SHADOW DETECTION IN HYPERSPECTRAL IMAGES ACQUIRED BY UAV
- UNESP, School of Science and Technology, 19060-900 Presidente Prudente, Brazil
Keywords: Shadow detection, UAV, hyperspectral image, high spatial and spectral resolution image, spectral signatures, cloud shadow in agricultural fields
Abstract. Shadows are common in any kind of remote sensing images. Unmanned Aerial Vehicle – UAV with a light camera attached can acquire images illuminated either by direct sunlight or by diffuse light under clouds. Indeed, areas with pixels shaded by clouds must be detected and labelled in order to use this additional information for image analysis. Classification of health and diseased plants in permanent culture as the orange plantation field can present some errors due to tree cast shadow. So, hyperspectral or multispectral image classification can be improved by previous shadow detection. Some FPI hyperspectral camera, designed for agricultural applications is limited in the spectral range between 500 to 900 nm. Wavelengths in the region of blue light and in the SWIR spectral region have physical properties that enable the enhancement of shaded regions in the images. In this work some combinations of different spectral bands were evaluated in order to specify those suitable to detect shadows in agricultural field images. In this sense, considering that vegetation and soil are the two main kind of coverage in an agricultural field, we hypothesized that wavelengths near blue light and the longest near infrared available in the camera range are good choices. In both spectral regions soil and vegetation targets have small spectral differences which contribute to enhance the differences between shaded and illuminated regions in the image. Hyperspectral images acquired with a FPI hyperspectral camera onboard a UAV over a plantation of oranges were used to evaluate these spectral bands. The results showed that the wavelengths of aproximatelly 510 nm and 840 nm available in the FPI camera are the best to detect any type of shadows in the agricultural fields.