Advances
in Engineering: an International Journal (ADEIJ), Vol.2, No.3
REGION
CLASSIFICATION AND CHANGE DETECTION USING LANSAT-8 IMAGES
S.Nivetha1
and Dr.R.Jensi2 1M.E, Final Year Dr.Sivanthi Aditanar
College of Engineering, Tiruchendur. 2Assistant Professor,
Dr.Sivanthi Aditanar College of Engineering, Tiruchendur.
ABSTRACT
The
change detection in remote sensing images remains an important and open problem
for damage assessment. A new change detection method for LANSAT-8 images based
on homogeneous pixel transformation (HPT) is proposed. Homogeneous Pixel
Transformation transfers one image from its original feature space (e.g., gray
space) to another feature space (e.g., spectral space) in pixel-level to make
the pre-event images and post-event images to be represented in a common space
or projection space for the convenience of change detection. HPT consists of
two operations, i.e., forward transformation and backward transformation. In
the forward transformation, each pixel of pre-event image in the first feature
space is taken and will estimate its mapping pixel in the second space
corresponding to post-event image based on the known unchanged pixels. A
multi-value estimation method with the noise tolerance is produced to determine
the mapping pixel using K-nearest neighbours technique. Once the mapping pixels
of pre-event image are identified, the difference values between the mapping
image and the post-event image can be directly generated. Then the similar work
is done for backward transformation to combine the post-event image with the
first space, and one more difference value for each pixel will be generated.
Then, the two difference values are taken and combined to improve the
robustness of detection with respect to the noise and heterogeneousness of
images. (FRFCM) Fast and Robust Fuzzy C-means clustering algorithm is employed
to divide the integrated difference values into two clusters- changed pixels
and unchanged pixels. This detection results may contain few noisy regions as
small error detections, and a spatial-neighbor based noise filter is developed
to reduce the false alarms and missing detections. The experiments for change
detection with real images of LANSAT-8 in Tuticorin between 2013-2019 are given
to validate the percentage of the changed regions in the proposed method.
KEYWORDS
Change
detection, remote sensing, heterogeneous images, mapping image
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