This work analyzes the applicability of using hyperspectral data for ship classification in coastal or harbor environment. An approach for hyperspectral feature selection based on bag-of-words method was developed. Nearest neighbor and random forest classifiers were used for evaluating hyperspectral bag-of-words features. The evaluation dataset was self-acquired at the Kiel Harbor in Germany, using Aisa Eagle in VNIR and Aisa Hawk in SWIR sensors. The dataset included 547 samples of 72 objects ranging from passenger ferries to sailing boats in different illumination conditions. An object library was created from the dataset and bag-of-words features were extracted. Two different separation strategies for separating training and test sets were selected: Random subsets and chronologically separated subsets. Chronological separation was more challenging than the random separation for both classifiers. In order to allow a future sliding window operation for object detection, the training and the classification were performed additionally on rectangular windows including background pixels. The performance of nearest neighbor classifier dropped whereas the performance of random forest classifier slightly improved. Overall performance of random forest classifier is better than nearest neighbor classifier; however it requires a more comprehensive dataset for training. The evaluations indicated that the bag-of-words feature selection is feasible for the given application.
KEYWORDS: Sensors, Cameras, 3D acquisition, Reconnaissance systems, Data acquisition, Calibration, 3D image processing, RGB color model, Clouds, Imaging systems
In modern aerial Intelligence, Surveillance and Reconnaissance operations, precise 3D information becomes inevitable for increased situation awareness. In particular, object geometries represented by texturized digital surface models constitute an alternative to a pure evaluation of radiometric measurements. Besides the 3D data's level of detail aspect, its availability is time-relevant in order to make quick decisions.
Expanding the concept of our preceding remote sensing platform developed together with OHB System AG and Geosystems GmbH, in this paper we present an airborne multi-sensor system based on a motor glider equipped with two wing pods; one carries the sensors, whereas the second pod downlinks sensor data to a connected ground control station by using the Aerial Reconnaissance Data System of OHB. An uplink is created to receive remote commands from the manned mobile ground control station, which on its part processes and evaluates incoming sensor data. The system allows the integration of efficient image processing and machine learning algorithms.
In this work, we introduce a near real-time approach for the acquisition of a texturized 3D data model with the help of an airborne laser scanner and four high-resolution multi-spectral (RGB, near-infrared) cameras. Image sequences from nadir and off-nadir cameras permit to generate dense point clouds and to texturize also facades of buildings. The ground control station distributes processed 3D data over a linked geoinformation system with web capabilities to off-site decision-makers. As the accurate acquisition of sensor data requires boresight calibrated sensors, we additionally examine the first steps of a camera calibration workflow.
Areas occupied by oil pipelines and storage facilities are prone to severe contamination due to leaks caused by natural forces, poor maintenance or third parties. These threats have to be detected as quickly as possible in order to prevent serious environmental damage. Periodical and emergency monitoring activities need to be carried out for successful disaster management and pollution minimization. Airborne remote sensing stands out as an appropriate choice to operate either in an emergency or periodically. Hydrocarbon Index (HI) and Hydrocarbon Detection Index (HDI) utilize the unique absorption features of hydrocarbon based materials at SWIR spectral region. These band ratio based methods require no a priori knowledge of the reference spectrum and can be calculated in real time. This work introduces a flexible airborne pipeline monitoring system based on the online quasi-operational hyperspectral remote sensing system developed at Fraunhofer IOSB, utilizing HI and HDI for oil leak detection on the data acquired by an SWIR imaging sensor. Robustness of HI and HDI compared to state of the art detection algorithms is evaluated in an experimental setup using a synthetic dataset, which was prepared in a systematic way to simulate linear mixtures of selected background and oil spectra consisting of gradually decreasing percentages of oil content. Real airborne measurements in Ettlingen, Germany are used to gather background data while the crude oil spectrum was measured with a field spectrometer. The results indicate that the system can be utilized for online and offline monitoring activities.
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