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Pansharpening is a technique that fuses the coarser resolution of multispectral imagery (MS) with high spatial resolution panchromatic (PAN) imagery. Pansharpening is prone to spectral distortions based on the nature of the panchromatic band. If the spatial features are unclear in the panchromatic image, the pan-sharpened image will not be able to produce clear images. Super-Resolution (SR) is a technique that enhances minute details of the features in the image, thereby improving spatial information in the image. By fusing the Multispectral image with the super-resolved panchromatic image, there is a chance for producing high-quality multispectral imagery (pan-sharpened image). In this paper, ten state-of-the-art super-resolution based on deep learning techniques are tested and analyzed using ten different publicly available panchromatic datasets. On analysis, a feedback network for image super-resolution (SRFBN) technique outperforms the other algorithms in terms of sharp edges and pattern clarity, which are not visible in the input image. The proposed method is the fusion of SR applied PAN image with the MS image using a benchmarked Band Depended Spatial Detail (BDSD) pansharpening algorithm. The proposed method experiments with six datasets from different sensors. On analysis, the proposed technique outperforms the other counterpart pansharpening algorithms in terms of enhanced spatial information in addition to sharp edges and pattern clarity at reduced spectral distortion. Hence, the super-resolution based pansharpening algorithm is recommended for high spatial image applications.

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The identification and interpretation of remote sensed (RS) objects in an image depend on how well the sensor captures the region. In rare cases, RS images may be vulnerable to a lack of interpretability issues in some parts of the image due to the sensor's limits and preprocessing techniques. Conventionally, the interpretation of the land cover pattern's shape and size is apparent when the distance between the sensor and object is closer to the visualization level of objects and viable with digital airborne imagery. In this paper, integration of the Super-Resolution (SR) technique in the high-resolution imagery to achieve the closer visualization level for mapping the vegetation is proposed. This approach enables the higher interpretive potential to define the land pattern's shape and size with very high spatial resolution, closer proximity, detailed and distinguishable patterns. This approach helps to precisely predict the total vegetated study area for land use and land cover changes (LULCC) and chlorophyll-rich vegetation applications. The proposed algorithm is carried out in two phases. In the first phase, the SR technique applied test images are tested for vegetation detection and mapping the vegetation information in a region with fourteen vegetation metrics. In the second phase, similar testing is done without applying SR for input test images. The experiment revealed that test images using the SR technique yielded higher average values of 5 percent and 1.1 percent for the Normalized Difference Vegetation Index (NDVI) and Fractional Vegetation Cover (FVC), respectively, as compared to images tested using the non-SR technique.

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