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CHRIS-PROBA PERFORMANCE EVALUATION: SIGNAL-TO-NOISE RATIO, INSTRUMENT EFFICIENCY AND DATA QUALITY
FROM ACQUISITIONS OVER SAN ROSSORE (ITALY) TEST SITE
Alessandro Barducci(1), Donatella Guzzi(1), Paolo Marcoionni(1), Ivan Pippi(1)
(1)National Research Council –Applied Physics Institute “Nello Carrara” via Panciatichi 64, 50127 FIRENZE, ITALY - [email protected]
ABSTRACT A methodology for evaluating CHRIS performance is presented. Starting from hyperspectral images acquired over San Rossore (Italy) test site, different issues concerning instrument response are investigated. Particular attention is devoted to the assessment of the noise (due to fully stochastic process) affecting the images and to the estimation of signal-to-noise ratio by means of an innovative algorithm developed by the authors. This procedure analyses the bit-planes extracted from any monochromatic image in the hyperspectral data cube, then it assesses the randomness of every bit-plane, and finally computes the signal-to-noise ratio for each spectral channel. Differently from more traditional signal-to-noise estimators, which need to select an homogeneous area in the concerned image to isolate noise contribution only, our algorithm is insensitive to scene texture. This circumstance allows the developed method to completely process the image of any observed ground area. The paper discusses other possible sources of systematic disturbance (like stripe-noise, smear effect, and so forth) in order to assess the overall data quality. Finally, comparisons between estimated and expected instrument efficiency are presented and discussed. 1. INTRODUCTION Nowadays a great effort is being devoted to the development of hyperspectral imagers with increased spectral, spatial and radiometric resolution for Earth remote sensing. However, these technical advantages may give rise to a lot of problems like as those related to data calibration that have great significance for the remote sensing of Earth. For example, the extended dynamic range of these sensors can be fully exploited only if the instrument response is correctly calibrated in order to avoid the effects of noise patterns and other disturbances that seriously dim the image quality. Moreover, the images acquired by hyperspectral device are generally affected by random noise, and non-periodic disturbances that are characterized by high degree of spatial and spectral coherence like the well known “stripe-noise” [1][2][3].
The amplitude of this disturbances pattern superimposed to the observed target may be quite large as much as 20-30% of the unperturbed signal [4][5][6]. To reject this kind of systematic disturbance a very often used technique is to perform a measurement of “flat-field”, i.e. illuminating the instrument entrance slit with a homogeneous and uniform radiance source. However, this measurement becomes a less practical strategy as long as the digitised accuracy and the detector’s dynamic range increase. This is due to the difficulty in finding laboratory sources whose output can be controlled with high spatial and spectral accuracy (1 part in 4095 for a 12 bit ADC). For example, satellite sensors can be influenced by aging and some types of detector require continuous re-calibration to accommodate the gain drift among the built-in read-out channels [7][8]. With particular reference to imaging spectrometer operating in “push-broom” mode like CHRIS sensor, the problems of removing systematic disturbance and estimating the image signal-to-noise ratio (SNR) are investigated in this paper. After introducing some details about CHRIS spectrometer in Section 2, outcomes from the procedure of rejection of “stripe-noise” are shown in Section 3. The description of an innovative algorithm aimed to estimate image SNR is given in Section 4 together with first results. In Section 5 conclusion and open problems are drawn with the discussion of future activities to be carried out in order to better assess CHRIS data quality. 2. IMAGING SPECTROMETER: THE CHRIS SENSOR CHRIS is a “push-broom” imaging spectrometer that was designed by SIRA Electro-Optics Ltd. (U.K.) to collect data for land investigation and aerosol measurement. CHRIS is the main instrument payload on ESA small satellite platform PROBA-1 launched on October 22, 2001. The mission is being used as a demonstrator in order to evaluate the performance of compact design technology. The knowledge derived from CHRIS-PROBA will guide the design of hyperspectral imaging systems for future missions. The main scientific CHRIS-PROBA goal is the measurement of Earth surface directional reflectance in the visible and near-infrared spectral bands using the platform pointing capability [9].
_____________________________________________________ Proc. of the 3rd ESA CHRIS/Proba Workshop, 21–23 March, ESRIN, Frascati, Italy, (ESA SP-593, June 2005)
To this aim CHRIS acquires a set of five images of the same scene during the same sun-synchronous polar orbit. Each image set has an associated “fly-by position” on the ground (roughly the image center) that corresponds to the Minimum Zenith Angle (MZA), defined as the off-nadir inclination of the sensor viewing direction in the plane perpendicular to the satellite orbit. The line of sight inclination in the along track direction (held in the orbit plane) is indicated by the Fly-by Zenith Angle (FZA). The geometrical composition of the aforementioned angles gives the true zenith angle of the sensor viewing direction. CHRIS acquires images of the same scene at FZA of +55o, +36o, 0o, -36o, and -55o during the same orbit with different spatial and spectral configurations (MODE). In fact, CHRIS collects 18, 37 or 63 spectral bands if operated at spatial resolution of 18 or 36 m with a spectral coverage ranging from 405 nm until 1050 nm. The main characteristics of CHRIS spectrometer are listed in Table 1.
Table 1. Main characteristics of CHRIS-PROBA. Instrument: push-broom imaging spectrometerField of view: 1.3o Ground swath: 13.5 km Altitude: Apogee: 688 km, perigee: 556 kmOrbit inclination: 97.8o Descending node: 12:10 local time Across track pixel size: 18m or 36m Along track pixel size: Finest resolution is 18m
Number of images: 5 acquisitions of the same area at +55, +36, 0, -36, -55 view angles
during the same orbit Spectral range: 410 nm to 1050 nm
Spectral resolution: From 1.25 nm @ 400 nm to 11
nm @ 1050 nm and binning possibility
Number of spectral bands From 18 bands at a spatial resolution of 18 m, to 63 at 36m
Sensor type:
E2V CCD25-20 (1152 x 780 pixels, 25 µm x 25 µm pixel size, full frame transfer, thinned and
back-illuminated) Digitalization 12 bits
Signal-to-noise ratio max. 250 @ target albedo = 0.2, λ= 800 nm, gain = 8.583
Since June 2002 up to now many hyperspectral images were collected over San Rossore forestry test site (latitude: 43.73o N, longitude: 10.30o E, altitude 5 meters a.s.l.) by CHRIS utilizing different MODEs and for various angular geometries. Table 2 lists the complete set of images acquired so far. Table 2. Complete list of CHRIS images so far acquired
over the San Rossore forestry test site.
DATE and TIME (GMT) MZA MODE1 June 19, 2002 10:27 - 6o 4 2 April 16, 2003 10:41 - 19o 4 3 June 16, 2003 10:32 0o 4 4 July 1, 2003 10:26 + 13o 4 5 July 25, 2003 10:34 - 2o 3 6 August 25, 2003 10:32 + 4o 30 7 September 18, 2003 10:37 - 8o 3 8 October 12, 2003 10:42 - 16o 3 9 October 19, 2003 10:31 + 5o 3
10 January 15, 2004 10:34 - 6o 3 11 February 9, 2004 10:43 - 15o 3 12 March 27, 2004 10:26 + 18o 3 13 May 7, 2004 10:29 + 14o 30 14 May 8, 2004 10:40 - 10° 30 15 September 7, 2004 10:29 + 13° 3 16 September 8, 2004 10:41 - 9° 3 17 September 16, 2004 10:38 - 4° 3 18 October 27, 2004 10:35 + 2° 3 19 January 2, 2005 10:40 - 4° 3 20 March 10, 2005 10:40 - 6° 3
Let us note that the CCD detector operates in a frame transfer mode, hence the sensor may be affected by an additional disturbance usually called “smearing effect”. 3. RANDOM NOISE AND SYSTEMATIC DISTURBANCE 3.1 Physical background Remotely sensed images are generally affected by different kinds of noise that can be distinguished in two main classes: pattern and temporal noise. To the first class belong periodic and coherent noise while random and isolated noise give rise to temporal disturbances [10]. Periodic noise shows a periodic spatial pattern whose phase and amplitude may or may not be constant. Coherent noise has a partially deterministic nature and, due to the circumstance that it can generate different patterns for each sensor spectral channel, this noise is also spatially and spectrally coherent. The random noise is generated by a fully stochastic process and it represents the last limitation to the image SNR. On the contrary isolated noise is characterised by large amplitude perturbation of the signal (spikes) that is measured in a limited number of samples. The temporal noise and in particular the random noise should be carefully considered for theoretical interpretation of data. In the case of an imaging spectrometer the main sources which contribute to random disturbances are well known and can be distinguished as in the following: - Photonic noise: it results from the inherent statistical
variation in the photon flux impinging on a sensitive element of the CCD. Photoelectrons generated within the semiconductor device constitute the signal whose amplitude will randomly fluctuate at each CCD pixel.
The photon number received in a given time interval obeys to Poisson’s statistics, and, therefore, the corresponding noise is proportional to the square root of the average signal amplitude. The term shot noise is applied to any noise component reflecting a similar statistical variation, or uncertainty, in measuring the number of photons collected during a given time interval.
- Dark noise: it arises from statistical variation in the number of electrons thermally generated inside the silicon structure of the photosensitive area. For frame transfer devices there is an additional contribution originated by the memory area of the detector. Other random noise components are due to noise in readout electronics, which often holds a d.c. offset produced by bias. This phenomenon is independent of photon-induced signal, but it highly depends on device temperature. The flux of thermal electrons at a given time referred to as dark current. Cooling the CCD significantly reduces the dark current. Another phenomenon that produces a similar effect is electron-hole pair generation due to high-energy radiation impinging on the detector area. Both corpuscular (mainly b-rays) and E.M. radiation may contribute to this phenomenon.
- Read noise: it is inherent to the process of converting CCD charge carriers into a voltage signal, and the subsequent process devoted to analogue-to-digital conversion. The major contribution to read noise usually originates from the on-chip amplifier chain, which originates frequency dependent noise. Inside this family we can distinguish reset noise and flicker noise. - Reset noise (kTC noise): Prior to the measurement
of each pixel charge packet the CCD capacitor is reset to a reference level. Noise is generated at the sense node by an uncertainty in the reference voltage level due to thermal variations in the channel resistance of the reset transistor;
- Flicker noise: it is referred to as 1/f noise because of its approximate inverse dependence on frequency;
- Charge transfer errors: it is due to imperfection in the process of transferring the charge packets between adjacent detector locations. In particular it results from the trapping of charge within a pixel either during integration time or as a charge packet passes through the pixel during the transfer time. This phenomenon gives rise to a random error whose amplitude may depend on the signal amplitude, array size, and read-out electronics.
- Round-off error: it is due to the finite ADC quantization accuracy.
Moreover, due to the operation mode of a frame transfer CCD another important source of systematic disturbance has to be considered: the “smearing effect”. This phenomenon is caused by light falling on the CCD during
the charge transfer phase. As long as the read-out phase duration is not negligible the charge packets significantly grow, originating a signal-dependent charge error. All these terms are summarized in the following Table 3. Table 3. Mathematical expression of possible sources of random noise ><σ (root mean square) in a remotely
sense d image.
Shot noise: intτσ ephoton N=><
Dark noise: int)(~ τςσ Tdark><
Read noise: wellread N1.0=><σ
Round-off error: )122( bitNwelloffround N=>< −σ
Smearing effect: λλττλδ
λ
λ∫=f
i
shifti dsks )(),()( int
where:
wellN : Full well capacity
eN Number of electrons generated after absorption of photons
intτ Integration time
)(Tς Function depending on sensor temperature T
bitN Accuracy of digitalisation
shiftτ Time for transferring carrier from a pixel-to-pixel
)(λs Spectral response of sensor
),( intττ shiftk Function depending on transfer time and integration time
fi λλ , Wavelength extension of a spectral channel 3.2 Coherent noise in “push-broom” imaging spectrometers The typical layout of an imaging device operating in the “push-broom” mode includes a CCD matrix array to resolve the spectral content of each target line imaged by the spectrometer. Thus a certain CCD line of elements corresponds to a fixed wavelength of the spectrometer’s free spectral range and each monochromatic image is sequentially acquired by the same row of CCD element located at the corresponding wavelength. Due to this circumstance the non-uniform distribution of sensitivity among the elements of the matrix detector superimposes a systematic pattern of noise organized by vertical lines upon the target image. We refer to this
disturbance as a spatial and spectrally coherent noise pattern, which would affect each pixel spectrum as well as the intensity distribution (texture) of each monochromatic image. From a theoretical point of view the signal g output by the detector is expressed as in Eq.1:
[ ] ))(,())(,()(*),,(),,( 0 ληληλλλ xgxSHyxiyxg += (1)
where x and y are the coordinates of a generic image pixel, S is the detector sensitivity, g0 the detector dark current, i the impinging radiance and H the Instrument Chromatic Response (ICR). In this way the convolution i*H represents the spectral radiance which illuminates the detector area placed in the focal plane of the spectrometer. Let us note that we have assumed the ICR to be a constant for all points of the spectrometer entrance slit. In addition the offset term g0 contains contributions from random noise and bias sources. The term ))(,( ληxS represents the response of the pixel at image column x that is dragged by the platform along the y (or t) dimension of the image to acquire the whole image at the wavelength (spectral channel) λ. Due to this circumstance ))(,( ληxS gives rise to a systematic pattern of disturbance that is constant along the image columns and that could change varying the selected spectral band. Let us note that the ))(,( ληxS fluctuations may be so large (~ to require accurate sensor calibration.
%)30±
3.3 Rejection of coherent noise: results from CHRIS images In the last year [1] we have developed an original algorithm to remove coherent noise patterns from remotely sensed images without the necessity of any flat-field calibration measurement. The basic idea is to separate contributions due to scene texture from those originated by the noise pattern. The algorithm utilises a subset of the Hough transform defined by all the integrals computed over image columns. In this way a profile p corresponding to the average image horizontal line is obtained as indicated in Eq.2:
∫= dyyxgxp ),,(),( λλ (2)
Due to the embedded averaging this profile is poorly influenced by any additive random noise, and it may
depend on the selected spectral channel (i.e. wavelength) and x coordinate. We note that image texture contribution to the mean line p is reduced by spatial averaging but it is not necessarily eliminated. Assuming that disturbance contributions rapidly change with position x differently from the smoothed texture contributions, it should exist a cut-off spatial frequency
which separates the two contributions. cfTo find and extract the higher-spatial-frequency components of profile p, originated from noise contributions alone, a two-stage filtering procedure has been designed. Firstly the profile p is convolved (smoothed) with a Gaussian weighting function w whose width σ is connected to the cut-off frequency σ
1~cf :
∫ −= ξλξξλ dpxwxs ),()(),( (3)
The obtained smoothed profile s contains contributions that are due to the scene’s texture, thus, the p-to-s ratio represents the shape of noise pattern alone as indicated in Eq.4:
),(
),(),(
λ
λλ
xs
xpxc = (4)
Finally the corrected image is obtained as the ratio between the original signal g and the correction profile c:
),(
),,(),,(
λ
λλ
xc
yxgyxi = (5)
Let us note that the correcting profile c oscillates around the value 1, hence the newly defined image i has on average the same intensity distribution of the original signal g. We have applied our algorithm to CHRIS images gathered over San Rossore since the radiance images as far distributed were always affected by notable striping. Fig. 1 shows an example of these radiance images of San Rossore corrupted by coherent noise.
0.90
0.95
1.00
1.05
1.10
0.90 0.95 1.00 1.05 1.10
intensity (u.a.)
inte
nsity
(u.a
.)
(a)
0.05
0.25
0.45
0.65
0.85
1.05
0.05 0.25 0.45 0.65 0.85 1.05
intensity (u.a.)
inte
nsity
(u.a
.)
(b)
Fig. 1. At-sensor radiance image acquired by CHRIS over San Rossore on September 18, 2003 with MZA = -8o and
FZA = 0o and displayed in the 1st band at 442 nm.
0
1
2
3
4
5
6
0 100 200 300 400 500 600 700 800pixel
inte
nsity
(u.a
.)
441.8 nm 490.5 nm 530.4 nm551.8 nm 570.5 nm 632.0 nm661.9 nm 675.4 nm 698.3 nm
(a)
0
1
2
3
4
5
6
0 100 200 300 400 500 600 700 800pixel
inte
nsity
(u.a
.)
441.8 nm 490.5 nm 530.4 nm551.8 nm 570.5 nm 632.0 nm661.9 nm 675.4 nm 698.3 nm
(b)
Fig. 3. Scatterplots for the correcting (a) and (b)
smoothed profiles for the first and last spectral bands. Application of our image-restoration procedure (destriping) to the shown CHRIS image is reported in Fig.4.
Fig. 2. Calculated profiles for the first nine bands of CHRIS hyperspectral image of Fig.1. Correcting (a) and (b) smoothed profiles were vertically shifted for clarity.
Fig. 4. at-sensor radiance image acquired by CHRIS over San Rossore on September 18, 2003 with MZA = -8o and
FZA = 0o and displayed in the 1st band at 442 nm. In Fig.2 the smoothed and corrected profiles for image of Fig.1 are shown. As expected the smoothed profiles slowly change with wavelength according to the assumption that they mainly contain contributions from texture.
As can be seen this algorithm originates impressive results, a quality level that is achieved for all spectral channels. Finally the algorithm’s ability to maintain unchanged the image brightness and the pixel spectra has been verified within %1± by using different sets of CHRIS images.
In Fig.3 the cross correlation between the correcting and smoothed profiles are detailed between different spectral channels. These scatterplots show that the correcting profiles are mutually uncorrelated while a strong correlation is found for the smoothed profiles.
In Fig.5 the same horizontal profiles extracted from image of Fig.2 and Fig.4 are compared. Let us note how
texture variations are maintained while the strong striping on the left side of the original data is suppressed.
0
20000
40000
60000
80000
100000
120000
140000
0 200 400 600 800
pixel
At-
sens
or r
adia
nce
(µW
m-2
sr-1
nm
-1)
-50000
-30000
-10000
10000
30000
50000
70000
90000
At-
sens
or r
adia
nce
(µW
m-2
sr-1
nm
-1)
L1B
BSQ
Fig. 5. Horizontal profiles extracted from Sira image (as shown in Fig. 2) (BSQ on right vertical axis) and from IFAC corrected image (as shown in Fig.3) (L1B on left
vertical axis). 4. IMAGE SIGNAL-TO-NOISE RATIO ESTIMATE 4.1 “Bit-plane” method Differently from coherent disturbances, random is due to a fully stochastic process. In remotely sensed images this kind of noise appears as a salt-pepper pattern which affects the scene quality, and can’t be removed without any spatial resolution losses. The knowledge of random noise amplitude is important to estimate the corresponding SNR. To this aim an estimation method has been with the essential property to be insensitive to scene texture. The algorithm is based on the assumption that noise statistics is spatially stationary and not auto-correlated (almost ergodic noise). The central idea of our algorithm is to extract from every image the corresponding bit-plane sequence starting from the less significant bit, and to establish if the bit-plane obeys a random distribution. To this aim the difference between any pixel of the bit-plane and each of its neighbour is computed, and the distribution of these differences is calculated. This distribution is analysed in order to assess if it has been generated by a random bit-plane or by a textured intensity distribution. The procedure is repeated for the entire bit-plane sequence and the SNR is computed. Let us suppose a rows by columns image to be
represented with and let be the extracted k
rN cN ),( yxg
bitN
,(xg
,,( yxi
),,( kyxg
{ }1,0
th bit-plane:
),1),1
),),
gkgkgkgk
−−−−
),,( kyxi
((
gg
gg
====
p−
0=i 1=∆ iP
1(21(2
−−+
), ∈ky (5)
Information about the randomness of the bit-plane distribution can be obtained by analysing the distribution of differences )k∆ between any pixel and each of its neighbour as in the following Eq.6:
(6)
),,(,1),,(),,(,1),,(),,(1,(),,(),,(,1(),,(
4
3
2
1
kyxyxkyxkyxyxkyxkyxyxkyxkyxyxkyx
+−∆++∆
+∆+∆
We only consider differences computed along four spatial directions being the remaining ones already included in difference computation performed around neighbouring pixels. It easily found that the statistics of the allowed values for the differences are given in the following table:
∆
Table 6. Possible values for the differences as
computed in Eq.6. ),,( kyxi∆
g1 0 1
0 A=0 B=1 g2 1 C=1 D=0 Let p be the probability to find a bit in the upper state “1” (or success event) and q = 1
∆P
the probability to find the bit in the lower state “0” (or failure event). Now we compute the probabilities , for the single
difference ),,( kyxi∆ as indicated in the Eq.7:
))
1
20
qPPPqPPP
CBi
DAi=+==+=
=∆
=∆ (7)
This expression means that, until the concerned bit-plane is dominated by random noise, the probabilities of the two only possible values for the difference ),,( kyxi∆ are equal to 21 . In other wordings the variable
),,( kyxi∆ obeys to the binomial distribution with 21 mean. This holds true even if the sensor ADC (Analogical-to-Digital Converter) would introduce a polarization effect in the output signal as demonstrated in the following. Let us suppose that the probability q of the
failure event is perturbed by a small amount 2/1<<ε so that:
1=∆i
δ
Our algorithm computes the variable δ and compares it with the expected standard deviation. The bit-plane is labelled as noisy if δ is less than the threshold being n an user-defined multiplier that affects the confidence of algorithm predictions. Let us note that accepting as noisy the values
ξσn
δσδ 3≤ we assess noisy bit-planes with roughly only 1% probability of missed hits (noisy bit-planes classified as textured ones). Moreover, calculation of false alarm probability is really complex and its prediction requires strong assumptions concerning textured bit-planes difference distribution
),,( kyxi∆ .
ε+=2
1q (8)
It is easy to verify that:
)(2
1221 2
0 εoqqP i +≈−+==∆ (9)
and in a similar way: 4.2 SNR estimates
)(2
1)1(21 εoqqP i +≈−==∆ (10)
which proves the probabilities and are 1/2
in the less of a second order power of ε. When the concerned bit-plane is dominated by scene texture the average value of the process
0=∆ iP
,,( yxi
P
)k∆ will tend to fall below 1/2, hence the difference
iyxi kyx ,,),,(21 >∆<−=δ between the expected 21
and the actual mean values should represent a reliable randomness index. In order to use this index for bit-plane randomness assessment it is necessary to account for the expected variability of it when considering an unknown pure-noise bit-plane. To this purpose the local theorem (Moivre-Laplace formulation) can help. Let be the number of “0” (failure) occurrences in the concerned bit-plane and
iyx ,,i kyx ),,(∆<
0n
>
the new random variable defined as:
Fig.7 shows an example of this processing as applied to the image reported in Fig.4. This image is obtained after composing together all the bit-planes recognised as noisy in the original image. As can be seen the extracted bit-planes truly seem to represent a fully random distribution, i.e. its outcome has the expected salt-pepper aspect.
Fig. 7. Result of extraction of noisy bit-plane of the CHRIS image acquired in the 4th spectral channel.
The processing reveals an average of 18 bits of data per pixel (information plus noise) in any spectral band. Only 7 out the 18 found bit-planes are recognized as significant (i.e., bit-planes which carry physical information), the remaining ones being dominated by noise (about 11 bit of noise per pixel).
cr
cr
NN
nNqN
4
4 0−=δ (11)
Let us note that the effective number of bits per pixel revealed by the algorithm is higher than the accuracy digitalisation of the CHRIS 12-bit sensor because calibrated images have been processed instead of raw data. Therefore radiometric levels higher than 4096 are expected.
where represent the total number of the computed differences.
cr NN4
According to Moivre-Laplace theorem as long as the number of measurements is high and the difference
is a fully stochastic field, this new variable ),,( kyxi∆ δ obeys to a normal distribution with vanishing mean and a standard deviation δσ given by:
However, if we extract the bit-planes sequence from each CHRIS spectral channel some troubles arise. An example of this processing for the 4th spectral band is shown in Fig.8.
cr NN
4
)1( −=δσ (12)
(bit-plane 01) (bit-plane 02)
(bit-plane 03) (bit-plane 04)
(bit-plane 05) (bit-plane 06)
(bit-plane 07) (bit-plane 08)
(bit-plane 09) (bit-plane 10)
(bit-plane 11) (bit-plane 12)
(bit-plane 13) (bit-plane 14)
(bit-plane 15) (bit-plane 16)
(bit-plane 17) (bit-plane 18) Fig. 8. Bit-planes sequence for the 4th spectral channel from the least significant bit up to the most significant bit carrying information. The obtained outcome in the 4th spectral band has a different looking: As an instance the 10th bit-plane of the analysed sequence obeys a fully random distribution over land and inland waters but is affected by image texture over sea. In other wordings while over land the concerned bit-plane has the expected salt-pepper aspect, over sea some scene textures already emerge from the image.
The algorithm reveals a noise distribution among bit-planes, which is systematically shifted with respect to that one measured over land. The problem does not appear related to the particular radiometry or spectral signature of water, since all inland waters have exactly the same noise distribution as soils with different cover types. It is worth noting that the sensor itself should not originate any signal-statistics variation between different image regions, a difference that would have been added in the subsequent processing step. In order to obtain a reliable estimate of the SNR we have selected an image stripe, as depicted in Fig.9, that excludes sea areas.
(a) (b)
Fig. 9. (a) CHRIS image acquired in the 4th spectral channel over San Rossore containing only soil and vegetation areas. (b) Result of extraction of noisy bit-plane of the same CHRIS image. As can be seen the part of the image signal dominated by texture is cleaned by the meaningless bit-planes which do not add information to the data. Let us note that the cleaned image does not have a higher SNR than the original one, since the first encountered bit-plane which is not recognized as noisy still contains important noise contribution. The obtained SNR is reported in the Table 10 and its spectral profile is plotted in Fig.11.
Table 10. CHRIS SNR computed from image shown in Fig.9 at each spectral channel.
Band Peak_SNR SNR 1 38.7 138.3 2 33.1 162.3 3 14.8 76.2 4 15.3 84.6
5 13.5 77.8 6 23.5 159.3 7 22.7 150.7 8 22.8 157.2 9 11.2 67.8 10 12.7 70.3 11 13.2 71.0 12 17.8 68.9 13 18.4 70.0 14 18.4 64.7 15 17.3 57.2 16 14.5 46.7 17 11.7 94.5 18 16.4 97.2
0
20
40
60
80
100
120
140
160
180
400 500 600 700 800 900 1000 1100
w a v e l e n g t h ( n m )
SNR
P e a k_SNR
Fig. 11. CHRIS SNR plot versus wavelength computed
from image of Fig.9. In Fig.12 the sequence of bit-planes extracted from Fig.9(a) is plotted.
(bit-plane01)
(bit-plane02) (bit-lane03)
(bit-plane04)
(bit-plane05) (bit-plane06)
(bit-plane07)
(bit-plane08) (bit-plane09)
(bit-plane10)
(bit-plane11) (bit-plane12)
(bit-plane13)
(bit-plane14) (bit-plane15)
(bit-plane16)
(bit-plane17) (bit-plane18)
Fig. 12. Sequence of bit-planes extracted from the image
shown in Fig.9(a). As can be seen the first 11 bit-planes of the 18 ones per pixel revealed by the algorithm are recognised as noisy while the remaining ones (about 7 bit per pixel) carry information containing scene texture. As expected the noise distribution is confined to the less significant bits while the information occupy the upper states of the word length. 5. CONCLUSIONS In this paper hyperspectral images acquired by CHRIS over San Rossore (Italy) site have been processed in order to assess information and data quality. Particular attention has been devoted to estimate noise and SNR by utilising an innovative algorithm developed by the authors. From this analysis, some troubles have arisen concerning the noise distribution among bit-planes as extracted from any monochromatic image. The algorithm revealed a noise distribution among bit-planes, which was systematically shifted with respect to that one measured over land. Finally, an example of SNR retrieved by the algorithm has been presented and its behaviour with changing wavelength has been investigated. 6. AKNOWLEDGMENTS We would like to thank all the scientists from different institutions that are involved in remote sensing activities carried out in San Rossore test site. Moreover, we would like to thank people from SIRA and RSAC in particular M. Cutter and P. Fletcher for planning the acquisitions and providing CHRIS data, and for useful discussions. 7. REFERENCES 1. Barducci, A., and Pippi, I. Analysis and rejection of systematic disturbances in hyperspectral remotely sensed images of the Earth. Applied Optics, Vol. 40, pp. 1464 – 1477, 2001.
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