This is an introduction into the classification of land cover and land use from Earth observation data. The presentation includes:
The material is valid for all fields of Earth observation, not only Radar remote sensing.
This presentation is part of a four-part series on the basics on Synthetic Aperture Radar.
Learning Objectives:
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)
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:
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)
ESA’s Sentinel Application Platform (SNAP) is a set of toolboxes, developed for the processing and analysis of Earth observation data.
The following video tutorial gives you an introduction into the usage of the Sentinel-1 toolbox.
You can either download SNAP from the Link above or use the direct links for your operating system below.
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 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.
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 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.
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.
The complete partioning of a remote sensing scene into non-overlapping regions/segments/objects is a strongly developing concept in the field of image interpretation. The lesson on “Object based image analysis” deals with the principles of image object formation in geosciences. Several segmentation approaches are presented and compared. Furthermore, underlying principles of object based classification approaches are outlined.
Speckle is a common phenomenon in SAR data, which has its origin in the radar image acquisition principles. In almost all applications in geosciences speckle is considered as noise. Therefore, many approaches for the reduction of speckle exist. This lessons gives insight into the origin of speckle and an overview of concepts and approaches to distinguish between speckle and relevant information content.
