Preprocessing of Hyperspectral Imaging

Authors- Associate Professor H. V. Bhujle, Associate Professor B.H. Vadavadagi

Abstract-In this study, we focus on preprocessing hyperspectral images for precision agriculture based on the Non-Local Means (NLM) technique. Hyperspectral data snaps earth objects by measuring a continuous spectrum for each pixel in a image. A hyperspectral cube contains thousands of images, each providing complementary information. Preprocessing of hyperspectral data is essential to enhance the accuracy and efficiency of subsequent analysis by minimizing various distortions. To enable comprehensive analysis, these images need to be fused into a single image that retains all critical information. Real-time sensing through ground-based remote sensors, combined with satellite remote sensing, can help bridge this time gap and enable applications across larger areas. This integration can also support the development of variable-rate input technologies for site-specific crop management, enhancing resource use efficiency and profitability. Such strategies allow for the implementation of both preventive measures and targeted field interventions. Currently, crop production and yield estimation rely on two main approaches: statistical methods and crop-growth models; Crop-growth models simulate and monitor crop growth by analyzing factors like planting date, seeding rate, crop density, variety, and weather conditions (solar radiation, precipitation, and temperature). Soil characteristics and nutrient availability are also critical inputs. We propose a hierarchical fusion model utilizing nonlocal means (NLM) filtering, known for its ability to preserve edges and structural details effectively. After image fusion, hyperspectral filtering is performed. Since noise can interfere with hyperspectral image analysis, particularly for crop classification, we introduce a noise removal method based on a multiresolution framework. Both qualitative and quantitative evaluations indicate that the proposed preprocessing techniques outperform existing methods.

DOI: /10.61463/ijset.vol.12.issue6.354