Autoencoder for anomaly detection in hperspectral imagery
Leveraging spectral and spatial information to improve autoencoders, enhancing the identification of abnormal pixels Hyperspectral imaging (HSI) from spaceborne, airborne, and drone platforms has emerged as a transformative Earth observation technology and provides spatially distributed detailed spectral information across potentially hundreds of contiguous wavelength bands. By capturing the unique spectral signatures of materials, hyperspectral sensors enable the precise identification and characterisation of vegetation, soils, minerals, water quality, and man-made objects. Satellite-based systems offer large-scale and repetitive monitoring for global environmental and climate studies, while airborne sensors provide higher spatial resolution and flexibility for regional applications. Drone-mounted HSI cameras further enhance observation capabilities by delivering ultra-high-resolution data tailored to local-scale investigations. Together, these platforms support a wide range of applications, including precision agriculture, ecosystem monitoring, biodiversity assessment, mineral exploration, environmental pollution detection, disaster management, urban mapping, and infrastructure inspection. The synergy between space, air, and drone observations enables multi-scale analyses, fostering improved understanding and sustainable management of natural and built environments. A hyperspectral image is arranged by a three-dimensional (3D) data cube (Fig. 1) acquired by using …






