Description: Depending on the landscape type high amounts of shadow can be present in remote sensing images. These areas are usually masked using shadow detection techniques and excluded from further analysis. Although significant research has been conducted on the detection of shadows there is still room for improvements. In this investigation we focus on the development of a new shadow detection algorithm capable to be automatically applied without user knowledge on any hyperspectral VIS-NIR image and thus can be implemented in automated pre-processing chains. The analysis is strictly focussed on the VIS-NIR part of the electromagnetic spectrum due to the growing number of VIS-NIR imaging spectrometers. The developed approach consists of two main steps, the selection of potential shadow pixels and the removal of no-shadow pixels from this mask. In this context the separation between shadow and water is the most challenging task. By analysing different images containing inland and ocean water types we found the slope of the reflectance spectrum of water at specific spectral wavelengths within the VIS-NIR range to be a diagnostic feature for water identification. However, the presence of these features depends on the spectral superimposition of water constituents and bottom coverage. These aspects have been considered in the development of a knowledge-based classifier. First results indicate the great potential of the developed algorithm for urban, rural and coastal scenes of different sensor data (AISA, HyMap).

Description: 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.

Description: Urban areas are among the most dynamic regions on earth, continuously and rapidly changing. For monitoring these changes, remote sensing has proven over the years to be a reliable source. Current airborne hyperspectral systems with spatial resolution of a few meters, combined with very high spectral resolution, facilitate the urban scene analysis by allowing to distinguish small details in the urban environment. This paper presents part of a project aiming to classify man-made objects using hyperspectral images and to investigate the complementarity between hyperspectral and SAR data. The intention is to develop methods that are able to quickly obtain an overview of the current situation and require as little human intervention as possible. This is very important for various applications related to disasters, e.g. emergency cartography, disaster monitoring, damage assessment, mission planning, etc.The paper describes a new method for classifying the main classes in an urban environment using hyperspectral data. The method is based on logistic regression (LR), which is a supervised multi-variate statistical tool that finds an optimal combination of the input channels for distinguishing one class from all the others. LR thus results in detection images per class that can then be combined into a classification image. The LR uses a step-wise method that implicitly performs a channel selection. The method is supervised in the sense that existing digital maps are used for learning. However, the method does not require the laboratory spectra or extensive ground truth. The method is applied on HyMAP data of an urban area in the South of Germany. The results of the proposed approach are compared to classical methods. Furthermore, a sensitivity analysis is presented, which investigates the robustness of the detection of the different classes against various influences and in particular the influence of channel width and pre-processing level. The classification results are better than those obtained by a classical method. The sensitivity analysis shows that the pre-processing level applied to the hyperspectral data does not influence the classification results significantly for this application. Furthermore, reducing the number of channels results in a drop of performance for some classes only when less the number of channels becomes inferior to 40.

Description: Urban biotopes are an important subject matter for ecological urban planning. Area-wide mapping and monitoring of biotopes is based on visual interpretation of color-infrared aerial photographs and field investigations. This combined inventory yields a high level of detail and accuracy but, as a drawback, it is very time- and money-consuming. Thus, many municipalities take the effort to build up an initial biotope map, but not to make regular updates. Hyperspectral data open up new opportunities for solving this problem. They allow a materialoriented identification of urban surfaces and vegetation types and the derivation of additional quantitative parameters (e.g. percentage of area, degree of surface sealing etc.). Since biotope types cannot be classified directly from pixels’ grey values the idea of this study is to use a material-oriented classification to identify them. Therefore, biotope types are modelled by a set of quantitative parameters derived from classified hyperspectral images, e.g. the percentage of area of their constituent surfaces which could be vegetation types as well as man-made surfaces, open soil etc.. Building up a generally applicable model of biotope types that way, can also include parameters to describe the location and distribution of the constituent surfaces in the biotope. Once the model is build, it can be used to check urban biotopes, taken from existing biotope maps, for changes of their type with hyperspectral data. Only biotopes indicating a change have to be inspected in the field and updated manually if necessary. The study is part of the Helmholtz-EOS project. Concept and methods of the study are presented focusing on the development of distinctive features for biotope types.

Description: The analysis of hyperspectral images belongs to the main tasks in Remote Sensing. The foregoing linear radiometric correction of registered digital numbers basically assigns the spectral and spatial dependent response of a hyperspectral pushbroom sensor to a physical meaning - radiance. Slopes and offsets of the correction are often determined in laboratory and in-flight calibrations, but may vary over time. This results in striping artefacts which aggravates succeeding processing steps such as atmospheric correction, classification and segmentation. In this work, a new approach is presented, that automatically removes these stripes calculating improved calibration factors without any prior knowledge or user interaction. The algorithm is based on the assessment of spectral and spatial probability distributions and is constrained by specific minimisation principles. Morphological and spatial filtering techniques and additionally a Signal-to-Noise-Ratio related decision tree are implemented to reduce computational effort and to stabilise the solution depending on local spatial entropy. To objectively evaluate the performance of the new approach, the technique was applied to broadly used image processing examples that has been artificially and randomly degraded by sets of multiplicative and additive noise of different distributions as well as miscalibrated AISA DUAL (VNIR and SWIR) scenes. The results clearly show the benefits of the new approach and, concurrently, provide correction facilities for other miscalibrated pushbroom sensor data.