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Study on the Spectral Quality Preservation Derived from Multisensor Image Fusion Techniques between JERS-1 SAR and Landsat TM Data
Rokhmatuloha, Tateishi R.a, Wikantika K.b, Munadi K.c, Mohammed Aslam M.A.a
a Ctr. for Environ. Remote Sensing, Chiba University-Japan, Japan
b Department of Geodtic Engineering, Institute of Technology, Indonesia
c Electrical Engineering Department, Tokyo Metropolitan University, Japan
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]The advantage of multisensor data fusion stems from the fact that the use of multiple types of sensors increases the accuracy with which a quantity can be observed or characterized. The response of radar is more a function of geometry and structure than surface reflection as occurs in the optical wavelengths. A suitable fusion method has to be chosen with respect to the used spectral characteristic of the multispectral bands and the intended application. This paper describes a comparative study of multisensor image fusion techniques in preserving spectral quality of the fused images. Image fusion techniques applied in this study are: wavelet, intensity-huesaturation (IHS), principal component analysis (PCA), and high pass filtering (HPF). With these image fusion techniques, a higher spatial resolution JERS-1 SAR is fused with Landsat TM data. The merging process is carried out at the pixel level and the comparison of the resulting images is explained based on the measurement in preserving spectral quality of the fused images. Assessment of the spectral quality is performed by graphical and statistical methods between original TM image and the fused images. The factors computed to qualify the fused images are: mean, standard deviation, coefficient correlation, and entropy. With a visual inspection, wavelet and PCA techniques seem to be better than the other techniques. PCA provided the greatest improvement with an average entropy of about 5.119 bits/pixel.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Fused images,Spectral quality[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]High-pass filtering,Intensity-hue-saturation,Multisensor data fusion,Principal component analysis,Spectral quality[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]