Description: An end-to-end Simulation software (EeteS) has been developed in the frame of the Environmental Mapping and Analysis Program (EnMAP) mission, and results are presented based on the approach of the complete processing chain simulation. The main objective of a hyperspectral sensor simulator is to provide an end-to-end capability which allows whether the science goals or the mission requirements can be met. Therefore, it may be a handful tool for a variety of tasks and system’s tradeoffs analysis, by defining of future Earth imaging systems, allowing the optimization of fundamental instrument parameters, the development and validation of calibration and data pre-processing algorithms or the generation of realistic images for the scientific exploitation. Based on spectral and spatial high resolution image data EnMAP-like image scenes (L0) are simulated taking into account varying instrumental and environmental configurations. The sequential processing chain consists of four independent modules referred to as atmospheric, spatial, spectral and radiometric. This forward simulator is coupled to a series of pre-processing modules (radiometric calibration, co-registration, atmospheric correction and orthorectification) in order to build a representative end-to-end simulation model. EeteS is able to derive EnMAP reflectance images after the complete L1 and L2 processing chain. The analysis of final and intermediate EeteS products resulting from the processing chain supports the decision making process of engineers and scientists involved in the Mission. Selected case studies for end-to-end simulations are presented emphasizing on quantitative evaluation of the influence of instrumental and processing errors on data quality and the retrieval of higher-level image products.

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: 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.