Abstract
Image segmentation is extensively used in remote sensing spectral image processing. Most of the existing region merging methods assess the heterogeneity or homogeneity using global or pre-defined parameters, which lack the flexibility to further improve the goodness-of-fit. Recently, the local spectral angle (SA) threshold was used to produce promising segmentation results. However, this method falls short of considering the inherent relationship between adjacent segments. In order to overcome this limitation, an adaptive SA thresholds methods, which combines the inter-segment and boundary homogeneities of adjacent segment pairs by their respective weights to refine predetermined SA threshold, is employed in a hybrid segmentation framework to enhance the image segmentation accuracy. The proposed method can effectively improve the segmentation accuracy with different kinds of reference objects compared to the conventional segmentation approaches based on the global SA and local SA thresholds. The results of the visual comparison also reveal that our method can match more accurately with reference polygons of varied sizes and types.
Highlights
With the rapid development of high resolution remote sensing imaging techniques, geographic object-based image analysis (GEOBIA) has become a promising paradigm to extract accurate and reliable ground information from various detectors [1,2]
After that, based on the suggestion in related works [29], the preset spectral angle (SA) threshold is refined by the local homogeneity of adjacent regions, which is quantified by the weighted average of inter-segment and boundary homogeneities in terms of relative areas of the boundary regions and internal regions, to obtain the adaptive local SA threshold
The best segmentation results obtained by the aforementioned three methods were selected for detailed analysis on the matching index (MI) and quality rate (QR) values of every reference polygons, where the MI and QR values were calculated by Equations (16) and (17)
Summary
With the rapid development of high resolution remote sensing imaging techniques, geographic object-based image analysis (GEOBIA) has become a promising paradigm to extract accurate and reliable ground information from various detectors [1,2]. Edge-based [12] and region-based [13] partition strategies were proposed to implement the image segmentation. Edge-based algorithms are sensitive to noise or texture variation, apt to render over-segmentation around textured regions [14]. Region-based algorithms exploit homogeneity or heterogeneity of adjacent regions to improve the robustness of the segmentation results against noise [13,15]. To take advantages from both sides, a set of hybrid segmentation methods have been proposed to jointly exploit the principles of edge-based and region-based algorithms [17,18,19]
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