A geometric approach to archetypal analysis and non-negative matrix factorization

Archetypal analysis and non-negative matrix factorization (NMF) are staples in a statisticians toolbox for dimension reduction and exploratory data analysis. We describe a geometric approach to both NMF and archetypal analysis by interpreting both problems as finding extreme points of the data cloud. We also develop and analyze an efficient approach to finding extreme points in high dimensions. For modern massive datasets that are too large to fit on a single machine and must be stored in a distributed setting, our approach makes only a small number of passes over the data. In fact, it is possible to obtain the NMF or perform archetypal analysis with just two passes over the data.Comment: 36 pages, 13 figure

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

The EnMAP user interface and user request scenarios

EnMAP (Environmental Mapping and Analysis Program) is a German hyperspectral satellite mission providing high quality hyperspectral image data on a timely and frequent basis. Main objective is to investigate a wide range of ecosystem parameters encompassing agriculture, forestry, soil and geological environments, coastal zones and inland waters. The EnMAP Ground Segment will be designed, implemented and operated by the German Aerospace Center (DLR). The Applied Remote Sensing Cluster (DFD) at DLR is responsible for the establishment of a user interface. This paper provides details on the concept, design and functionality of the EnMAP user interface and a first analysis about potential user scenarios. The user interface consists of two online portals. The EnMAP portal (www.enmap.org) provides general EnMAP mission information. It is the central entry point for all international users interested to learn about the EnMAP mission, its objectives, status, data products and processing chains. The EnMAP Data Access Portal (EDAP) is the entry point for any EnMAP data requests and comprises a set of service functions offered for every registered user. The scientific user is able to task the EnMAP HSI for Earth observations by providing tasking parameters, such as area, temporal aspects and allowed tilt angle. In the second part of that paper different user scenarios according to the previously explained tasking parameters are presented and discussed in terms of their feasibility for scientific projects. For that purpose, a prototype of the observation planning tool enabling visualization of different user request scenarios was developed. It can be shown, that the number of data takes in a certain period of time increases with the latitude of the observation area. Further, the observation area can differ with the tilt angle of the satellite. Such findings can be crucial for the planning of remote sensing based projects, especially for those investigating ecosystem gradients in the time domain