Application of Inverse Mapping for Automated Determination of Normalized Indices Useful for Land Surface Classification

Precise surface classification is essential for glacial health monitoring, where normalized indices have traditionally been used. These indices are created empirically for a specific sensor. The transferability of these indices to other sensors can be affected by differences in spectral and spatial resolution. Thus, it is essential to evaluate the transferability of an index before applying it to a new sensor to ensure accuracy and reliability. However, as the number of satellites, sensors, and observation bands increases, there is a need for automated methods for determining application-specific normalized indices. In this article, we propose using all the bands of multispectral optical sensors to generate multiple normalized indices and determining application-specific indices using inverse mapping. We use these normalized indices for pixel-by-pixel surface classification using neural networks. First, we employ all the bands for generating normalized indices and then eliminate low-spatial-resolution bands to evaluate classification performance by using only high-spatial-resolution indices. We apply this method to a glacial region and observe 81.98% and 84.81% overall accuracy compared to the ground truth data for the two classifications, respectively. We then apply inverse mapping dynamics to the classification results to determine prominent indices useful for glacier classification. The results show that although some of the determined indices are not traditional indices, they are still useful for classification due to the relative differences between various land types. The proposed method has the potential to automate normalized index determination, thereby eliminating the need for empirical band assessment methods and making the index development process more efficient.