = dcomplex(fine0);complex, double-precision, floating-point
12:fine0 = uint(fine0) ; unsigned integer vector or array
13:fine0 = ulong(fine0) ; unsigned longword integer vector or array
14:fine0 = long64(fine0) ;a 64-bit integer vector or array
15:fine0a = ulong64(fine0) ;an unsigned 64-bit integer vector or array
endcase
print,'finished ',isub+1,' block'
tempoutname1=temp_file+'\temp_blended'
envi_write_envi_file,fine0,out_name = tempoutname1+strtrim(isub+1,1)
envi_file_mng, id=fid1, /remove, /delete
envi_file_mng, id=fid2, /remove, /delete
envi_file_mng, id=fid3, /remove, /delete
envi_file_mng, id=fid4, /remove, /delete
envi_file_mng, id=fid5, /remove, /delete
endfor
;;--------------------------------------------------------------------------------------
;mosiac all the blended patch
mfid=intarr(n_ns*n_nl)
mdims=intarr(5,n_ns*n_nl)
mpos=intarr(nb,n_ns*n_nl)
pos=indgen(nb)
x0=intarr(n_ns*n_nl)
y0=intarr(n_ns*n_nl)
for isub=0,n_ns*n_nl-1,1 do begin
envi_open_file, tempoutname1+strtrim(isub+1,1), r_fid= sub_fid
if (sub_fid eq -1) then begin
envi_batch_exit
return
endif
envi_file_query, sub_fid, ns=sub_ns, nl=sub_nl
mfid[isub] = sub_fid
mpos[*,isub] = indgen(nb)
mdims[*,isub] = [-1,0, sub_ns-1,0, sub_nl-1]
x0[isub]=ind_patch[0,isub]
y0[isub]=ind_patch[2,isub]
endfor
xsize = orig_ns
ysize = orig_nl
pixel_size = [1.,1.]
use_see_through = replicate(1l,n_ns*n_nl)
see_through_val = replicate(0l,n_ns*n_nl)
out_name=filename5+'_estarfm_fast'
envi_doit, 'mosaic_doit', fid=mfid, pos=mpos, $
dims=mdims, out_name=out_name, xsize=xsize, $
ysize=ysize, x0=x0, y0=y0, georef=0,map_info=map_info, $
out_dt=data_type, pixel_size=pixel_size, $
background=0, see_through_val=see_through_val, $
use_see_through=use_see_through
for i=0,n_ns*n_nl-1,1 do begin
envi_file_mng, id=mfid[i], /remove, /delete
endfor
print, 'time used:', floor((systime(1)-t0)/3600), 'h',floor(((systime(1)-t0) mod 3600)/60),'m',(systime(1)-t0) mod 60,'s'
end