Processors for ALOS Optical Data: Deconvolution, DEM Generation, Orthorectification, and Atmospheric Correction

Haze degrades optical data and reduces the accuracy of data interpretation. Haze detection and removal is a challenging and important task for optical multispectral data correction. This paper presents an empirical and automatic method for inhomogeneous haze detection and removal in medium-and high-resolution satellite optical multispectral images. The dark-object subtraction method is further developed to calculate a haze thickness map, allowing a spectrally consistent haze removal on calibrated and uncalibrated satellite multispectral data. Rare scenes with a uniform and highly reflecting landcover result in limitations of the method. Evaluation on hazy multispectral data (Landsat 8 OLI and WorldView-2) and a comparison to haze-free reference data illustrate the spectral consistency after haze removal.

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“…Fig. 4 illustrates such a situation on AVNIR-2 data (see [14]). A per-band analysis should be carried out [compare Fig.…”

Section: Band Extrapolationmentioning

confidence: 99%

Haze Detection and Removal in Remotely Sensed Multispectral Imagery

Makarau

Richter

Müller

et al. 2014

IEEE Trans. Geosci. Remote Sensing

123

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“…The Level 2, i.e., geometric correction (GC), processor creates the Level 2 (georeferenced) images by indirect georeferencing with the RPC model and an average height in the region of interest (ROI). More details of RRC, MTFC, and GC can be found elsewhere [6]. For more detailed descriptions about DU, MF, and RPCG, please refer to [7].…”

Section: A Zsms Image Preprocessingmentioning

confidence: 99%

MOC-Based Parallel Preprocessing of ZY-3 Satellite Images

Fang

Wang

et al. 2015

IEEE Geosci. Remote Sensing Lett.

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The launch of the ZY-3 surveying and mapping satellite (ZSMS) by China has resulted in a significant increase in the volume of image data collected for subsequent processing. In this letter, we present our research on the message passing interface (MPI), open multiprocessing (OpenMP), and compute unified device architecture (CUDA)-based (MOC-based) preprocessing of ZSMS images in a system that consists of multiple central processing units (CPUs) and graphics processing units (GPUs). First, CPUs and GPUs in the system are organized into atomic computing resources (ACRs) by means of an MPI. Then, three cooperative methods are proposed for potential performance improvement of the processors with OpenMP and CUDA. The input/ output (I/O) overhead is also addressed in this letter. The experimental results show that the total execution time of the 12 ZSMS nadir images with four ACRs is reduced to 86.10 s, which could provide near-real-time response for the time-critical applications that follow. Index Terms-Central processing units (CPUs), graphics processing units (GPUs), input/output (I/O), MOC, parallel preprocessing, ZY-3 surveying and mapping satellite (ZSMS) images.

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“…For the ALOS-PRISM images, a sophisticated physical sensor model (JAXA, 2006) together with periodically updated geometric calibration values, and for IKONOS, the RPC delivered by the satellite provider, serve as input for the DLR in-house developed geometric processing software. These sensor models lead to good results in terms of absolute geometric accuracy, but, nevertheless, by introducing GCP information the geometric accuracy can be significantly improved Schwind et al, 2009). Complementing the tie points found in the high-precision orthorectified TerraSAR-X data with interpolated height values from the DEM leads to qualified 3D GCPs connected to the conjugated image coordinates of the VHR optical data.…”

Section: Improvement Of Sensor Model and Rpcmentioning

confidence: 99%