The Modular Aerial Camera System (MACS) is a development platform for optical remote sensing concepts, algorithms and special environments. For Real-Time Services for Maritime Security (EMSec joint project) a new multi-sensor configuration MACS-Mar was realized. It consists of 4 co-aligned sensor heads in the visible RGB, near infrared (NIR, 700-950 nm), hyperspectral (HS, 450-900 nm) and thermal infrared (TIR, 7.5…14 µm) spectral range, a mid-cost GNSS/INS system, a processing unit and two data links. On-board image projection, cropping of redundant data and compression enable the instant generation of direct-georeferenced high resolution image mosaics, automatic object detection, vectorization and annotation of floating objects on the water surface. The results were transmitted over a distance up to 50 km in real-time via narrow and broadband data links and were visualized in a maritime situation awareness system. For the automatic onboard detection of objects a segmentation and classification workflow based on RGB, NIR and TIR information was developed and tested in September 2016. The completeness of the object detection in the experiment resulted in 95 %, the correctness in 53 %. Mostly bright backwash of ships led to overdetection of the number of objects, further refinement using water homogeneity in the TIR, as implemented in the workflow, couldn't be carried out due to problems with the TIR sensor. To analyze the influence of high resolution TIR imagery and to reach the expected detection quality a further experiment was conducted in August 2017. Adding TIR images the completeness was increased to 98 % and the correctness to 74 %.

ABSTRACT:In this paper we propose a new algorithm for digital terrain (DTM) model reconstruction from very high spatial resolution digital surface models (DSMs). It represents a combination of multi-directional filtering with a new metric which we call normalized volume above ground to create an above-ground mask containing buildings and elevated vegetation. This mask can be used to interpolate a ground-only DTM. The presented algorithm works fully automatically, requiring only the processing parameters minimum height and maximum width in metric units. Since slope and breaklines are not decisive criteria, low and smooth and even very extensive flat objects are recognized and masked. The algorithm was developed with the goal to generate the normalized DSM for automatic 3D building reconstruction and works reliably also in environments with distinct hillsides or terrace-shaped terrain where conventional methods would fail. A quantitative comparison with the ISPRS data sets Potsdam and Vaihingen show that 98-99% of all building data points are identified and can be removed, while enough ground data points (~66%) are kept to be able to reconstruct the ground surface. Additionally, we discuss the concept of size dependent height thresholds and present an efficient scheme for pyramidal processing of data sets reducing time complexity to linear to the number of pixels, !(#$).

The modular aerial camera system (MACS) is a development platform for optical remote sensing concepts, algorithms and special environments. For real-time services for maritime security (EMSec joint project), a new multi-sensor configuration MACS-Mar was realized. It consists of four co-aligned sensor heads in the visible RGB, near infrared (NIR, 700-950 nm), hyperspectral (HS, 450-900 nm) and thermal infrared (TIR, 7.5-14 µm) spectral range, a mid-cost navigation system, a processing unit and two data links. On-board image projection, cropping of redundant data and compression enable the instant generation of direct-georeferenced high-resolution image mosaics, automatic object detection, vectorization and annotation of floating objects on the water surface. The results were transmitted over a distance up to 50 km in real-time via narrow and broadband data links and were visualized in a maritime situation awareness system. For the automatic onboard detection of floating objects, a segmentation and classification workflow based on RGB, IR and TIR information was developed and tested. The completeness of the object detection in the experiment resulted in 95%, the correctness in 53%. Mostly, bright backwash of ships lead to an overestimation of the number of objects, further refinement using water homogeneity in the TIR, as implemented in the workflow, couldn't be carried out due to problems with the TIR sensor, else distinctly better results could have been expected. The absolute positional accuracy of the projected real-time imagery resulted in 2 m without postprocessing of images or navigation data, the relative measurement accuracy of distances is in the range of the image resolution, which is about 12 cm for RGB imagery in the EMSec experiment.

An Empirical Line Model (ELM) was tested to correct Sentinel-2A (MSI) images acquired in the tropical archipelago of San Andrés, Colombia. This approach uses a linear regression to model the relationship between the average ground reflectance and radiance on bands 2, 3, 4, and 8, for 32 spectrally homogeneous targets. The model was validated from eight targets measured on different land-covers trough the estimated coefficient of determination R 2. The result of the prediction equations observed was high, with a value of R 2 :0.91 performed at the 0.01 level of significance for the four wavebands, against the R 2 :0.77 of SEN2COR and R 2 :0.81 of ATCOR Correction Models. Complementary, a quantitative approach was proposed to determine the suitability of the ELM, based on the spectral response on six land-covers types for every band after correction. A separability index (M) was used from a set of independent targets to estimate the effectiveness of spectral classification of land-covers. The more evident results of the correction are on the vegetation cover in the NIR band (785-900 nm), where the ELM has 55% and 58% more separability than the SEN2COR and ATCOR models, respectively. Additionally, the absolute difference between the Top-of-Atmosphere (TOA) and Bottom-of-Atmosphere (BOA) images was calculated, finding the highest differences in the NIR band with 0.094 in the L1C-TOA reflectance image, and 0.013 in the ELM-BOA image. Finally, a sensitivity analysis on the Normalized Difference Vegetation Index (NDVI) to estimate the performance of the spectral response of ELM on vegetation cover was employed.

ABSTRACT:Natural disasters as well as major man made incidents are an increasingly serious threat for civil society. Effective, fast and coordinated disaster management crucially depends on the availability of a real-time situation picture of the affected area. However, in situ situation assessment from the ground is usually time-consuming and of limited effect, especially when dealing with large or inaccessible areas. A rapid mapping system based on aerial images can enable fast and effective assessment and analysis of medium to large scale disaster situations. This paper presents an integrated rapid mapping system that is particularly designed for real-time applications, where comparatively large areas have to be recorded in short time. The system includes a lightweight camera system suitable for UAV applications and a software tool for generating aerial maps from recorded sensor data within minutes after landing. The paper describes in particular which sensors are applied and how they are operated. Furthermore it outlines the procedure, how the aerial map is generated from image and additional gathered sensor data.