Land Cover Classification of Hyperspectral Imagery using Deep Neural Networks

Hyperspectral Imaging is an important technique in remote sensing, which collects the electromagnetic spectrum ranging from the visible to the near-infrared wavelength. Hyperspectral imaging sensors often provide hundreds of narrow spectral bands from the same area on the surface of the earth. In hyperspectral images (HSI), each pixel can be regarded as a high-dimensional vector whose entries correspond to the spectral reflectance in a specific wavelength. With the advantage of distinguishing subtle spectral differences, HSIs have been widely applied in diverse areas such as Crop Analysis, Geological Mapping, Mineral Exploration, Defence Research, Urban Investigation, Military Surveillance, Flood Tracking, etc. In this article, we are going to use the Pavia University Hyperspectral Image for classification purposes.