Description
Aircraft Recognition In Satellite Images using Matlab
The project proposes to recognize an aircraft in satellite images using template matching for accurate detection and tracking. This recognition system involves dimensionality reduction, segmentation, and aircraft identification with templates. Here, Histogram probability thresholding is used to detect the desired object from the background. The connected component analysis is used here to label the segmented image for grouping similar objects. Correlation measurement is used for measuring the similarity between two different object region features. This is used to locate the aircraft region for tracking it and it shows that reliable and compatible method for this process. High-resolution multispectral satellite images with multi-angular look capability have tremendous potential applications. Here the system involves an object tracking algorithm with three-step processing that includes moving object estimation, target modeling, and target matching. Potentially moving objects are first identified in the time-series images. The target is then modeled by extracting both spectral and spatial features. In the target matching procedure, the template will be used as a matching model to recognize each frame by frame for accurate detection. The final simulation will be demonstrated the capability of object tracking in remote sensing images with help of used approaches.
Aircraft Recognition In Satellite Images using Matlab
Introduction
AIRCRAFT recognition is an important issue of target recognition in satellite images and has many important applications in practice such as airfield dynamic surveillance. As the resolution of satellite images gets higher, more abundant color, texture, and spatial information are provided. Such information offers a good opportunity to recognize aircraft that have a very complex structure. However, automatic aircraft recognition is not a simple problem. Besides the complex structure, different aircraft differ in size, shape, and color, and even for one kind of aircraft, the texture and intensity are usually dissimilar in different scenarios. Moreover, recognition often suffers from various disturbances such as clutter, different contrasts, and intensity inhomogeneity. Thus, robustness and resistance to disturbance are highly required for the method. We illustrate some typical satellite aircraft images. Here the system involves an object tracking algorithm with three-step processing that includes moving object estimation, target modeling, and target matching. Potentially moving objects are first identified in the time-series images. The target is then modeled by extracting both spectral and spatial features. In the target matching procedure, the template will be used as a matching model to recognize each frame by frame for accurate detection. The final simulation will be demonstrated the capability of object tracking in remote sensing images with help of used approaches.
Existing Systems
- Sensor-based tracking
- Internet protocol-based tracking
- Wireless communication
Drawbacks
- Visualization is not possible
- The position of an object cannot be found
- Recognition is not possible
Proposed method
The proposing method has three steps, object detection, target modeling, and target tracking.
- Object detection involves objects extracted frame by frame.
- In target modeling, we find features and obtain the target in the images.
- Target tracking, we track the object in every image
Advantages
- Accurate tracking with position
- Communication with objects to satellite is achieved
- Low complexity
Block diagram
System Architecture
Template Matching
Requirement Specifications
Hardware Requirements
- system
- 4 GB of RAM
- 500 GB of Hard disk
SOFTWARE REQUIREMENTS:
- MATLAB 2018b
References
[1] A. Yilmaz, O. Javed, and M. Shah, ?Object tracking: A survey,? ACM Comput. Surv., vol. 38, Dec. 2006.
[2] S. Hinz, R. Bamler, and U. Stilla, ?Theme issue: Airborne and spaceborne traffic monitoring,? ISPRS J. Photogramm. Remote Sens., vol. 61, no. 3? 4, pp. 135? 280, 2006.
[3] I. Szottka and M. Butenuth, ?Tracking multiple vehicles in airborne image sequences of complex urban environments,? in Proc. 2011 Joint Urban Remote Sensing Event (JURSE), Apr. 2011, pp. 13? 16.
[4] K. Palaniappan, F. Bunyak, P. Kumar, I. Ersoy, S. Jaeger, K. Ganguli, A. Haridas, J. Fraser, R. Rao, and G. Seetharaman,?Efficient feature extraction and likelihood fusion for vehicle tracking in low frame rate airborne video,? in Proc. 13th Conf. Information Fusion (FUSION), Jul. 2010, pp. 1?8
[5] N. Joshi, R. Szeliski, and D. Kriegman, ?Post estimation using sharp edge prediction,? in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Anchorage, AL, USA, 2008.
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