This slide collection focusses on quantification using regression analysis and RTMs. Following a short introduction about radiance regime of vegetation, we provide an overview about retrieval methods of vegetation bio-geophysical and biochemical traits from imaging spectroscopy data. Retrieval methods can be classified into the following four methodological categories: (1) parametric & (2) nonparametric regressions, (3) RTMs, and (4) hybrid methods. Further, emulation and quantification of uncertainties will be briefly discussed in this lecture.
Image Processing
Dimensionality reduction of imaging spectroscopy data
This unit provides an introduction to dimensionality reduction methods related to hyperspectral data or imaging spectroscopy. First, feature engineering methods using selected parametric and nonparametric regression methods are presented. Second, feature selection or extraction, using filter, wrapper and embedded methods will be discussed. Finally, some alternative methods are presented.
Land Cover Classification
This is an introduction into the classification of land cover and land use from Earth observation data. The presentation includes:
- Land Use / Land Cover
- EO Image classification
- Land Cover Classification Systems
- Accuracy Assessment
The material is valid for all fields of Earth observation, not only Radar remote sensing.
SAR Processing and Data Analysis
This presentation is part of a four-part series on the basics on Synthetic Aperture Radar.
Learning Objectives:
- Understand Sentinel Data
- Perform image preprocessing
- Analyze SAR imagery to classify land and water
Erica Podest
NASA/Jet Propulsion Laboratory
Dr. Erika Podest is a scientist with the Carbon Cycle and Ecosystems Group at NASA’s Jet Propulsion Laboratory. Her research focuses on using Earth observing satellites, particularly microwave sensors, for characterizing and monitoring wetland ecosystems and seasonal freeze/thaw dynamics in the northern high latitudes as related to the global carbon and water cycles and climate change. She is working on the Soil Moisture Active Passive (SMAP) mission, a NASA Earth observing satellite that launched on Jan. 31 2015, which is improving our understanding of Earth’s water and carbon cycles and our ability to manage water resources. (Source: NASA ARSET)
Procesamiento y análisis de datos de SAR
Esta presentación es parte de una serie de cuatro tomos sobre los conceptos básicos del Radar de Apertura Sintética.
Al finalizar esta presentación los participantes podrán:
- Entender los parámetros fÃsicos de las imágenes SAR
- Describir la interacción de la señal de SAR con la superficie terrestre
- Describir los pasos necesarios para pre procesar las imágenes
- Entender la información que se puede extraer de las imágenes SAR
Erica Podest
NASA/Jet Propulsion Laboratory
Dr. Erika Podest is a scientist with the Carbon Cycle and Ecosystems Group at NASA’s Jet Propulsion Laboratory. Her research focuses on using Earth observing satellites, particularly microwave sensors, for characterizing and monitoring wetland ecosystems and seasonal freeze/thaw dynamics in the northern high latitudes as related to the global carbon and water cycles and climate change. She is working on the Soil Moisture Active Passive (SMAP) mission, a NASA Earth observing satellite that launched on Jan. 31 2015, which is improving our understanding of Earth’s water and carbon cycles and our ability to manage water resources. (Source: NASA ARSET)
SNAP – First Steps
What is SNAP?
ESA’s Sentinel Application Platform (SNAP) is a set of toolboxes, developed for the processing and analysis of Earth observation data.
This software is free to use and open source.
The following video tutorial gives you an introduction into the usage of the Sentinel-1 toolbox.
Download SNAP
You can either download SNAP from the Link above or use the direct links for your operating system below.
Ready? Let's go!
You can download the tutorial data directly here:
This is a direct link to download the data from the Copernicus Scihub (log in required).
Raysar
RaySAR is a 3D synthetic aperture radar (SAR) simulator which enables to generate SAR image layers related to detailed 3D object models. Moreover, it enables one to localize the 3D positions and surface intersection points related to reflected radar signals. In particular, RaySAR helps to understand the nature of signal multiple reflections at man-made objects (e.g. building structures) or artificial shapes. Scene models with different levels of detail can be processed – from digital surface models (DSMs) to high-end 3D structures – which can be defined in relative or absolute world coordinates. RaySAR can be run on Windows / Linux and is based on an adapted version of the open-source ray tracer POV-Ray.
Classification
The overall objective of the categorization of all pixels in a (SAR) image into semantically meaningful classes is one of the most conducted processing steps in image processing for geosciences. Thematic classification allocates pixels to classes based on functions of their spectral (or backscatter) properties. In this lesson parametric and non-parametric classification concepts are explained.
Change Detection
Change is a direct consequence of the physical composition of the Earth. The continous course of interconnected cycles causes alterations of the land surface. This lesson gives insight to methods and principles of change detection in remote sensing, both optical and radar based. It deals with the nature and types of change as well as image processing methods to detect change in RS images.
Data fusion
The concept of data fusion describes the combination of two or more images by means of a certain algorithm. This is done to achieve synergetic effects in order to gain more information from the fused product than the original images contain. This lesson presents general concepts of data fusion, describes different data fusion levels and shows existing fusion techniques in geo-scientific frameworks.