Hyperspectral Image Classification with Convolutional Neural Networks
作者： Viktor Slavkovikj , Steven Verstockt , Wesley De Neve , Sofie Van Hoecke , Rik Van de Walle
关键词:
摘要: Hyperspectral image (HSI) classification is one of the most widely used methods for scene analysis from hyperspectral imagery. In past, many different engineered features have been proposed HSI problem. this paper, however, we propose a feature learning approach based on convolutional neural networks (CNNs). The CNN model able to learn structured features, roughly resembling spectral band-pass filters, directly input data. Our experimental results, conducted commonly-used remote sensing dataset, show that method provides results are among state-of-the-art, without using any prior knowledge or features.
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