Abstract
Image segmentation plays a significant role in remote sensing image processing. Among numerous segmentation algorithms, the region-merging segmentation algorithm is widely used due to its well-organized structure and outstanding results. Many merging criteria (MC) were designed to improve the accuracy of region-merging segmentation, but each MC has its own shortcomings, which can cause segmentation errors. Segmentation accuracy can be improved by referring to the segmentation results. To achieve this, an approach for detecting and correcting region-merging image segmentation errors is proposed, and then an iterative optimization model is established. The main contributions of this paper are as follows: (1) The conflict types of matching segment pairs are divided into scale-expression conflict (SEC) and region-ownership conflict (ROC), and ROC is more suitable for optimization. (2) An equal-scale local evaluation method was designed to quantify the optimization potential of ROC. (3) A regional anchoring strategy is proposed to preserve the results of the previous iteration optimization. Three QuickBird satellite images of different land-cover types were used for validating the proposed approach. Both unsupervised and supervised evaluation results prove that the proposed approach can effectively improve segmentation accuracy. All explicit and implicit optimization modes are concluded, which further illustrate the stability of the proposed approach.
Highlights
Object-based image analysis (OBIA) has been widely used in the past two decades, such as in urban-land-cover mapping [1,2,3], ecological monitoring [4,5,6], disaster evaluation [7,8,9], and crop-type identification [10,11,12]
Results fromDataset the segments of main merging criteria (MMC), and the remaining segment is anchored in the anchor map
The three scenes were all captured by a remote sensing satellite, QuickBird, of reference merging criteria (RMC), we adopted the following anchor strategy: with three spectral bands and 0.6-meter spatial resolution
Summary
Object-based image analysis (OBIA) has been widely used in the past two decades, such as in urban-land-cover mapping [1,2,3], ecological monitoring [4,5,6], disaster evaluation [7,8,9], and crop-type identification [10,11,12]. The most intriguing feature of OBIA is that homogeneous pixels are merged into one segment, and the segment serves as the smallest unit for image analysis [16,17]. The application of OBIA both avoids the salt and pepper noise in the pixel-level classification method, and enables the utilization of geometric and spatial contextual features of segments, which is conducive to the classification of human-affected areas, where most geographic objects have regular shapes [18]. There are two types of segmentation error: oversegmentation error (OSE) and under-segmentation error (USE) [20].
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