**Linear and NonȬLinear Models for RemotelyȬSensedȱ ȱ Hyperspectral Image Visualization**

**RaduȬMihai Coliban \*, Maria Marincas, Cosmin Hatfaludi and Mihai Ivanovici**

Electronics and Computers Department, Transilvania University of Bra¸sov, 500036 Bra¸sov, Romania;ȱ ȱ maria.marincas@student.unitbv.ro (M.M.); cosmin.hatfaludi@student.unitbv.ro (C.H.);ȱ ȱ mihai.ivanovici@unitbv.ro (M.I.)

**\*** Correspondence: coliban.radu@unitbv.ro

This paper proposed the use of a linear model for color formation to emulate the image acquisition process by a digital color camera and investigated the impact of the choice of spectral sensitivity curves on the visualization of hyperspectral images as RGB color images. In addition, a non-linear model based on an artificial neural network was also proposed. With the proposed linear and nonlinear models, the impact and the intrinsic quality of the hyperspectral image visualization could be assessed based on the amount of information present in the image quantified by color entropy and scene complexity measured by color fractal dimension, both of which provide an indication of detail and texture characteristics of the image. The experiments compared four other methods and the superiority of the proposed method was demonstrated.

#### IX. **Applications (four papers)**

remotesensingȬ13Ȭ02243Ȭv3

## **Generative Adversarial Network Synthesis of Hyperspectral Vegetation Data**

**Andrew Hennessy \*, Kenneth Clarke and Megan Lewis**

School of Biological Sciences, The University of Adelaide, Adelaide 5000, Australia;ȱ ȱ kenneth.clarke@adelaide.edu.au (K.C.); megan.lewis@adelaide.edu.au (M.L.) \* Correspondence: andrew.hennessy@adelaide.edu.au

This paper applied advances in generative deep learning models to produce realistic synthetic hyperspectral vegetation data whilst maintaining class relationships. Specifically, a Generative Adversarial Network (GAN) was trained using the Cramér distance on two vegetation hyperspectral datasets, demonstrating the ability to approximate the distribution of the training samples. The creation of an augmented dataset consisting of synthetic and original samples was used to train multiple classifiers, with increases in classification accuracy observed in almost all circumstances. Both datasets showed improvements in classification accuracy ranging from a modest 0.16% for the Indian Pines set to a substantial increase of 7.0% for the New Zealand vegetation.

remotesensingȬ13Ȭ03207
