SAR Image Segmentation Using Morphological Attribute Profiles
TL;DR: In this paper, a morphological differential attribute profile (DAP) is calculated pixel-wise in a region growing procedure to distinguish between heterogeneous and homogeneous image content and deliver a precise segmentation result.
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Abstract: . In the last years, the spatial resolution of remote sensing sensors and imagery has continuously improved. Focusing on spaceborne Synthetic Aperture Radar (SAR) sensors, the satellites of the current generation (TerraSAR-X, COSMO-SykMed) are able to acquire images with sub-meter resolution. Indeed, high resolution imagery is visually much better interpretable, but most of the established pixel-based analysis methods have become more or less impracticable since, in high resolution images, self-sufficient objects (vehicle, building) are represented by a large number of pixels. Methods dealing with Object-Based Image Analysis (OBIA) provide help. Objects (segments) are groupings of pixels resulting from image segmentation algorithms based on homogeneity criteria. The image set is represented by image segments, which allows the development of rule-based analysis schemes. For example, segments can be described or categorized by their local neighborhood in a context-based manner. In this paper, a novel method for the segmentation of high resolution SAR images is presented. It is based on the calculation of morphological differential attribute profiles (DAP) which are analyzed pixel-wise in a region growing procedure. The method distinguishes between heterogeneous and homogeneous image content and delivers a precise segmentation result.
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Citations
Pixel-Based Classification of SAR Images Using Feature Attribute Profiles
TL;DR: It is shown that the FPs are capable of more accurately classifying the pixels compared to conventional and widely used SAR feature extraction methods such as gray-level co-occurrence matrices, histograms of oriented gradients, local binary patterns, and Gabor filters.
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Satellite Image Classification Based Spatial-Spectral Fuzzy Clustering Algorithm
Sinh Dinh Mai,Long Thanh Ngo,Hung Le Trinh +2 more
- 19 Mar 2018
TL;DR: Experimental results show that this new approach named spatial-spectral fuzzy clustering (SSFC) can improve the clustering accuracy and avoid falling into local optimum and can further make cluster more stable.
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Building extraction from very high-resolution synthetic aperture radar images based on statistical and structural information fusion
TL;DR: The experimental results on the four real VHR SAR images show that the proposed method has a high detection rate (DR) and low false alarm rate (FAR) and the final result is obtained by fusing the two sets of detections.
8
Change Analysis at Stuttgart Airport Using TerraSAR-X Imagery
Markus Boldt,Markus Boldt,Antje Thiele,Antje Thiele,Erich Cadario,Karsten Schulz,Stefan Hinz +6 more
- 23 Oct 2014
TL;DR: In this paper, a change analysis focusing on small-sized areas is presented, which includes five central components given by the change detection itself, the pre-categorization of change pixels, the feature extraction for change blobs, the analysis of their spatial context, and the final decision making forming a categorization statement.
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The Fusion of Morphological and Contextual Information for Building Detection from Very High-Resolution SAR Images
Sadjad Adelipour,Hassan Ghassemian +1 more
- 01 May 2018
TL;DR: The results show that the proposed method has a high detection rate (DR), while the false alarm rate (FAR) is low, and the results are reported by the implementation of the method on two different real TerraSAR-X images.
5
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