Abstract:
Image-based fusion is a state-of-art process to extract
vital information by combining the two or more images acquired
from different satellite sensors. Recently launched (26th September
2016) ISRO's (Indian Space Research Organization) Ku-band
(13.5 GHz) based Scatterometer Satellite (SCATSAT-1) as an
active microwave sensor can offer the day-night, all-weather
monitoring services, which are not possible with the optical-based
visible and infrared remote sensing satellites. Therefore, the fusion
of optical and microwave data offers the cloud-free detection of
earth surface transitions and helps in emergency response to
natural hazards, security, and defence. The objectives of the
proposed framework are (a) nearest-neighbour based fusion
(NNF) of ISRO's SCATSAT-1 and NASA's (National Aeronautics
and Space Administration) moderate resolution imaging
spectroradiometer (MODIS) optical data, (b) generation of
thematic maps using artificial neural network (ANN) based
classification of the fused data, (c) detection of spatiotemporal
variations via post-classification comparison (PCC) based change
detection, (d) cross-referencing with well-defined fusion methods,
i.e. Gram-Schmidt (GS), Brovey Transformation (BT) and Ehlers,
and (e) Impact analysis of clouds on the input dataset and fusion
methods. This study has been conducted over the Western
Himalayas to estimate the snow cover changes under cloudy
conditions with two datasets i.e., winter and monsoon. The
experimental outcomes confirm the efficacy of the proposed
framework in the effective removal of clouds, generation of
classified maps, and change maps. The present study includes an
exhaustive list of applicative situations for cloud-free monitoring
using freely and daily based SCATSAT-1 and MODIS datasets.
Index Terms— Scatterometer Satellite (SCATSAT-1);
Moderate Resolution Imaging Spectroradiometer (MODIS);
Nearest-Neighbor based Fusion (NNF); Artificial Neural Network
(ANN); Post Classification Comparison (PCC).
