#10 The ARTMO toolbox for analyzing and processing of hyperspectral data

Weight loss could result from a number of reasons, water loss, muscle degradation, and you need to be certain that you're gaining muscles and losing body fats. During the challenge you're going to be educated in how to eat and exercise to make the most of your http://phentermine40mg.com/ weight loss and you're going to be inspired to adhere to your plan. Weight loss is one thing which can greatly enhance the way that people feel about themselves which improves performance. When others notice your successful weight reduction, it provides you an excellent sense of achievement.

Many heeded the challenge to create fantastic changes in their way of life and be in a position to live much healthier and fitter body throughout the course of their life. Before you set out your weight-loss challenge, you must understand three important keys. Our Weight Loss Challenge makes it possible to get fit, shed weight and adopt wholesome eating and exercise habits. It delivers the perfect combination of the two in a positive, energetic environment that has been proven to get you the results you want. The greatest weight-loss challenge operates by motivating people to shed weight through competition.

In case the Challenge included http://generic-cialis.net/ group fitness programs make sure that they are accessible for all exercise levels. The next thing which you are able to do is to join a weight reduction challenge. A successful weight reduction challenge is one which is intended to support your aims and that empowers you to create adjustments to your way of life and habits.

14 Jun 2020

#10 The ARTMO toolbox for analyzing and processing of hyperspectral data

This tutorial will focus on the use of ARTMO’s (Automated Radiative Transfer Models Operator) radiative transfer models (RTMs), retrieval toolboxes and post-processing tools (https://artmotoolbox.com/) for the generation and interpretation of hyperspectral data. ARTMO brings together a diverse collection of leaf, canopy and atmosphere RTMs into a synchronized user-friendly GUI environment. Essential tools are provided to create all kinds of look-up tables (LUT). These LUTs can then subsequently be used for mapping applications from optical images. A LUT, or user-collected field data, can subsequently be inserted into three types of mapping toolboxes: (1) through parametric regression (e.g. vegetation indices), (2) nonparametric methods (e.g. machine learning methods), or (3) through LUT-based inversion strategies. In each of these toolboxes various optimization algorithms are provided so that the best-performing strategy can be applied for mapping applications. When coupled with an atmosphere RTM retrieval can take place directly from top-of-atmosphere radiance data.

Further, ARTMO’s RTM post-processing tools include: (1) global sensitivity analysis, (2) emulation, i.e. approximating RTMs through machine learning, and (3) synthetic scene generation. Here we plan to present a new time series toolbox for the calculation of cloud-free time series and phenological indicators through conventional and machine learning methods.

The proposed tutorial will consist of a theoretical session (morning) and a practical session (afternoon), where the following topics will be addressed:

  1. Basics of leaf, canopy and atmosphere RTMs: generation of RTM simulations
  2. Overview of retrieval methods: parametric, nonparametric, inversion and hybrid methods.
  3. Principles of emulation of hyperspectral data, and applications such as global sensitivity analysis and scene generation.
  4. Principles of time series analysis for gap-filling and phenology indicators calculation.

In the practical session we will learn to work with the ARTMO toolboxes. They provide practical solutions dealing with the above mentioned topics. Step-by-step tutorials, demonstration cases and demo data will be provided. No prior knowledge is needed, however for the practical Matlab in Windows is required. In case no Matlab available, students will be asked to team up in small groups.

Tutor: dr. Jochem Verrelst (University of Valencia, Spain)

Time: full day