CBERS simulation from SPOT and its restoration

Gerald Jean Francis Banon and Leila Maria Garcia Fonseca

Content

1 CBERS Band 4 simulation from SPOT
1.1 Along line MTF of CBERS Band 4
1.2 Along line MTF of SPOT Band 3 
1.3 Along line MTF of the simulation filter
1.4 Along line simulation filter kernel
1.5 MTF of the file effect
1.6 Along track MTF of CBERS Band 4
1.7 Along track MTF of SPOT Band 3
1.8 Along track MTF of the simulation filter
1.9 Along track simulation filter kernel
1.10 Simulation filter kernel
2 CBERS Band 4 Restoration
2.1 Along line MTF of the restoration filter
2.2 Along line restoration filter kernel
2.3 Along track MTF of the restoration filter
2.4 Along track restoration filter kernel
2.5 Restoration filter kernel
3 CBERS Band 4 Restoration within a window
3.1 Along line MTF of the attenuation filter
3.2 Along line MTF of the restoration filter
3.3 Along line restoration filter kernel
3.4 Along track MTF of the attenuation filter
3.5 Along track MTF of the restoration filter
3.6 Along track restoration filter kernel
3.7 Restoration filter kernel


1 CBERS simulation from SPOT
1.1 Along line MTF of CBERS Band 4

function c=cbers1
	f(1)=1;
	f(2)=1;
	f(3)=.98;
	f(4)=.88;
	f(5)=.70;
	f(6)=.56;
	f(7)=.42;
	f(8)=.32;
	f(9)=.28;
	f(10)=.22;
	f(11)=.18;
	f(12)=.15;
	f(13)=.12;
	f(14)=.11;
	f(15)=.085;
	f(16)=.08;
	f(17)=.075;
	f(18)=.07;
	f(19)=.065;
	f(20)=.06;
	for i=1:19
		c(i+1)=f(i+1);
		c(i+20)=f(21-i);
	end
	c(1)=f(1);

figure
x=cbers1;
plot(0:2:38,x(1:20))
xlabel('lp/mm')
title('Figure 1 - Along line MTF of CBERS Band 4')
see plot

1.2 Along line MTF of SPOT Band 3 

The MTF is gaussian with parameter sigma = 11,2906 m
(Begni & Rayssiguiar, 1983)

1/(2*19.5) 1/meter  <-->  38.5 lp/mm
     x     1/meter  <-->  38   lp/mm

38 lp/mm is chosen as half the sample frequency for the sake of
simplicity when computing later the Point Spread Function (PSF).

x=(38/38.5)/(2*19.5)
                
function s=spot1
	N=19;
	f(1)=1;
	for n=1:19
		u=(n/N)*(38/38.5)/(2*19.5);
		f(n+1)=exp(-2*(3.1416^2)*(11.2906^2)*(u^2));
	end
	for i=1:19
		s(i+1)=f(i+1);
		s(i+20)=f(21-i);
	end
	s(1)=f(1);

x=spot1;
plot(0:2:38,x(1:20))
xlabel('lp/mm')
title('Figure 2 - Along line MTF of SPOT Band 3')
see plot

1.3 Along line MTF of the simulation filter

function f=filter1
	c=cbers1;
	s=spot1;
	for i=1:39
		f(i)=c(i)/s(i);
	end

x=filter1;
plot(0:2:38,x(1:20))
xlabel('lp/mm')
title('Figure 3 - Along line MTF of the simulation filter')
see plot

1.4 Along line simulation filter kernel

x=real(fft(filter1,39));
plot(0:19,x(1:20))
xlabel('pixel')
title('Figure 4 - Along line PSF for the CBERS simulation')
see plot

function k=kernel1
	x=real(fft(filter1,39));
	k(1)=x(4);
	k(2)=x(3);
	k(3)=x(2);
	k(4)=x(1);
	k(5)=x(2);
	k(6)=x(3);
	k(7)=x(4);
	a=0;
	for i=1:7
		a=a+k(i);
	end
	for i=1:7
		k(i)=k(i)/a;
	end

kernel1

    0.0216    0.0944    0.1646    0.4391    0.1646    0.0944    0.0216   

1.5 MTF of the file effect

function s=file
	N=19;
	f(1)=1;
	for n=1:19
		u=(n/N)*(38/38.5)/(2*19.5);
		f(n+1)=sin(3.1416*u*19.5)/(3.1416*u*19.5);
	end
	for i=1:19
		s(i+1)=f(i+1);
		s(i+20)=f(21-i);
	end
	s(1)=f(1);

x=file;
plot(0:2:38,x(1:20))
xlabel('lp/mm')
title('Figure 5 - MTF of the file effect')
see plot

1.6 Along track MTF of CBERS Band 4

function f=cbers2
	b=file;
	c=cbers1;
	for i=1:39
		f(i)=b(i)*c(i);
	end

x=cbers2;
plot(0:2:38,x(1:20))
xlabel('lp/mm')
title('Figure 6 - Along track MTF of CBERS Band 4')
see plot

1.7 Along track MTF of SPOT Band 3 

The MTF is gaussian with parameter sigma = 10,3840 m
(Begni & Rayssiguiar, 1983)

function s=spot2
	N=19;
	f(1)=1;
	for n=1:19
		u=(n/N)*(38/38.5)/(2*19.5);
		f(n+1)=exp(-2*(3.1416^2)*(10.3840^2)*(u^2));
	end
	for i=1:19
		s(i+1)=f(i+1);
		s(i+20)=f(21-i);
	end
	s(1)=f(1);

x=spot2;
plot(0:2:38,x(1:20))
xlabel('lp/mm')
title('Figure 7 - Along track MTF of SPOT Band 3')
see plot

1.8 Along track MTF of the simulation filter

function f=filter2
	b=file;
	c=cbers1;
	s=spot2;
	for i=1:39
		f(i)=b(i)*c(i)/s(i);
	end

x=filter2;
plot(0:2:38,x(1:20))
xlabel('lp/mm')
title('Figure 8 - Along track MTF of the simulation filter')
see plot

1.9 Along track simulation filter kernel

x=real(fft(filter2,39));
plot(0:19,x(1:20))
xlabel('pixel')
title('Figure 9 - Along track PSF for the CBERS simulation')
see plot

function k=kernel2
	x=real(fft(filter2,39));
	k(1)=x(4);
	k(2)=x(3);
	k(3)=x(2);
	k(4)=x(1);
	k(5)=x(2);
	k(6)=x(3);
	k(7)=x(4);
	a=0;
	for i=1:7
		a=a+k(i);
	end
	for i=1:7
		k(i)=k(i)/a;
	end

kernel2

    0.0292    0.0885    0.1889    0.3868    0.1889    0.0885    0.0292

1.10 Simulation filter kernel

kernel2'*kernel1

    0.0006    0.0028    0.0048    0.0128    0.0048    0.0028    0.0006
    0.0019    0.0084    0.0146    0.0389    0.0146    0.0084    0.0019
    0.0041    0.0178    0.0311    0.0829    0.0311    0.0178    0.0041
    0.0083    0.0365    0.0637    0.1699    0.0637    0.0365    0.0083
    0.0041    0.0178    0.0311    0.0829    0.0311    0.0178    0.0041
    0.0019    0.0084    0.0146    0.0389    0.0146    0.0084    0.0019
    0.0006    0.0028    0.0048    0.0128    0.0048    0.0028    0.0006

2 CBERS Band 4 Restoration
2.1 Along line MTF of the restoration filter

In this secftion, the restoration objective is to return to the
SPOT Band 3 along line MTF

function r=restoration1
	c=cbers1;
	s=spot1;
	for i=1:39
		r(i)=s(i)/c(i);
	end

x=restoration1;
plot(0:2:38,x(1:20))
xlabel('lp/mm')
title('Figure 10 - Along line MTF of the restoration filter')
see plot

2.2 Along line restoration filter kernel

x=real(fft(restoration1,39));
plot(0:19,x(1:20))
xlabel('pixel')
title('Figure 11 - Along line PSF for the CBERS restoration')
see plot

function k=kernelForRestoration1
	x=real(fft(restoration1,39));
	k(1)=x(4);
	k(2)=x(3);
	k(3)=x(2);
	k(4)=x(1);
	k(5)=x(2);
	k(6)=x(3);
	k(7)=x(4);
	a=0;
	for i=1:7
		a=a+k(i);
	end
	for i=1:7
		k(i)=k(i)/a;
	end

kernelForRestoration1

    0.1907   -0.3224   -0.8181    2.8997   -0.8181   -0.3224    0.1907 

2.3 Along track MTF of the restoration filter

In this section, the restoration objective is to return to the
SPOT Band 3 along track MTF

function r=restoration2
	c=cbers2;
	s=spot2;
	for i=1:39
		r(i)=s(i)/c(i);
	end

x=restoration2;
plot(0:2:38,x(1:20))
xlabel('lp/mm')
title('Figure 12 - Along track MTF of the restoration filter')
see plot

2.4 Along track restoration filter kernel

x=real(fft(restoration2,39));
plot(0:19,x(1:20))
xlabel('pixel')
title('Figure 13 - Along track PSF for the CBERS restoration')
see plot

function k=kernelForRestoration2
	x=real(fft(restoration2,39));
	k(1)=x(4);
	k(2)=x(3);
	k(3)=x(2);
	k(4)=x(1);
	k(5)=x(2);
	k(6)=x(3);
	k(7)=x(4);
	a=0;
	for i=1:7
		a=a+k(i);
	end
	for i=1:7
		k(i)=k(i)/a;
	end

kernelForRestoration2

    0.1694   -0.0908   -1.5746    3.9920   -1.5746   -0.0908    0.1694

2.5 Restoration filter kernel

kernelForRestoration2'*kernelForRestoration1

    0.0323   -0.0546   -0.1386    0.4912   -0.1386   -0.0546    0.0323
   -0.0173    0.0293    0.0743   -0.2633    0.0743    0.0293   -0.0173
   -0.3002    0.5076    1.2882   -4.5660    1.2882    0.5076   -0.3002
    0.7611   -1.2870   -3.2660   11.5758   -3.2660   -1.2870    0.7611
   -0.3002    0.5076    1.2882   -4.5660    1.2882    0.5076   -0.3002
   -0.0173    0.0293    0.0743   -0.2633    0.0743    0.0293   -0.0173
    0.0323   -0.0546   -0.1386    0.4912   -0.1386   -0.0546    0.0323

3 CBERS Band 4 Restoration within a window
3.1 Along line MTF of the attenuation filter

Following Fonseca (1988, p. 97).

1/(2*19.5) 1/meter  <-->  38.5 lp/mm
     x     1/meter  <-->  11   lp/mm

11 lp/mm is chosen as the frequency at 0.5 of the along line CBERS MTF.

x=(11/38.5)/(2*19.5)
                
function w=window1
	N=19;
	f(1)=1;
	uw=(11/38.5)/(2*19.5);
	uc=1/(2*19.5);
	for n=1:19
		u=(n/N)*(38/38.5)/(2*19.5);
		if (u<=uw)
			f(n+1)=1;
		else
			f(n+1)=0.5*(1+cos(3.1416*(u-uw)/(uc-uw)));
		end
	end
	for i=1:19
		w(i+1)=f(i+1);
		w(i+20)=f(21-i);
	end
	w(1)=f(1);

x=window1;
plot(0:2:38,x(1:20))
xlabel('lp/mm')
title('Figure 14 - Along line MTF of the attenuation filter')
see plot

3.2 Along line MTF of the restoration filter

function r=restorationb1
	c=cbers1;
	s=spot1;
	w=window1;
	for i=1:39
		r(i)=s(i)*w(i)/c(i);
	end

x=restorationb1;
plot(0:2:38,x(1:20))
xlabel('lp/mm')
title('Figure 15 - Along line MTF of the restoration filter')
see plot

3.3 Along line restoration filter kernel

x=real(fft(restorationb1,39));
plot(0:19,x(1:20))
xlabel('pixel')
title('Figure 16 - Along line PSF for the CBERS restoration')
see plot

function k=kernelForRestorationb1
	x=real(fft(restorationb1,39));
	k(1)=x(4);
	k(2)=x(3);
	k(3)=x(2);
	k(4)=x(1);
	k(5)=x(2);
	k(6)=x(3);
	k(7)=x(4);
	a=0;
	for i=1:7
		a=a+k(i);
	end
	for i=1:7
		k(i)=k(i)/a;
	end

kernelForRestorationb1

    0.0536   -0.6970    0.2413    1.8043    0.2413   -0.6970    0.0536

3.4 Along track MTF of the attenuation filter

Following Fonseca (1988, p. 97).

1/(2*19.5) 1/meter  <-->  38.5 lp/mm
     x     1/meter  <-->  11   lp/mm

11 lp/mm is chosen as the frequency at 0.5 of the along track CBERS MTF.

x=(11/38.5)/(2*19.5)
                
function w=window2
	N=19;
	f(1)=1;
	uw=(11/38.5)/(2*19.5);
	uc=1/(2*19.5);
	for n=1:19
		u=(n/N)*(38/38.5)/(2*19.5);
		if (u<=uw)
			f(n+1)=1;
		else
			f(n+1)=0.5*(1+cos(3.1416*(u-uw)/(uc-uw)));
		end
	end
	for i=1:19
		w(i+1)=f(i+1);
		w(i+20)=f(21-i);
	end
	w(1)=f(1);

x=window2;
plot(0:2:38,x(1:20))
xlabel('lp/mm')
title('Figure 17 - Along track MTF of the attenuation filter')
see plot

3.5 Along track MTF of the restoration filter

function r=restorationb2
	c=cbers2;
	s=spot2;
	w=window2;
	for i=1:39
		r(i)=s(i)*w(i)/c(i);
	end

x=restorationb2;
plot(0:2:38,x(1:20))
xlabel('lp/mm')
title('Figure 18 - Along track MTF of the restoration filter')
see plot

3.6 Along track restoration filter kernel

x=real(fft(restorationb2,39));
plot(0:19,x(1:20))
xlabel('pixel')
title('Figure 19 - Along track PSF for the CBERS restoration')
see plot

function k=kernelForRestorationb2
	x=real(fft(restorationb2,39));
	k(1)=x(4);
	k(2)=x(3);
	k(3)=x(2);
	k(4)=x(1);
	k(5)=x(2);
	k(6)=x(3);
	k(7)=x(4);
	a=0;
	for i=1:7
		a=a+k(i);
	end
	for i=1:7
		k(i)=k(i)/a;
	end

kernelForRestorationb2

    0.1569   -0.8236    0.1326    2.0680    0.1326   -0.8236    0.1569

3.7 Restoration filter kernel

kernelForRestorationb2'*kernelForRestorationb1

    0.0084   -0.1094    0.0379    0.2832    0.0379   -0.1094    0.0084
   -0.0441    0.5740   -0.1987   -1.4860   -0.1987    0.5740   -0.0441
    0.0071   -0.0924    0.0320    0.2393    0.0320   -0.0924    0.0071
    0.1108   -1.4415    0.4990    3.7315    0.4990   -1.4415    0.1108
    0.0071   -0.0924    0.0320    0.2393    0.0320   -0.0924    0.0071
   -0.0441    0.5740   -0.1987   -1.4860   -0.1987    0.5740   -0.0441
    0.0084   -0.1094    0.0379    0.2832    0.0379   -0.1094    0.0084



Bibliography

@Article{Banon:1990:SIB,
  author =          "Banon, G",
  title =           "simulacao de imagens de baixa resolucao",
  journal =         "Controle & Automacao",
  year =            "1990",
  volume =          "2",
  number =          "3",
  pages =           "180-192",
  month =           "March",
  note =            "INPE-8900-PRE/899",
  annote =          "entry from dpi.inpe.br/banon/1997/12.04.09.59",
  citationkey =     "Banon:1990:SIB",
  entrytype =       "Article",
  type =            "PRE",
}

@TechReport{BanonSant:1993:DFD,
  author =          "Banon, Gerald Jean Francis and Santos, Ailton Cruz 
                     dos",
  title =           "Digital filter design for sensor simulation 
                     application to the Brazilian Remote Sensing 
                     Satellite",
  institution =     "INPE",
  year =            "1993",
  type =            "RPQ",
  address =         "Sao Jose dos campos",
  note =            "INPE-5523-RPQ/665",
  abstract =        "Neste artigo, um modelo de interacao, entre um 
                     sensor de baixa resolucao com largo campo de 
                     visada a bordo de um satelite de observacao da 
                     terra, e a superficie da terra e apresentado. O 
                     sensor simulado e obtido atraves da composicao de 
                     um algoritmo de simulacao digital por um sensor de 
                     alta resolucao e menor campo de visada. Uma nova 
                     tecnica de desenvolvimento de filtro digital e 
                     proposto para aproximar um filtro Gaussiano ideal. 
                     O filtro resultante pode ser implementado em 
                     qualquer plataforma existente de processamento de 
                     imagens. Finalmente, dois retahos de imagem, da 
                     maneira que eles seriam produzidos pelo SSR 
                     (Satelite de Sensoriamento Remoto) da MECB (Missao 
                     Espacial Completa Brasileira) a partir de uma cena 
                     LANDSAT-TM (Thematic Mapper) sao apresentadas como 
                     exemplo..",
  annote =          "entry from dpi.inpe.br/banon/1997/12.04.09.59",
  citationkey =     "BanonSant:1993:DFD",
  entrytype =       "TechReport",
}

@MastersThesis{Fonseca:1988:RII,
  author =          "Fonseca, Leila Maria Garcia",
  title =           "Restauracao e interpolacao de imagens do satelite 
                     Landsat por meio de tecnicas de projeto de filtros 
                     FIR",
  school =          "ITA",
  year =            "1988",
  address =         "Sao Jose dos Campos",
  month =           "April",
  note =            "INPE-8898-TAE/898",
  annote =          "entry from dpi.inpe.br/banon/1997/12.04.09.59",
  citationkey =     "Fonseca:1988:RII",
  entrytype =       "MastersThesis",
  program =         "DPI",
  type =            "TAE",
}

@InProceedings{FonsecaBano::DTF,
  author =          "Fonseca, Leila Maria Garcia and Banon, Gerald Jean 
                     Francis",
  title =           "Duas tecnicas de filtragem espacial para simular a 
                     resolucao espacial ao Nadir do satelite de 
                     sensoriamento remoto brasileiro",
  booktitle =       "Simposio Brasileiro de Computacao Grafica e 
                     Processamento de Imagens, 2",
  pages =           "69-76",
  month =           "April",
  note =            "INPE-8023-PRE/023",
  abstract =        "Este trabalho apresenta duas tecnicas de filtragem 
                     espacial para simular a resolucao espacial ao 
                     nadir do Satelite de Sensoriamento Remoto 
                     brasileiro (SSR). Estas duas tecnicas sao 
                     avaliadas atraves da sua aplicacao na simulacao de 
                     imagens MSS. Posteriormente, os filtros sao 
                     aplicados para simular a banda 1 so SSR a partir 
                     da banda 3 do TM. De um modo geral, os resultados 
                     mostraram-se satisfatorios.",
  annote =          "entry from dpi.inpe.br/banon/1997/12.04.09.59",
  citationkey =     "FonsecaBano::DTF",
  entrytype =       "InProceedings",
  type =            "PRE",
}

@InProceedings{FonsecaMascBano:1987:TRR,
  author =          "Fonseca, Leila Maria Garcia and Mascarenhas, 
                     Nelson Delfino d'Avila and Banon, Gerald Jean 
                     Francis",
  title =           "Tecnicas de restauracao para remostragem de 
                     imagens do satelite Landsat-5",
  booktitle =       "Simposio Brasileiro de Telecomunicacoes, 5",
  year =            "1987",
  pages =           "204-208",
  organization =    "Sociedade Brasileira de Telecomunicaoes",
  month =           "June",
  note =            "INPE-4189-PRE/1076",
  keywords =        "processamento digital e correcoes, restauracao de 
                     imagens, landsat-5, transformada de fourier, 
                     digital processing and correction, image 
                     restoration, thematic mappers (landsat)",
  abstract =        "Sao mostrados neste trabalho alguns resultados da 
                     restauracao para reamostragem de imagens do TM 
                     (Thematic Mapper), utilizando tecnicas no dominio 
                     de Fourier. A imagem restaurada e comparada a 
                     aimagem reamostrada com o interpolador de 
                     convolucao cubica. A comparacao e feita 
                     visualmente e atraves do perfil radiometrico de 
                     uma linha da imagem.",
  annote =          "entry from dpi.inpe.br/banon/1997/12.04.09.59",
  citationkey =     "FonsecaMascBano:1987:TRR",
  entrytype =       "InProceedings",
  type =            "PRE",
}

@InProceedings{MascarenhasBanoFons:1990:SPB,
  author =          "Mascarenhas, Nelson Delfini D'Avila and Banon, 
                     Gerald Jean Francis and Fonseca, Leila Maria 
                     Garcia",
  title =           "SPOT panchromatic band simulation by linear 
                     combination of multispectral bands",
  booktitle =       "Simposio Brasileiro de Sensoriamento Remoto, 6",
  year =            "1990",
  pages =           "181-187",
  organization =    "Instituto Nacional de Pesquisas Espaciais",
  note =            "INPE-5295-PRE/1658",
  keywords =        "processamento digital e correcoes, spot (satelite 
                     frances, sensores, sensors",
  abstract =        "A simulacao de uma de mais bandas de um sensor 
                     multiespectral por combinacao de outras bandas 
                     apresenta possibilidades atraentes. Por exemplo, 
                     ela poderia diminuir a taxa de dados do canal de 
                     comunicacao entre o satelite e a terra. Neste 
                     trabalho, e feita uma analise da simulacao da 
                     banda pancromatica do SPOT (degrada 
                     espacialmente), por uma combinacao linear de 
                     bandas multiespectrais. Dois metod sao usados para 
                     a combinacao linear espectral: a) minimo quadrado 
                     sem restricao e b) minimo quadrado com restricoes 
                     de igualdade. Usando o segundo metodo mostra-se 
                     que a relacao sinal-ruido da banda simulada ttende 
                     a ser melhor que a relacao sinal-ruido de cada 
                     banda multiespectal. Os resultados experimentais 
                     incluem a simulacao espacial de uma banda 
                     pancromatica degradada espacialmente, com uma 
                     resolucao de 20 m, opara comparacao com a banda 
                     simulada espectralmente. Esses resultados 
                     demostram que uma estimativa razoavekl do ponto de 
                     vista visual e numerico da banda pancromatica 
                     degradada pode ser obtida.",
  annote =          "entry from dpi.inpe.br/banon/1997/12.04.09.59",
  citationkey =     "MascarenhasBanoFons:1990:SPB",
  entrytype =       "InProceedings",
  type =            "PRE",
}

@InProceedings{MascarenhasBanoFons:1991:SAP,
  author =          "Mascarenhas, Nelson Delfino D'Avila and Banon, 
                     Gerald Jean Francis and Fonseca, Leila Maria 
                     Garcia",
  title =           "Simulation of a panchormatic band by spectral 
                     linear combination of multiespectral bands",
  booktitle =       "International Symposium on Remote Sensing of 
                     Environment,24",
  year =            "1991",
  organization =    "ERIM",
  note =            "INPE-5293-PRE/1698",
  abstract =        "The simulation of one or more bands of a 
                     multispectral sensor by combination of other bands 
                     represents an attractive possibility. In this work 
                     an anlysis of the spatially degraded SPOT 
                     panchromatic band by linear combination of the 
                     multispectral bands has been performed using least 
                     squares methods. The results include 1) a 
                     theoretical study of the signal to noise ratio of 
                     the simulated band as compared with the 
                     multispectral bands, and 2) experimental work 
                     showing a numerically and visually reasonable 
                     estimate of the pancromatic band.",
  annote =          "entry from dpi.inpe.br/banon/1997/12.04.09.59",
  citationkey =     "MascarenhasBanoFons:1991:SAP",
  entrytype =       "InProceedings",
  type =            "PRE",
}

@MastersThesis{Santos:1992:SIS,
  author =          "Santos, Ailton Cruz",
  title =           "Simulacao de Imagens de Sensores com Largo Campo 
                     de Visada a partir de Imagens de Sensores com 
                     menor Campo de Visada- O caso SSR/TM",
  school =          "Instituto Nacional de Pesquisas Espaciais",
  year =            "1992",
  address =         "Sao Jose dos Campos",
  month =           "March",
  note =            "INPE-5378-TDI/473",
  keywords =        "?????????, compo de visada, filtro, processamento 
                     de imagens, mapeador tematico (landsat), satelites 
                     landsat, computerized simulation, field of view, 
                     image enhancement, image processing, thematic 
                     mappers (landsat), adaptive filters",
  abstract =        " Com este trabalho pretende-se a simulacao da 
                     imagem bruta obtida por um sensor orbital com 
                     largo campo de visada sob um modelo aproximado 
                     para o Satelite de Sensoriamento Remoto (SSR) da 
                     Missao Espacial completa brasileira (MECB), a 
                     partir de um conjunto de imagens obtidas por um 
                     sensor com menor campo de visada. No caso, usou-se 
                     um conjunto de imagens obtidas pelo sensor 
                     TM/Landsat, construidas com controle geometrico em 
                     uma projecao cartografica e corrigidas com pontos 
                     de controle. Utilizando-se um modelo da superficie 
                     terrestre, sao determinados os pontos na 
                     superficie correspondente a projecao do centro de 
                     cada detentor (em coordenadas geodesicas-latitude 
                     e longitude). Os valores dos pixels da imagem com 
                     largo campo de visada sao calculados a partir dos 
                     valores dos pixels da imagem TM vizinhos a estes 
                     pontos. Este calculo e feito empregando-se um 
                     filtro de simulacao adaptativo. Os resultados 
                     mostram-se satisfatorios considerando-se o aspecto 
                     geometrico sob o qual foi desenvolvido o presente 
                     trabalho.",
  annote =          "entry from dpi.inpe.br/banon/1997/12.04.09.59",
  citationkey =     "Santos:1992:SIS",
  entrytype =       "MastersThesis",
  program =         "SER",
  type =            "TDI",
}