Higher resolution images for visible and near infrared bands of LANDSAT-7 ETM+ by using panchromatic band

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doi: 10.1016/So273-1177(03)00689-6

HIGHER RESOLUTION IMAGES FOR VISIBLE AND NEAR INFRARED BANDS OF LANDSAT- ETM+ BY USING PANCHROMATIC BAND Y.Oguro’, STakeuchi’, Y.Suga’, H.Ogawa’ and K.Tsuchiya* ‘Hiroshima Institute of Technology. 2-l-l Miyake, Saeki-ku, Hiroshima 731-5193, Japan ‘Hiroshima Earth Environment Information Centec 2-l-l ~Uiyake,Saeki-ku, Hiroshima 731-5193, Japan

ABSTRACT Landsat-7 ETM+ (Enhanced Thematic Mapper Plus) simultaneously acquires multispectral image data with three difberent spatial resolutions; 30 meters for Band l-5 and 7 (visible, near infrared and short wave infrared bands), 60 meters for Band 6 (thermal band) and 15 meters for Band 8 (panchromatic band). The spectral range of Band 8 is 520-900 mn and those of Bands 2,3, and 4 are 530-610,630690 and 780-900 mn respectively. Multispectral Band 2,3 and 4 data at a 15 meter spatial resolution were generated by fusing with the higher resolution Band 8 data. Four m&hods are tested i.e. HSI (Hue, Saturation, Intensity) transformation, Brovey transformation and two proposed methods which utilize the statistical features of the multispectral and panchromatic data. The result indicates that in HSI and Brovey transformations the fusion of Band 8 data has reduced the numerical values of the average and the standard deviations of the original data while the proposed methods have generated the data of almost same color tone with nearly same values of average and standard deviations of the original data.

0 2003 COSPAR. Published by Elsevier Ltd. All rights reserved.

INTRODUCTION

In the detailed study of land surface phenomena high spatial resolution multispectral image data are indispensable. Oguro et al. (2001) made a study on the monitoring of rice in Hiroshima, Japan through the analysis of data acquired with four different sensors. During the study it was realized that higher spatial resolution multispectral image data are necessary for monitoring of rice. The statistical analysis of SPOT multispectral and panchromatic data by Tsuchiya et al. (1997) suggests a possibility of utilizing panchromatic data to improve spatial resolution of multiqectml data. Suga et al. (2001) tried to make high spatial resolution multispectral image data by fusing higher spatial image data of EROS-Al into ETM+ multiqectml image data. The HSI (Hue, Satumtion, Intensity) transformation is one of the most popular methods of image data fusion. There are several reports in this category (Smith 1978, Joblore et al. 1978, Raines 1977, and Haydn 1982). The Brovey transformation is another popular image fusion method with a simple computation (Prinz et al., 1997). Recently, an image fusion based on wavelet transformation theory has become popular (Ranchin et al. 2000, and Mallat 1989). An attempt is made to find a simple and reliable method to convert 30 meter spatial resolution ETM+

multispectral image data into 15 meter spatial resolution image data through fusion of 15 meter spatial resolution panchromatic data Following four methods will be testedz HSI and Brovey lransformations and two proposed

methods developed by present authors. Adv. Space Res. Vol. 32. No. 11. pp. 2269-2274.2003 0 2003 COSPAR. PubIished by Elsevies Ltd. All rights PhtedhGreatBfitain 0273-l 177/530.00 + 0.00

reserved

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METHOD OF ANALYSIS Satellite Data There are two product generation systems for Landsat- ETM+ Level 1G data: The Landsat Product Generation System (LPGS) and the National Landsat Archive Processing System (NLAPS). These are the following differences in the products: map projection, edge trimming, gain state, scaling and TIFF header format. The difference in the pixel placement of the respective products is illustrated in Figure 1. The LPSG product has a map coordinate system on a pixel center basis while in the NLAPS product, the allocation of the pixel is to the upper left corner as are shown in Figure 1. In this study the Landsat- ETM+ Level 1G LPGS product covering Hiroshima city, Japan (Path/Row: 112/036, Observation Day: 25 May 2002) is used. Conversion of Satellite Data from Digital Numbers to Radiances The Digital Numbers (DN’s) of the image data from Landsat- ETM+ in the Level 1G product are converted back to spectral radiances (L) based on Eq. (1) (http:llltpwww.gsfc.nasa.gov/IASkandbookml, 2002).

L = {(Lmax - Lmin)/(255

- Qmin) * (DN - Qmin) + Lmin}

[W/(m*

* Sr * p m)]

(1)

where Lmin and L- are the spectral radiances for each band at the minimum and the maximum digital numbers Qmi,,and 255 respectively. LPGS Level 1G products uses 1 for Q,,,i,,while NLAPS Level 1G products uses 0 for Qmi,,.The spectral ranges, resolutions, spectral radiances and gain setting for each band of Landsat- ETM+ data listed in Table 1. The spectral filter responses of Band 2,3,4, and 8 are also shown in Figure 2.

Table 1. Spectral ranges, resolutions, spectral radiances for each band of Landsat- ETM+ Level 1G data

7.5m

30m

15m

lil 30m

1.0 w 0.8 n 2 0.6

30m

2 0.4

:

: L

30m

(a) LPGS products (b) NLAPS products Fig. 1. Upper left corner pixel alignment for 15m and 30m resolution data of LPGS versus NLAPS products.

b

0.2

._. !

0.0 4 09 600 WAVELENGTH Fig. 2.

800

1000

[nm]

Spectral filter responses Band 2,3,4 and 8.

of

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Resolution

Images

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and Near Infrared

Bands

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Image Fusion based on HSI Transformation The HSI transformation is the most well known method of image fusion, but some variations are available: (1) The HSV (Hue-Saturation-Value) model of color is defined by a hex-cone and is also called the HSB model with B for brightness (Smith 1978). (2) The HIS (hue-lightness-saturation) model of color is defined by a double hex-cone (Joblore et al., 1978). (3) The HSI model is defined by Haydn (1982). (4) The HSI model of color is defined by Raines (1977). In this study the HSI transformation and inverse HSI transformation defined by Smith (1978) are used. (HSI Transformation) I=max { R, G, B} (a) In the case of I = 0, S = 0, H=undetined. (b) In the case of If 0, S = (I - i)/I where i = min{R, G, B), r = (I-R)/(I-i), g = (I-G)/(I-i), b = (I-B)/(I-i), In the case of R=I, H= (b-g)* 7~/3, In the case of G=I, H=(2+r+b)* 7c/3, In the case of B=I, H=(4+g+r)* tc /3, In the case of H
(2)

where max{} is the function that compares signed integers and returns the largest of them. And min{} is the function that compares signed integers and returns the least of them. (Inverse HSI Transformation) (a) In the case of S = 0, R=G=B=I. (b) In the case of SZO, h=floor(3*W x ), if H=2* x then H=O, P = 1*(1-S), .Q = I*(l-S*(H-h)}, T = I*{l-S*(l-H+h)} In the case of h=O, R=I, G=T, B=P, In the case of h=l, R=Q, G=I, B=P, In the case of h=2, R=P, G=I, B=T, In the case of h=3, R=P, G=Q, B=I, In the case of h=4, R=T G=P, B=I, In the case of h=5, R=I, G=P, B=Q.

(3)

where floor(x) is the function that computes the largest integral value not greater than x. In case of LandsatETM+ spectral radiance data, the image fusion by HSI transformation is as follows. (1) choose three bands (Band 2, 3 and 4), (2) resample them to 15 meter spatial resolution, (3) represent each pixel as a point in RGB space (R=Band 4, G=Band 3, B=Band 2), (4) perform the HSI transformation, (5) replace the intensity component (I) of each pixel with the corresponding panchromatic band (I=Band 8), and (6) perform the inverse HSI transformation (R=Band 4, G=Band 3, B=Band 2).

Image Fusion based on Brovey Transformation The Brovey transformation (Prinz et al., 1997) is defined by the simple computation given in Eq.(4). This method assumes that the spectral range spanned by the panchromatic band is same as that covered by the multispectral bands. Yl,(i,j)=

x,(i,j)*X,(m,n)/

CX,(i,j) k=*

(4)

where i and j are the pixel number and the line number of k-th multispectral bands respectively. x&j) is the k-th original multispectral band data and Y&j) is the k-tb fused multispectral band data. On the other hand, m and n

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are the pixel number and the line number of panchromatic band data respectively. X,&n& is the original panchromatic band data which exist in a range of the k-th original multispectral band data of X&j). In case of Landsat- ETM+ spectral radiance data, the image fusion by Brovey transformation is as follows. (1) choose three bands (Band 2, 3 and 4), (2) resample them to 15 meter spatial resolution, and (3) perform the Brovey transformation for the resample image data. Image Fusion based on Proposed Methods Two types of image fusion .method based on the characteristics of spectral radiances of panchromatic band and those of multispectral bands are proposed: (1) It is assumed that the original spectral radiance of panchromatic band is the truth value (Method I) defined by Eq.(Sa) and (2) it is assumed that the original spectral radiances of multispectral bands are the truth values (Method II) defined by Eq. (5b).

1(54 Yk(i,j)= Xk(i,j)*Xp(m,n)lXl,(m,n)

Xi(m,n)=(l/MN

)*[E

iiX,(u,v)]

u=l

X;(m,n)=

i[A

(ln the case of method I)

v=1

k *Xk(i,jll

+ Q,

k=2 Yk(i,j)=

Al,

0,

Xk(i,j)*XX,(m,n)/X’p(m,n)

=G,V1,/

=

O,--

IiGk k=2

i

[A

k l Lmh

(h the case of method II)

(k)l

W

k=2

(P) OP = L, = [Lmax (k)Gk (k)GP = IL, Yp(m,n)=

i[A

L, L, k

(k)l (k)l l

yk

(i,j)l

4255 4255 +

0

Q mm 1 Q mim 1 rn‘

k=2

where i and j are the pixel number and the line number of k-th multispectral bands respectively. X&j) is the k-th original multispectral band data and Y&j) is the k-th fused multispectral band data. On the other hand, m and n are the pixel number and the line number of panchromatic .band data respectively. X,,(m,n) is the original panchromatic band data and Yr(m,n) is the fused panchromatic band data. Also u and v are the temporary pixel number and the temporary line number of panchromatic band data which exist in a range of the k-th original multispectral band data of X&j) respectively. M and N are the total number of pixels and the total number of lines of panchromatic band data which exist in a range of the k-th original multispectral band data of X&j) respectively. At is the amplitude value of k-th multispectral band data. 0, and 0, are the offset value of original multispectral band data and that of original panchromatic band data respectively. Gt and G,, are the gain value of the original k-th multispectral band data and that of original panchromatic band data respectively. Qmi. is the minimum digital number defined by Eq.(l). L&k) and I.&k) are the spectral radiances for the k-th multispectral band defined by Bq.( 1). L(p) and L,,,&) are those of the panchromatic band. In case of LandsatETM+ spectral radiance data, the image fusion by the proposed methods is as follows. (1) choose three bands (Band 2,3 and 4), (2) resample them to 15 meter spatial resolution, and (3) perform the Method I or Method II for the resampled image data.

Higher

Resolution

Images

for Visible

and Near Infrared

Bands

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The results of fused images in radiance units obtained from HSI transformation, Brovey transformation and two proposed methods are shown in Figure 3. The statistical information (averages, standard deviations and correlations) in radiance units of Iused images obtained from each method is also listed on Table 2. For the HSI transformation, it is found that the statistical information of fused multispectral bands is different from that of the original one and the available multispectral bands are limited. For the Brovey transformation, it is found that the statistical information of lsed multispectral bands is different from that of the original one since the original information of multispectral bands is derived from the ratio of the panchromatic band. On the other hand, the statistical information of fused multispectral bands obtained from the proposed method I is equal to the original one within the ranges of calculation accuracy since this method assumes that the statistical information of original multispectral bands is the truth-value. Moreover the statistical information of panchromatic band computed from the fused multispectral bands with the proposed method II is approximately equal to the original one although individual values of fused multispectral bands are a little different ,since this method assumes that the statistical information of original panchromatic band is the truth-value. The disadvantage of the proposed two methods is that the gain values and the offset values of the used image data are needed and the available multispectral bands are limited to the spectral filter response of panchromatic band.

(a) Original false color image

(d) Brovey transformation

(b) Original panchromatic image

(e) Proposed method I

(c) HSI transformation

(9 Proposed method II

Fig. 3. Results of fused image in radiance units obtained from each method (0 HEEIC, NASDA, USGS).

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Table 2. The statistical information in radiance units of fused image obtained from each method

CONCLUDING

REMARKS

The foregoing analyses lead to the following conclusions. In the HSI and Brovey transformations, the fusion of panchromatic band data has reduced the numerical values of the average and the standard deviations of the original multispectral band data. In the two proposed methods, the fusion of panchromatic band data have generated the data of almost the same color tone with nearly same values of average and standard deviations of the original multispectral band data. ACKNOWLEDGEMENTS

We are grateful to Professor Thomas T. Wilheit of Texas A&M University for his precious advice. REFERENCES

http://ltpwww.gsfc.nasa.gov/IAS/handbooMhandbook_toc.html, Landsat 7 science data users handbook, 2002. Haydn, R., GW.Dalke, J.Henkel, J.E.Bare, Application of the IHS color transform to the processing of multisensor data and image enhancement, Proceedings of the International Symposium on Remote Sensing of Arid and Semi-Arid Lands, Cairo, Egypt, 599-6 16, 1982. Joblore G H. and D.Greenberg, Color spaces for computer graphics, Computers Graphics, 12,20-27, 1978. Mallat S. G., A theory for multiresolution signal decomposition: the wavelet representation, IEEE Transaction on Pattern Analysis and Machine Intelligence, 11(7), 647-693, 1989. Oguro, Y., Y.Suga, S.Takeuchi, M.Ogawa, T.Konishi and K.Tsuchiya, Comparison of SAR and optical sensor data for monitoring of rice plant around Hiroshima, Ah! Space Res. 28, 1, 195-200,200 1. Prinz B., R.Wiemker, H.Spitzer, Simulation of high resolution satellite imagery from multispectral airborne scanner imagery for accuracy assessment of fusion algorithms, Proceedings of Joint Workshop of International Society for Photogrammetry and Remote Sensing WG I/l, I/3 and IV/4, 17,223-23 1, 1997. Raines G., Digital color analysis of color-ratio composite Landsat scenes, Proceedings of 11th International Erasmus Research Institute of Management Symposium on Remote Sensing of Environment, 1463-1472, 1977. Ranchin T., L.Wald, Fusion of high spatial and spectral resolution images: the ARSIS concept and its implementation, Photogrametric Engineering and Remote Sensing, 66( 1), 49-6 1,200O. Smith A.R., Color gamut transformation pairs, Computers Graphics, 12, 12-19, 1978. Suga Y., H.Ogawa, K.Ohno and S.Takeuchi, A Trial for data fusion by using EROS-Al image - combination with Terra/ASTER and Landsat-7/ETM+ image -, Proceedings of the 3 lrd Conference of Remote Sensing Society of Japan, P22,189-190,200l. (In Japanese) Tsuchiya, K. and Y.Oguro, Comparison of remotely sensed data obtained with different sensors over an arid zone, Adv. Space Res. 19,9,1383-1386, 1997. E-mail address of Y.Oguro [email protected] Manuscript received 14 October 2002; revised 16 December 2002, accepted 2 1 December 2002