Methods

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.

Win 64-bit
Win 32-bit
Mac OS X
Unix 64-bit

Ready? Let's go!

You can download the tutorial data directly here:

Download Data

This is a direct link to download the data from the Copernicus Scihub (log in required).

Download Step-by-Step instruction

SAR Tomography

The module SAR Tomography introduces the advanced technique of combining multiple radar images from several viewing angles into a new dimension of information. Although tomogoraphic measuring systems and applications are recently in an experimental stage, this technology has a large potential to enhance the information content of radar data. This module gives an overview of the basics, limitations, mathematical foundations, advanced techniques and recent case studies.

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.

Get RaySAR
Read RaySAR publications

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.

Object based image analysis

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.

Texture

Image texture is an important feature in image interpretation in optical as well as radar remote sensing. This lesson gives insights into the concept of texture, ways to measure and interpret it. Furthermore, some application examples of texture in radar remote sensing are given for the demonstration of its usability as measure to quantify land cover information.

DEM generation with Python

SAR interferometry is widely used for the generation of digital elevation models (DEM). The topographic information is derived by exploiting the phase differences of the coherent radar signal from 2 complex-valued SAR images, which are acquired from slightly different orbit positions. After focusing the raw SAR data, three essential steps are the main components of the standard InSAR processing:

  1. Co-Registration of the two complex images
  2. Interferogram generation and coherence estimation
  3. Phase unwrapping

In this tutorial the processing is conducted with the open source programming language Python. All data and instruction is included in the downloadable ZIP-File.