Abstract
Target detection is an important task for remote sensing images, while it is still difficult to obtain satisfied performance when some images possess complex and confusion spectrum information, for example, the high similarity between target and background spectrum under some circumstance. Traditional detectors always detect target without any preprocessing procedure, which can increase the difference between target spectrum and background spectrum. Therefore, these methods could not discriminate the target from complex or similar background effectively. In this paper, sparse representation was introduced to weight each pixel for further increasing the difference between target and background spectrum. According to sparse reconstruction error matrix of pixels on images, adaptive weights will be assigned to each pixel for improving the difference between target and background spectrum. Furthermore, the sparse weighted-based constrained energy minimization method only needs to construct target dictionary, which is easier to acquire. Then, according to more distinct spectrum characteristic, the detectors can distinguish target from background more effectively and efficiency. Comparing with state-of-the-arts of target detection on remote sensing images, the proposed method can obtain more sensitive and accurate detection performance. In addition, the method is more robust to complex background than the other methods.
Highlights
With the rapid development of remote sensing technology, the spatial resolution, spectral resolution and time resolution of remote sensing image have greatly improved, which facilitates a wide range of applications [1,2,3,4]
Four remote sensing datasets are used to evaluate the effectiveness of proposed sparse weighted-based constrained energy minimization (SWCEM) algorithm
The proposed method employs the parameters setting in hierarchical CEM (hCEM), and the procedure with results are presented and discussed as follows
Summary
With the rapid development of remote sensing technology, the spatial resolution, spectral resolution and time resolution of remote sensing image have greatly improved, which facilitates a wide range of applications [1,2,3,4]. Remote sensing image contains abundant information, of which the processing and analyzing can help people in military and civilian applications, such as disaster control, land planning, urban monitoring, traffic planning, target tracking, etc. [5,6,7] For all these applications, target detection is the necessary and key step. The targets are usually large objects, such as aircraft, ship, building, etc., which can provide more valuable object for further analyzing. The object detection is usually human, animal and other small objects, and the target is not limited to one specific category. Research of fast identification and precise interpretation on particular target has very important strategic significance [8,9]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
