CLOUD REMOVAL IN HIGH
RESOLUTION SATELLITE IMAGES
USING DISCRETE WAVELET
TRANSFORM
P.Ramya 1, Mr.S.KarthiPrem2 ,A.Nithyasri3
123
Department of Information Technology
Vivekananda College of Engineering for women,
1
Pg scholar
23
Assistant professor
1
29ranram@gmail.com, 2karthiprem@gmail.com,3nithi.becse@gmail.com
ABSTRACT:
Satellite images playing a major role for monitoring earth
comparing the multi-temporal images and find the
changes in land covers such as forest, cities, agriculture,
difference between the images. If the differences between
coastal area etc., but the major problem in the earth
the images are positive then it has higher accuracy so the
observation is the clouds in the satellite images. Thus cloud
values are added otherwise the values are subtracted. Then
cover removal is very essential in the field of satellite
the cloudy region will be replaced by the value found. The
imagery analysis. But it is very complex to present the most
cloud removal method will gain the accuracy between
accurate information in the cloudy region. The existing
85%– 90%. After removing clouds the Discrete wavelet
system is the modified neighborhood similar pixel
transforms are applied to gain the accuracy of the result.
interpolates method which as the major limitations that it
The wthresh is applied to remove noise from the images.
lack its accuracy when the size of the cloud pixel increase
Then the inverse discrete wavelet transform is applied to
and if the frequent clouds are present it is hard to find the
gain the output image.
similar pixels. The proposed method helps to improve the
Keywords: Efficient Cloud Detection and Removal
accuracy level to some extent than the existing methods.
Algorithm, wthresh, NSPI(Neighborhood similar pixel
The proposed Efficient Cloud Detection and Removal
interpolator), spectro-spatial information.
Algorithm (ECDR) may improve the degree of accuracy by
I.EXISTING SYSTEM
from the satellite images. It is the proposed method
A modified neighborhood similar pixel interpolates
of the Neighborhood similar pixel interpolator
method (modified NSPI) helps to remove the cloud
(NSPI). In the NSPI approach, it predicts the spectral
value of the target pixels from its neighboring similar
pixels. It employs the threshold to identify the similar
pixels. The weight between the similar pixels and
If the target pixel is located near the cloud center
target pixels can be calculated by using the Euclidean
then the spectro-temporal information is more
distance. It has the major drawback such as cloudy
consistent
images may sometime replace as the similar pixels.
information becomes less useful around the
Due to the varying size of the cloud and randomly
cloud center where the target pixel is farther to
shaped the similar pixel replacement can reflect the
its similar pixels. The cloud center is computed
cloudy appearance. Thus the modified NSPI method
by averaging the coordinates of the pixel.
because
the
spectro-spatial
is used and it overcomes those problems.In the
modified NSPI method the time series images are
Some Drawback of the detections:
used to replace the cloud area. The detailed
The accuracy of the recovered image decreases
descriptions of the steps that are different from the
with the increased cloud size. As the cloud size
original NSPI approach are given as follows:
increases, the average distance between a target
The cloud masking is the first step used in this
method where thick clouds are brighter in the visible
band and lighter in thermal bands than the land
surface. The second step is searching the similar
pixel. In a modified NSPI method, it helps to search
the spectrally similar pixels around the cloud. It is
pixel and the similar pixels increases and their
correlation decreases.It is hard to obtain a cloud
free auxiliary image from the same sensor in the
same location.If the cloud presents frequently in
an image then this method lack its efficiency.
II. PROPOSED SYSTEM
shown in the figure 2. The cloud-free image is used
In the cloud removal process there are lots of
to select the nearest similar pixels which should
existing algorithms are available. The proposed
satisfy the spectral similarity criteria.The third step is
method is Efficient Cloud Detection and
the gap-filling, in which the data has to fill in the area
Removal Algorithm (ECDR) which helps to
where the cloud masked. In the cloud-removal
improves the accuracy of the cloud restoration
process, the distance between a cloudy and its similar
area than the NSPI method. The proposed
pixels
algorithm is based on the multi-temporal
may
vary
greatly.
approach. The time series images are taken as a
reference image. The reference image must be
more than three to get the accuracy in the result.
Consider the reference images as R1, R2, R3,
etc., and the cloud contaminated images as C1
and it is considered as the original images.
The cloud detection is the very important step
without cloud detection the cloud removal and
reconstruction is a complex problem. The clouds
Fig.1 Modified neighborhood Similar Pixel
can be detected by the calculation of the pixel
Interpolator
value. The clouds are brighter in visible bands
and lighter in thermal bands. The cloud pixel
monitored because the clouds will cover the
values are ranges from 200 to 255 in thermal
areas. Thus, lots of existing methods are
band and 255 in visible band. Based on this
available for cloud detection and removing.The
calculation the cloud values are detected. After
existing system has lot of problems when the
cloud detection, consider the reference image
images are applied to the real time applications.
RI1, RI2 and RI3 and find the difference
The existing system modified NSPI approaches
between them. Then find the difference between
has face problems such as lack of accuracy,
them. Compare the pixel values from the image
similar to the other existing methods etc. The
difference and find the average. Calculate the
major disadvantages in the existing method is
RI3 variations using RI3 and RI3 with average of
that, if the frequent clouds are appeared then the
image difference. Find the difference between
existing method cannot produce the accurate
the D1 and D2, based on the positive and
result because in the existing method where the
negative values in the difference; replace the
similar pixels can be find by calculating based on
cloud pixel by RI3 with variance. If the
the distance between the target pixels.Thus, it is
difference value is positive then add the value
possible to replace the cloud pixel has the similar
with RI3 otherwise subtract the value and replace
pixel. If the cloud pixel is replaced then the
for the cloudy region.
method lacks its accuracy.
In the proposed
ECDR algorithm there is no chance of replacing
The filtering is used to enhance the contrast of
the image. The quality of the image is improved.
The discrete wavelet transform is used for
the cloud pixel because all the information’s are
gathered from the reference images. Thus, there
is no chance of replacing the cloud pixel.
filtering the image i.e.,de-noising. The images
are divided into the sub-bands and the wavelet
Generally there are three types of cloud removal
threshold (WThresh) is applied to the sub-bands
methods are present but each has its own
except the original images. The WThresh helps
algorithm, methods and techniques.
to de-noise the image. So, the quality of the
The three types of cloud removal methods
image is improved.Image can be de-noised at
different levels of resolution and can be
are:
sequentially processed from low resolution to

In-painting method
high resolution.Sub-band level analysis results in

Multi-spectral method
high accuracy. Gain accuracy in cloud removal.

Multi-temporal method
Applicable for all sizes of the clouds. Produce
efficient results even frequent clouds are
appearing.The proposed system is designed to
detect and remove clouds from the satellite
images. In the coastal regions and during the
winter season the tropical regions are fully
covered with clouds. The cloudy regions are not
In-Painting Method
Multi-Spectral Method
In multispectral-based approaches, multispectral
data are utilized in cloud detection and removal. The
different band images are taken and reconstruct the
cloudy region of the image. The different satellite
images have different spectral bands such as band
1,2,3,4 and so on. But the different spectral band
Figure (a) Simulated original Cloudy image
images have different color combinations thus the
accuracy of the image is not up to the level. But when
compared to the In-painting technique the result
accuracy was level increased.
Multi-Temporal Method
The Multi-temporal based method is somewhat better
method when compared to the Multi-temporal and inpainting method, which rely on both temporal
coherence and spatial coherence have a better ability
Figure (b) Result of original image
Fig1.1 In-painting method’s result with simulated
cloud
The in-painting method is the earliest method that
helps to remove clouds from the satellite images. In
the in-painting technique, the data’s are gathered
from its neighboring regions either its mean value or
to cope with large clouds. The spectrotemporal
relationships are inferred from cloud-free areas in the
neighborhood of cloud-contaminated regions over the
available temporal images. A thresholding-based
approach is used to identify the best cloud-free and
non-shadow pixels region is given and then generated
by stitching or mosaic king the selected cloud free
pixels.
the similar pixel value.
The multi-temporal based method produced result is
But the biggest problem is that the data which
reconstruct on the cloudy region is not accurate. The
major problem in this method is that the boundary of
the cloud is not fixed. It is represented in the figure
5.1. The boundary of the cloud is clearly visible in
the above result and the data which substitute for the
cloudy region is not an accurate result. This problem
can be solved in the proposed ECDR algorithm.
more accurate when compared to the above two
method because in this method where the information
are gathered from the previous year image of the
same location.
The result of the multi-temporal based information
cloning algorithm is presented below:
band. This makes cloud removal relatively simple
and convenient; the cloud removal method will gain
the accuracy between 85%– 90%. After removing
clouds the Discrete wavelet transforms are applied to
gain the accuracy of the result.
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