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中国科学院博士 主要从事遥感机理、定量反演、数据处理以及GIS应用研究。ArcGIS、Envi 、ERDAS、Ecognition软件、IDL语言、6S、SAIL

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EnMAP-Box(Environmental Mapping and Analysis Program)  

2013-06-19 21:20:21|  分类: 遥感 |  标签: |举报 |字号 订阅

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http://www.enmap.org/mission_statement

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EnMAP简介

EnMAP (Environmental Mapping and Analysis Program) is a German hyperspectral satellite mission providing high quality hyperspectral image data on a timely and frequent basis. The main objective is to investigate a wide range of ecosystem parameters encompassing agriculture, forestry, soil and geological environments, coastal zones and inland waters. This will significantly increase our understanding of coupled biospheric and geospheric processes and thus, enable the management and ensure the sustainability of our vital resources. The envisaged launch of the EnMAP satellite is 2015.

EnMAP参数

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  • Dedicated imaging pushbroom hyperspectral sensor mainly based on modified existing or pre-developed technology

  • Broad spectral range from 420 nm to 1000 nm (VNIR) and from 900 nm to 2450 nm (SWIR) with high radiometric resolution and stability in both spectral ranges

  • Swath width 30km at high spatial resolution of 30 m x 30 m and off-nadir (30°) pointing feature for fast target revisit (4 days)

  • Sufficient on-board memory to acquire 1.000 km swath length per orbit and a total of 5.000 km per day.

EnMAP软件

http://indus.caf.dlr.de/forum/

Presently,EnMAP-Box 1.4 available for download

The EnMAP Box is a platform-independent software interface designed to process hyperspectral remote sensing data, and particularly developed to handle data from the EnMAP sensor.

Main features include:

  • easy-to-use GUI, with drop-down menus, expandable tree-based file explorer and drag-and-drop capabilities
  • in-built modules aimed at the processing of hyperspectral data, such as the Savitzky-Golay smoothing filter of spectral data and Support Vector Machines classification or regression of image data
  • easy incorporation of external modules for EnMAP data processing developed by other research groups
  • twined image and spectral panels, specifically designed for the import, visualization and processing of hyperspectral image data and field/laboratory spectra
  • Import and export from and to different data formats

The EnMAP-Box can be used with any IDL 8.0.1/8.1 runtime environment. This allows you to add the EnMAP-Box to an existing ENVI 4.8 installation.

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imageSVM--Support Vector Machines for Classification and Regression (provided by HU Berlin)

ImageSVM is an IDL based tool for the support vector machine (SVM) classification and regression analysis of remote sensing image data. Its workflow allows a flexible and transparent use of the support vector concept for both simple and advanced classification/regression approaches. The goal of imageSVM is to advance the use of the support vector concept in the field of remote sensing image analysis.

imageSVM SVC Manual, imageSVM SVC Tutorial

imageSVM SVR Manual, imageSVM SVR Tutorial

imageSVM Tutorial Data

imageRF --Random Forests for Classification and Regression (provided by Uni Bonn and HU Berlin)

imageRF is an IDL based tool for the supervised classification and regression analysis of remote sensing image data. It implements the machine learning approach of Random Forests? (RF) (Breiman, L & Cutler, A, 2011) that uses multiple self-learning decision trees to parameterize models and use them for estimating categorical or continuous variables.

imageRF Manual, imageRF API

Waske et al. 2012: imageRF – A user oriented implementation for remote sensing image analysis with Random Forests

autoPLSR --Partial Least Squares Regression (provided by Uni Bonn)

The aim of the autoPLSR is to provide a software tool with automatic feature and latent variable selection for the Partial Least Squares Regression (PLSR: Wold et al. (2001)), a multivariate regression method that is widely used in chemometrics, hyperspectral remote sensing, bioinformatics and other fields.

Auto PLSR Manual

SpInMine--Spectral Index Data Mining Tool (provided by Uni Trier)

SpInMine (Spectral Index Data Mining Tool) is a tool for finding the optimal index of two narrow bands for a regression problem.

SpInMine_Manual

ASE---Advanced Statistical Evaluator (ASE) (provided by LMU München)

The objective of the ASE is to provide for remote sensing practitioners (i.e., non-statisticians) guidance for model evaluation. An optimal set of statistical measures is proposed for the quantitative assessment of model performance in the context of vegetation biophysical variable retrieval from Earth observation (EO) data.

ASE Description

ASI--Analyze Spectral Integral (provided by LMU München)

The Analyze Spectral Integral (ASI) is based on the concept of continuum removal, an approach commonly applied in the chemical sciences for the determination of mixture component concentrations. This approach has been developed as an alternative to simple Vegetation Indices.

ASI Description

AVI---Agricultural Vegetation Indices (AVI) (provided by LMU München)

The module AVI is a collection of 66 Vegetation Indices (VI) selected from an extensive literature survey.

AVI Description

imageMath---imageMath (provided by HU Berlin)

Calculator for spatial and spectral image processing functionality with look-and-feel of a common hand-held calculator.

imageMath Manual

imageMath API

LibMix---Linear Mixtures of Spectral Libraries (provided by HU Berlin)

The libMix Application provides a simple way to generate synthetically and binary mixtures of spectral profiles. Its aim is to simulate the spectral mixing gradients, e.g. to estimate fractional content of specific land cover classes.

LibMix Manual

MaxEntWrapper--Maximum Entropy Analysis (provided by Uni Bonn)

The MaxEnt-Wrapper is an IDL based wrapper for the Java based program MaxEnt written by Steven Phillips, Miro Dudik and Rob Schapire for the maximum entropy analysis of remote sensing image data.

The MaxEnt-Wrapper provides users of the EnMap-Box/ENVI to use remote sensing data within MaxEnt without converting the data itself.?

MaxEntWrapper Manual

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