There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

On robust estimation of the location parameter

A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

For 11 days in February 2000, the Shuttle Radar Topography Mission (SRTM) successfully recorded by interferometric synthetic aperture radar (InSAR) data of the entire land mass of the earth between 60°N and 57°S. The data acquired in C- and X-bands are processed into the first global digital elevation models (DEMs) at 1 arc sec resolution, by NASA-JPL and German aerospace center (DLR), respectively. From the perspective of the SRTM-X system, we give in this paper an overview of the mission and the DEM production, as well as an evaluation of the DEM product quality. Special emphasis is on challenges and peculiarities of the processing that arose from the unique design of the SRTM system, which has been the first single-pass interferometer in space.

The shuttle radar topography mission—a new class of digital elevation models acquired by spaceborne radar

. In this paper we propose a cloud removal algorithm for scenes within a Sentinel-2 satellite image time series based on synthetisation of the affected areas via sparse reconstruction. For this purpose, a clouds and clouds shadow mask must be given. With respect to previous works, the process has an increased automation degree. Several dictionaries, on the basis of which the data are reconstructed, are selected randomly from cloud-free areas around the cloud, and for each pixel the dictionary yielding the smallest reconstruction error in non-corrupted images is chosen for the restoration. The values below a cloudy area are therefore estimated by observing the spectral evolution in time of the non-corrupted pixels around it. The proposed restoration algorithm is fast and efficient, requires minimal supervision and yield results with low overall radiometric and spectral distortions.

/pdf/cloud-removal-from-sentinel-2-image-time-series-through-23isqxk41s.pdf

Cloud Removal from Sentinel-2 Image Time Series through Sparse Reconstruction from Random Samples

Fusion of optical and radar remote sensing data is becoming an actual topic recently in various application areas though the results are not always satisfactory. In this paper we analyze some disturbing aspects of fusing orthoimages from sensors having different acquisition geometries. These aspects are errors in DEM used for image orthorectification and existence of 3D objects in the scene. We analyze how these effects influence a ground displacement in orthoimages produced from optical and radar data. Further, we propose a sensor formation with acquisition geometry parameters which allows to minimize or compensate for ground displacements in different orthoimages due the above mentioned effects and to produce good prerequisites for the following fusion for specific application areas e.g. matching, filling data gaps, classification etc. To demonstrate the potential of the proposed approach two pairs of optical-radar data were acquired over the urban area – Munich city, Germany. The first collection of WorldView-1 and TerraSAR-X data followed the proposed recommendations for acquisition geometry parameters, whereas the second collection of IKONOS and TerraSAR-X data was acquired with accidental parameters. The experiment fully confirmed our ideas. Moreover, it opens new possibilities for optical and radar image fusion.

/pdf/fusion-of-optical-and-radar-remote-sensing-data-munich-city-1mt6nlne92.pdf

Fusion of optical and radar remote sensing data: munich city example

The DLR Earth Sensing Imaging Spectrometer (DESIS) is a space-based instrument installed and operated on the International Space Station (ISS) [1]. This space mission is the achievement of the collaboration between the German Aerospace Center (DLR) and the US company Teledyne Brown Engineering (TBE). DLR has developed the instrument and the software for data processing [2], while TBE provides the Multi-User System for Earth Sensing (MUSES) platform, where DESIS is installed, and the infrastructure for operation and data tasking [3].

/pdf/the-spaceborne-imaging-spectrometer-desis-data-access-and-22dml0d9zo.pdf

The Spaceborne Imaging Spectrometer Desis: Data Access and Scientific Applications

Nach dem Kosovo-Krieg im vergangenen Jahr ist der Wiederaufbau und damit verbunden die langfristige Integration der Balkanregion zu einem zentralen Thema der EU geworden. Neben den sehr zeitkritischen humanitaren Aufgaben sind mittel- und langfristige Planungen durchzufuhren, fur die geographisch exakt zugeordnete Datenbestande von grosem Nutzen sind. Aus diesen Grunden wurde ein Prototyp eines raumbezogenen Informationssystems fur die Unterstutzung des Wiederaufbaus des Kosovo erarbeitet, welches hier vorgestellt wird. Als Grundlage dienen Fernerkundungsdaten fur die thematische Kartierung und die Gewinnung von digitalen Hohenmodellen. Weitere eingesetzte Technologien sind Satellitennavigation fur die Positionierung, Telekommunikation und das Internet fur den Datentransfer. Zusammen mit den durch traditionelle Methoden akquirierten Informationen werden diese Daten in einem Geographischen Informationssystem bearbeitet, abgefragt und visualisiert, so dass sie eine sofort nutzbare Informationsquelle fur Aufbau- und Entscheidungsprozesse darstellen.

Ein raumbezogenes Informationssystem für den Wiederaufbau des Kosovo

In the post crisis phase a much more detailed analysis can be done with higher accuracy and less pressure of time compared to the general situation assessment of the rapid mapping process directly after the crisis. In this investigation the analysis is concentrated on the urban area of the capital town of Port-au-Prince. In order to develop a service for detailed damage assessment, methods of (semi-)automatic change detection are used and compared, since up to now, damage information was mainly derived by visual interpretation. Any improvement in terms of accuracy and speed of analysis is of relevance to users in this context. The results of the different change detection algorithms achieved using the Haiti datasets are compared to each other and also to the database of the Haiti Action Plan for Reconstruction and Development (PDNA). The results are very promising although further improvements have to be made.

/pdf/detailed-damage-assessment-after-the-haiti-earthquake-25j46fdvvf.pdf

Peter Reinartz

Papers

Cloud Removal from Sentinel-2 Image Time Series through Sparse Reconstruction from Random Samples

Fusion of optical and radar remote sensing data: munich city example

The Spaceborne Imaging Spectrometer Desis: Data Access and Scientific Applications

Ein raumbezogenes Informationssystem für den Wiederaufbau des Kosovo

Detailed Damage Assessment After the Haiti Earthquake