Mathias Bochow

Reduction of Radiometric Miscalibration—Applications to Pushbroom Sensors

The analysis of hyperspectral images is an important task in Remote Sensing. Foregoing radiometric calibration results in the assignment of incident electromagnetic radiation to digital numbers and reduces the striping caused by slightly different responses of the pixel detectors. However, due to uncertainties in the calibration some striping remains. This publication presents a new reduction framework that efficiently reduces linear and nonlinear miscalibrations by an image-driven, radiometric recalibration and rescaling. The proposed framework—Reduction Of Miscalibration Effects (ROME)—considering spectral and spatial probability distributions, is constrained by specific minimisation and maximisation principles and incorporates image processing techniques such as Minkowski metrics and convolution. To objectively evaluate the performance of the new approach, the technique was applied to a variety of commonly used image examples and to one simulated and miscalibrated EnMAP (Environmental Mapping and Analysis Program) scene. Other examples consist of miscalibrated AISA/Eagle VNIR (Visible and Near Infrared) and Hawk SWIR (Short Wave Infrared) scenes of rural areas of the region Fichtwald in Germany and Hyperion scenes of the Jalal-Abad district in Southern Kyrgyzstan. Recovery rates of approximately 97% for linear and approximately 94% for nonlinear miscalibrated data were achieved, clearly demonstrating the benefits of the new approach and its potential for broad applicability to miscalibrated pushbroom sensor data.

On the Use of Airborne Imaging Spectroscopy Data for the Automatic Detection and Delineation of Surface Water Bodies

There is economical and ecological relevance for remote sensing applications of inland and coastal waters: The European Union Water Framework Directive (European Parliament and the Council of the European Union, 2000) for inland and coastal waters requires the EU member states to take actions in order to reach a good ecological status in inland and coastal waters by 2015. This involves characterization of the specific trophic state and the implementation of monitoring systems to verify the ecological status. Financial resources at the national and local level are insufficient to assess the water quality using conventional methods of regularly field and laboratory work only. While remote sensing cannot replace the assessment of all aquatic parameters in the field, it powerfully complements existing sampling programs and offers the base to extrapolate the sampled parameter information in time and in space. The delineation of surface water bodies is a prerequisite for any further remote sensing based analysis and even can by itself provide up-to-date information for water resource management, monitoring and modelling (Manavalan et al., 1993). It is further important in the monitoring of seasonally changing water reservoirs (e.g., Alesheikh et al., 2007) and of shortterm events like floods (Overton, 2005). Usually the detection and delineation of surface water bodies in optical remote sensing data is described as being an easy task. Since water absorbs most of the irradiation in the near-infrared (NIR) part of the electromagnetic spectrum water bodies appear very dark in NIR spectral bands and can be mapped by simply applying a maximum threshold on one of these bands (Swain & Davis, 1978: section 5-4). Many studies took advantage of this spectral behaviour of water and applied methods like single band density slicing (e.g., Work & Gilmer, 1976), spectral indices (McFeeters, 1996, Xu, 2006) or multispectral supervised classification (e.g., Frazier & Page, 2000, Lira, 2006). However, all of these methods have the drawback that they are not fully automated since the analyst has to select a scene-specific threshold (Ji et al., 2009) or training pixels. Moreover there are certain situations where these methods lead to misclassification. For instance, water constituents in turbid water as well as water bottom reflectance and sun glint can raise the reflectance spectrum of surface water even in the NIR spectral range up to a reflectance level which is typical for dark surfaces on land such as dark rocks (e.g., basalt, lava), bituminous roofing materials and in particular shadow regions. Consequently, Carleer & Wolff (2006) amongst others found the land cover classes water and shadow to be highly confused in image classifications. This problem especially occurs in environments where both, a high amount of shadow and water regions can exist, such as urban landscapes, mountainous landscapes or cliffy coasts as well as generally in images with water bodies and cloud shadows. In this investigation we focus on the development of a new surface water body detection algorithm that can be automatically applied without user knowledge and supplementary data on any hyperspectral image of the visible and near-infrared (VNIR) spectral range. The analysis is strictly focused on the VNIR part of the electromagnetic spectrum due to the growing number of VNIR imaging spectrometers. The developed approach consists of two main steps, the selection of potential water pixels (section 4.1) and the removal of false positives from this mask (sections 4.2 and 4.3). In this context the separation between water bodies and shadowed surfaces is the most challenging task which is implemented by consecutive spectral and spatial processing steps (sections 4.3.1 and 4.3.2) resulting in very high detection accuracies.

An automated and adaptable approach for characterizing and partitioning cities into urban structure types

Recently a growing number of investigations is dealing with the characterization and partitioning of urban agglomerations into urban structure types (USTs) based on remote sensing data. Since the USTs of interest are usually chosen with respect to the research question, application and type of urban agglomeration there is a need for a flexible and adaptable approach for automatic UST classification. In this study we identify the commonalities of published approaches and derive requirements and tasks to deal with in UST classification. Based on this, we focus on the development of a UST classification system that is highly automated, flexible and adaptable to enable a wide applicability.

Automating the Build-Up Process of Feature-Based Fuzzy Logic Models for the Identification of Urban Biotopes from Hyperspectral Remote Sensing Data

The automatic classification of urban biotopes from remote sensing (RS) data is not a task for a pixel-based classifier. All pixels of a biotope have to be taken into account during the classification of a biotope. Therefore, fuzzy logic models of biotope types are built with regard to the composition of the biotopes of different surface materials and their arrangement in the biotopes. The models consist of lists of numerical features and associated membership functions. The features have been calculated on RS data and are able to quantitatively assess characteristic differences between the biotopes of different types. There are not enough one-against-all features that separate one biotope type from all others. Therefore, the fully automated feature selection process aims at finding the set of features that separates two biotope types best with a pairwise maximum likelihood classification. The resulting list of features for the separation of two types is incorporated into the fuzzy logic models of these two types and serves there as one input variable. Thus, a model consists of n-1 input variables whereby n is the number of biotope types to distinguish. The application of the associated membership functions (Bayesian probability functions defined by means and covariances of training biotopes) on the input variables results in probability values between zero and one. The smallest of these probability values is taken as the crisp output value of a model and can be interpreted as a similarity value expressing the similarity of the classified biotope to the type of the model. The classification of the biotopes in the 14.5 km2 test area with the developed models yields an overall accuracy of 87%.

Automatic detection and delineation of surface water bodies in airborne hyperspectral data

In this investigation we focused on the development of a new water detection algorithm based on VIS-NIR imaging spectroscopy data. By analyzing different images containing inland and ocean water types we found the slopes of the reflectance spectrum of water at specific spectral wavelengths within the VIS-NIR spectral region to be diagnostic features for surface water identification. However, the presence of these features depends on the spectral superimposition of water constituents and bottom coverage. This aspect has been considered in the development of a knowledge-based classifier. The results (probability of detection generally lies above 90%) indicate the great potential of the developed algorithm for surface water body detection and delineation within urban, rural and coastal scenes.

A contribution to the reduction of radiometric miscalibration of pushbroom sensors

Imaging spectroscopy is used for a variety of applications such as the identification of surface cover materials and its spatiotemporal monitoring. Contrary to multispectral instruments more spectral information can be incorporated in the differentiation of materials. New generations of sensors are based on the pushbroom technology, where a linear array of sensors perpendicular to the flight direction scans the full width of the collected data in parallel as the platform moves. Contrary to whiskbroom scanners that collect data one pixel at a time pushbroom systems can simply gather more light as they sense a particular area for a longer time. This leads to a better Signal-to-Noise Ratio (SNR). In addition, the two dimensional photo detector array in pushbroom systems may enable different readout configuration settings, such as spatial and/or spectral binning, allowing a better control of the SNR. It follows from this that low reflective materials can be potentially sensed as well as high reflective materials without saturating the detector elements. However, the use of detector arrays requires a precise radiometric calibration as different detectors might have different physical characteristics. Any miscalibration results in visually perceptible striping and uncertainties increase in preceding analyses such as classification and segmentation (Datt et al., 2003). There are various reasons for miscalibration, for instance temporal fluctuations of the sensor temperature, deprecated calibration coefficients or uncertainties in the modelling of the calibration coefficients. In addition, ageing and environmental stresses highly affect the mechanical and optical components of a sensor system; its reliability is thus not such to grant unchanged calibration accuracies for the entire mission life span.

Comparison of Multi- and Hyperspectral Remote Sensing Data for Use in Comprehensive Urban Biotope Mapping

We classified 922 urban biotopes from 11 different biotope types in a 50.6 km2 study area in Berlin, Germany. As input advanced data products were derived from hyperspectral and simulated multispectral data. Urban surface materials were derived from the hyperspectral data by classification and linear spectral unmixing. Multispectral data was classified using four different per-pixel and object-oriented classifiers. The results show that our developed method for biotope classification works well with hyperspectral and with multispectral input data yielding comparable overall accuracies of 88.1 and 91.3 percent.