The Synthetic Image TEsting Framework (SITEF) for the evaluation of multi-spectral image segmentation algorithms

ABSTRACT One of the most challenging tasks in Remote Sensing at present is how to handle the huge amounts of image data acquired every day by the existing Earth Observation Satellites (EOS). An alternative approach to the standard per-pixel analysis of multi-spectral EOS images has evolved over the last decade. Instead of focusing on individual image pixels, the object-based image analysis approach consists of partitioning an image into meaningful image-objects. One of the reasons for the development of object-based methods has been the dramatic increase in commercially available high resolution digital remote sensing imagery, with spatial resolutions of 5.0 m and finer [1]. Also it has been recognised that the image pixel is not a "natural" element of an image scene. A common element of all object-based image analysis systems is the segmentation stage, where the image is partitioned in a number of objects (or segments), which is clearly a critical stage of the whole process. If the segmentation fails to identify as an object a given element present in the image, the subsequent stages will generally be unable to recognise or to classify this element. An evaluation of the abilities and limitations of the segmentation algorithms used is therefore an important aspect of any object based image analysis system. However, there is no established standard procedure for the evaluation of the segmentation results produced for EOS images The purpose of this work is to present the Synthetic Image TEsting Framework (SITEF), a tool to evaluate the performance of segmentation algorithms on multi-spectral images. The method is based on the production of synthetic images with the spectral characteristics of the image pixels extracted from a signature multi-spectral image The methodology used here is an evolution of the method described in REFERENCES [1] G.J. Hay, G. Castilla, M.A. Wulder, J.R. Ruiz, "An automated object-based approach for the multiscale image segmentation of forest scene

Assessment of the Segmentation of RGB Remote Sensing Images: A Subjective Approach

The evaluation of remote sensing imagery segmentation results plays an important role in the further image analysis and decision-making. The search for the optimal segmentation method for a particular data set and the suitability of segmentation results for the use in satellite image classification are examples where the proper image segmentation quality assessment can affect the quality of the final result. There is no extensive research related to the assessment of the segmentation effectiveness of the images. The designed objective quality assessment metrics that can be used to assess the quality of the obtained segmentation results usually take into account the subjective features of the human visual system (HVS). A novel approach is used in the article to estimate the effectiveness of satellite image segmentation by relating and determining the correlation between subjective and objective segmentation quality metrics. Pearson’s and Spearman’s correlation was used for satellite images after applying a k-means++ clustering algorithm based on colour information. Simultaneously, the dataset of the satellite images with ground truth (GT) based on the “DeepGlobe Land Cover Classification Challenge” dataset was constructed for testing three classes of quality metrics for satellite image segmentation.This article belongs to the Special Issue The Quality of Remote Sensing Optical Images from Acquisition to UsersThis research has received funding from the Research Council of Lithuania (LMTLT), agreement No. S-MIP-19-27