Abstract
Speed and accuracy are important factors when dealing with time-constraint events for disaster, risk, and crisis-management support. Object-based image analysis can be a time consuming task in extracting information from large images because most of the segmentation algorithms use the pixel-grid for the initial object representation. It would be more natural and efficient to work with perceptually meaningful entities that are derived from pixels using a low-level grouping process (superpixels). Firstly, we tested a new workflow for image segmentation of remote sensing data, starting the multiresolution segmentation (MRS, using ESP2 tool) from the superpixel level and aiming at reducing the amount of time needed to automatically partition relatively large datasets of very high resolution remote sensing data. Secondly, we examined whether a Random Forest classification based on an oversegmentation produced by a Simple Linear Iterative Clustering (SLIC) superpixel algorithm performs similarly with reference to a traditional object-based classification regarding accuracy. Tests were applied on QuickBird and WorldView-2 data with different extents, scene content complexities, and number of bands to assess how the computational time and classification accuracy are affected by these factors. The proposed segmentation approach is compared with the traditional one, starting the MRS from the pixel level, regarding geometric accuracy of the objects and the computational time. The computational time was reduced in all cases, the biggest improvement being from 5 h 35 min to 13 min, for a WorldView-2 scene with eight bands and an extent of 12.2 million pixels, while the geometric accuracy is kept similar or slightly better. SLIC superpixel-based classification had similar or better overall accuracy values when compared to MRS-based classification, but the results were obtained in a fast manner and avoiding the parameterization of the MRS. These two approaches have the potential to enhance the automation of big remote sensing data analysis and processing, especially when time is an important constraint.
Highlights
In the last decades, object-based image analysis (OBIA) has emerged as a sub-discipline of GIScience devoted to analysis and processing of very high resolution (VHR) satellite imagery [1].OBIA builds on older concepts of image analysis, like image segmentation and classification [2,3].Image segmentation aims to partition relatively homogeneous image objects, non-overlapped and spatially adjacent [2]
Because T3 is of lower landscape complexity than T2, the runtime difference is not very big in the case of Simple Linear Iterative Clustering (SLIC) superpixels (8 s) and almost negligible for SLIC version (SLICO)
ESP2 Supplementary tests proved that for scenes of tens of millions of pixels ESP2 is successfully fast when starting from SLIC superpixels, while in the case of starting from pixels it crashes due to the immense amount of resources needed to compute the statistics at the pixel-level
Summary
Object-based image analysis (OBIA) has emerged as a sub-discipline of GIScience devoted to analysis and processing of very high resolution (VHR) satellite imagery [1].OBIA builds on older concepts of image analysis, like image segmentation and classification [2,3].Image segmentation aims to partition relatively homogeneous image objects, non-overlapped and spatially adjacent [2]. Object-based image analysis (OBIA) has emerged as a sub-discipline of GIScience devoted to analysis and processing of very high resolution (VHR) satellite imagery [1]. Many image segmentation algorithms have been developed [4], but segmentation is still an unresolved problem in OBIA [1,5,6]. This is due to the fact that segmentation is sensitive to many factors, like image sensor resolution, scene complexity or number of bands [6]. Object-based classification of VHR images represents a viable
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