Geospatial Object Recognition From High Resolution Satellite Imagery

Ergul, Mustafa
Alatan, Abdullah Aydın
In this paper, a novel automatic geo-spatial object recognition algorithm from high resolution satellite imagery is proposed. The proposed algorithm consists of two main steps; the generation of hypothesis with a local feature based algorithm and verification step with a shape based approach. The superiority of this method is the ability of minimization of false alarm number in the recognition and this is because object shape includes more characteristic and discriminative information about object identity and functionality. Furthermore, the location information can be extracted for the detected objects completely and precisely. Experimental results reveal that the proposed algorithm has promising results in terms of accuracy in recognizing geospatial objects, such as airplane etc., from high resolution satellite imagery.


An automatic geo-spatial object recognition algorithm for high resolution satellite images
Ergul, Mustafa; Alatan, Abdullah Aydın (2013-09-26)
This paper proposes a novel automatic geo-spatial object recognition algorithm for high resolution satellite imaging. The proposed algorithm consists of two main steps; a hypothesis generation step with a local feature-based algorithm and a verification step with a shape-based approach. In the hypothesis generation step, a set of hypothesis for possible object locations is generated, aiming lower missed detections and higher false-positives by using a Bag of Visual Words type approach. In the verification s...
Region of Interest Detection Based Fast and Robust Geo-Spatial Object Recognition
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In this paper a novel computationally efficient algorithm to detect objects automatically from high definition satellite imagery with high performance is presented. The proposed algorithm has three main steps supporting each other: Filtering, shape based and appearance based object detection. A region of interest indicating the possible regions that may have the objects to be detected is determined in a very short time via filtering step. In the remaining steps, the objects are extracted from that region an...
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In this study, we propose two algorithms for measuring the distance between shape boundaries. In the algorithms, shape boundary is represented by the Beam Angle Statistics (BAS), which maps 2-D shape information into a set of 1-D functions. Firstly, we adopt Dynamic Time Warping method to develop an efficient distance calculation scheme, which is consistent with the human visual system in perceiving shape similarity. Since the starting point of the representations may differ in shapes, the best corresponden...
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This article introduces a novel method for 3D object recognition, which utilizes well-known local features in a more efficient way, without any reliance on partial or global planarity. Geometrically consistent local features, which form the crucial basis for object recognition, are identified using affine 3D geometric invariants. The utilization of 3D geometric invariants replaces the classical 2D affine transform estimation/verification step, and provides the ability to directly verify 3D geometric consist...
Image fusion for improving spatial resolution of multispectral satellite images
Ünlüsoy, Deniz; Süzen, Mehmet Lütfi; Department of Geological Engineering (2013)
In this study, four different image fusion techniques have been applied to high spectral and low spatial resolution satellite images with high spatial and low spectral resolution images to obtain fused images with increased spatial resolution, while preserving spectral information as much as possible. These techniques are intensity-hue-saturation (IHS) transform, principle component analysis (PCA), Brovey transform (BT), and Wavelet transform (WT) image fusion. Images used in the study belong to Çankırı reg...
Citation Formats
M. Ergul and A. A. Alatan, “Geospatial Object Recognition From High Resolution Satellite Imagery,” 2013, Accessed: 00, 2020. [Online]. Available: