Effectiveness of Super-Resolution Technique on Vegetation Indices

Abstract

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, an 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 indices. 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 percent for the Normalized Difference Vegetation Index (NDVI) and Fractional Vegetation Cover (FVC), respectively, as compared to images tested using the non-SR technique.

Keywords

• Vegetation mapping
• convolutional neural networks
• ground sampling distance (GSD)
• NDVI