Real Time Airborne Monitoring for Disaster and Traffic Applications

Remote sensing applications like disaster or mass event monitoring need the acquired data and extracted information within a very short time span. Airborne sensors can acquire the data quickly and on-board processing combined with data downlink is the fastest possibility to achieve this requirement. For this purpose, a new low-cost airborne frame camera system has been developed at the German Aerospace Center (DLR) named 3K-camera. The pixel size and swath width range between 15 cm to 50 cm and 2.5 km to 8 km respectively. Within two minutes an area of approximately 10 km x 8 km can be monitored. Image data are processed onboard on five computers using data from a real time GPS/IMU system including direct georeferencing. Due to high frequency image acquisition (3 images/second) the monitoring of moving objects like vehicles and people is performed allowing wide area detailed traffic monitoring

Tree Species Classification by Fusing of very high-resolution Hyperspectral Images and 3K-DSM

Tree species information is crucial in sectors such as forest management and nature conservation. It is often required over a large area. In this study, tree species classification was performed using hyperspectral data and the Digital Surface Model generated from DLR-3K aerial borne stereo camera System. In the classification step, pixelbased approach and the patch-based approach with Bag-ofWord (BoW) model were proposed and tested. The two approaches have been performed in the Kranzberg Forest near Munich, Germany. The comparison was taken in a statistical way. By using proper features combination, the pixel-based classification can achieve very high accuracy (Kappa =0.95), while the patch-based method only has accuracy around 60%

Development of a new Middleware for Real Time Image Processing in Remote Sensing

At the German Aerospace Center (DLR) a new project aiming at the efficient control of traffic and at providing help in cases of natural disaster is being developed which is supposed to transmit pre-processed images of high resolution from a plane to a station on the ground. Aboard the aircraft a computer network is installed which is to process these images shot at short intervals. This intensive computational work is done with the latest methods of image processing and is allocated to several modules running on different computers. The process of this computationally intensive work on highly correlated modules requires a permanent exchange of small and big amounts of data of totally different data types. Here a middleware is needed, which guarantees easy and efficient communication between the modules and simultaneously hides their spatial distribution from them. The first part of this thesis analyses to what extent the process of exchange of small and big amounts of data is supported by existing middlewares. Then the new middleware DANAOS, which is developed in this thesis, is presented, a middleware which compared with other middlewares not only guarantees an efficient communication between the modules using message passing but also supports the exchange of big amounts of image data using a distributed shared memory. For this purpose the module programmer is offered an application programming interface, which - in addition to this - provides further services for group communication and name service

Airborne Crowd Density Estimation

This paper proposes a new method for estimating human crowd densities from aerial imagery. Applications benefiting from an accurate crowd monitoring system are mainly found in the security sector. Normally crowd density estimation is done through in-situ camera systems mounted on high locations although this is not appropriate in case of very large crowds with thousands of people. Using airborne camera systems in these scenarios is a new research topic. Our method uses a preliminary filtering of the whole image space by suitable and fast interest point detection resulting in a number of image regions, possibly containing human crowds. Validation of these candidates is done by transforming the corresponding image patches into a low-dimensional and discriminative feature space and classifying the results using a support vector machine (SVM). The feature space is spanned by texture features computed by applying a Gabor filter bank with varying scale and orientation to the image patches. For evaluation, we use 5 different image datasets acquired by the 3K+ aerial camera system of the German Aerospace Center during real mass events like concerts or football games. To evaluate the robustness and generality of our method, these datasets are taken from different flight heights between 800 m and 1500 m above ground (keeping a fixed focal length) and varying daylight and shadow conditions. The results of our crowd density estimation are evaluated against a reference data set obtained by manually labeling tens of thousands individual persons in the corresponding datasets and show that our method is able to estimate human crowd densities in challenging realistic scenarios

Real time camera system for disaster and traffic monitoring

A real time airborne monitoring system for monitoring of natural disasters, mass events, and large traffic disasters was developed in the last years at the German Aerospace Center (DLR). This system consists of an optical wide-angle camera system (3K system), a SAR sensor, an optical and microwave data downlink, an onboard processing unit and ground processing station with online data transmission to different end user portals. The development of the real time processing chain from the data acquisition to the ground station is still a very challenging task. In this paper, an overview of all relevant parts of the airborne optical mapping system is given and selected system processes are addressed and described in more detail. The experiences made in the flight campaigns of the last years are summarized with focus on the image processing part, e.g. reached accuracies of georeferencing and status of the traffic processors

Real Time Airborne Monitoring for Disaster and Traffic Applications

Remote sensing applications like disaster or mass event monitoring need the acquired data and extracted information within a very short time span. Airborne sensors can acquire the data quickly and on-board processing combined with data downlink is the fastest possibility to achieve this requirement. For this purpose, a new low-cost airborne frame camera system has been developed at the German Aerospace Center (DLR) named 3K-camera. The pixel size and swath width range between 15 cm to 50 cm and 2.5 km to 8 km respectively. Within two minutes an area of approximately 10 km x 8 km can be monitored. Image data are processed onboard on five computers using data from a real time GPS/IMU system including direct georeferencing. Due to high frequency image acquisition (3 images/second) the monitoring of moving objects like vehicles and people is performed allowing wide area detailed traffic monitoring

A real-time optical airborne road traffic monitoring system

In the last years, the Remote Sensing Technology Institute of the German Aerospace Center has been developing a real-time airborne monitoring system which can be used during natural disasters, mass events and huge traffic jams. This system consists of a cost-effective optical camera system, onboard computers for real-time processing and a datalink for transmitting the traffic information and aerial images to the ground station. The traffic information is automatically extracted from the images by detecting the cars and tracking them within a few frames. Results are the position and the speed of each cars, which are written into a database on the ground. In this paper we give a description of the overall system with focus on the automatic vehicle detection

Real-time mapping from a helicopter with a new optical sensor system

Ein neues optisches echtzeitfähiges real-time Sensorsystem auf einem Hubschrauber (4k System) für Einsätze bei Katastrophen, Großereignissen und anderen Überwachungsaufgaben ist nun als Prototyp einsatzfähig. Der Sensor wurde gewichtsoptimiert, klein und mit preisgünstigen Bauteilen in einem Pylon seitlich am Hubschrauber konzipiert. Es mussten jedoch aufgrund der geforderten Funktionalität und der Echtzeitfähigkeit real-time Fähigkeit hier Kompromisse eingegangen werden. Integriert sind neben der automatischen Orthophotoerstellung verschiedene thematische Prozessoren wie z.B. die automatische Verkehrsdatenextraktion. Der Sensor ist mit der neuesten Generation handelsüblicher High-End Kleinbildkameras bestückt, die je nach Anwendung konfiguriert werden können. Dabei decken die Kameras bestückt mit 50mm Objektiven ein Blickfeld von bis zu 104° ab, beim Einsatz von 100mm Objektiven werden Auflösungen bis zu 3.5 cm bei einer Flughöhe von 500m ü.G. erreicht. Zusätzlich kann mit einer Kamera der nahe Infrarotbereich erfasst werden oder ein hochaufgelöstes Video (4k Video) aufgenommen werden. In diesem Paper werden die Systemkomponenten, die technischen Eigenschaften des Sensors beschrieben sowie die verschiedenen Einsatzmöglichkeiten des Systems skizziert

Efficient Image Data Processing based on an Airborne Distributed System Architecture

This paper examines and describes improvements of the processing time of an airborne wide area monitoring system which is under development at the German Aerospace Center (DLR). Aboard the aircraft a computer network equipped with three off-the-shelf cameras acquires images at intervals of up to 3fps. After orthorectification a traffic processor runs an automatic road traffic-data extraction and sends its result to a receiving ground station via an S-Band radio link. The processed results must be available as fast as possible. Hence, processing times of the computationally intensive tasks are meassured. In this context it turned out to consider not only the pure processing times but also the copy operations of the images between the modules. We compare several interprocess communication mechanisms and discuss the results. With the usage of relatively simple shared memory concepts a significant speed-up can be reached

An Operational System for Estimating Road Traffic Information from Aerial Images

Given that ground stationary infrastructures for traffic monitoring are barely able to handle everyday traffic volumes, there is a risk that they could fail altogether in situations arising from mass events or disasters. In this work, we present an alternative approach for traffic monitoring during disaster and mass events, which is based on an airborne optical sensor system. With this system, optical image sequences are automatically examined on board an aircraft to estimate road traffic information, such as vehicle positions, velocities and driving directions. The traffic information, estimated in real time on board, is immediately downlinked to a ground station. The airborne sensor system consists of a three-head camera system, a real-time-capable GPS/INS unit, five industrial PCs and a downlink unit. The processing chain for automatic extraction of traffic information contains modules for the synchronization of image and navigation data streams, orthorectification and vehicle detection and tracking modules. The vehicle detector is based on a combination of AdaBoost and support vector machine classifiers. Vehicle tracking relies on shape-based matching operators. The processing chain is evaluated on a large number of image sequences recorded during several campaigns, and the data quality is compared to that obtained from induction loops. In summary, we can conclude that the achieved overall quality of the traffic data extracted by the airborne system is in the range of 68% and 81%. Thus, it is comparable to data obtained from stationary ground sensor networks