An Algorithm of Dim and Small Target Detection Based on Wavelet Transform and Image Fusion

Zhao, Jie; Liu, Fuxiang; Mo, Bo

doi:10.1109/iscid.2012.162

Cited by 13 publications

(11 citation statements)

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“…Several studies have employed wavelet-based techniques to address this concern [39][40][41]. The discernibility of targets from the background may vary at different scales, which can be problematic for object detection [19,42]. Wavelet analysis can transform signals into multiple resolutions, using an adaptive window [43], and thereby latently detect targets in cluttered backgrounds.…”

Section: Introductionmentioning

confidence: 99%

Automatic Counting of Large Mammals from Very High Resolution Panchromatic Satellite Imagery

2017

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Estimating animal populations by direct counting is an essential component of wildlife conservation and management. However, conventional approaches (i.e., ground survey and aerial survey) have intrinsic constraints. Advances in image data capture and processing provide new opportunities for using applied remote sensing to count animals. Previous studies have demonstrated the feasibility of using very high resolution multispectral satellite images for animal detection, but to date, the practicality of detecting animals from space using panchromatic imagery has not been proven. This study demonstrates that it is possible to detect and count large mammals (e.g., wildebeests and zebras) from a single, very high resolution GeoEye-1 panchromatic image in open savanna. A novel semi-supervised object-based method that combines a wavelet algorithm and a fuzzy neural network was developed. To discern large mammals from their surroundings and discriminate between animals and non-targets, we used the wavelet technique to highlight potential objects. To make full use of geometric attributes, we carefully trained the classifier, using the adaptive-network-based fuzzy inference system. Our proposed method (with an accuracy index of 0.79) significantly outperformed the traditional threshold-based method (with an accuracy index of 0.58) detecting large mammals in open savanna.

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Section: Introductionmentioning

confidence: 99%

Automatic Counting of Large Mammals from Very High Resolution Panchromatic Satellite Imagery

2017

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“…Image decomposition method has also been a lot of attention, this method uses wavelet transform [5], and Fourier transform [6] and so on to the image for time domain analysis or frequency domain analysis. Zhao et al [7] use the wavelet transform to decompose the image into high-frequency part and low-frequency part, extract and fuse the high-frequency part of the image, then finish the target detection by using threshold segmentation; Bai [8] uses the quaternion to reconstruct the image, obtains the spectrum and amplitude spectrum of the image through the super complex Fourier transform and filters it, and finally returns the time domain to complete the detection task. Such methods usually work well, but are usually computationally complex and time-consuming.…”

Section: Introductionmentioning

confidence: 99%

Infrared Small Target Detection Using Modified Order Morphology and Weighted Local Entropy

Zhang¹,

Chi²,

Hu³

et al. 2017

Proceedings of the 2nd International Conference on Computer Engineering, Information Science &Amp; Application Technology (ICCI

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Abstract. Infrared images in the sky scence often contain different complexity noises, which affect the validity of target detection algorithm and result in high false alarm rate. In order to solve this problem, this paper proposes a method based on weighted order morphology, which utilizes the improved open operation to do the denoising and then uses the edge structuring element to extract the small target, and defines a weighted order morphology-based top-hat (WOTH) operation to suppress background while enhancing the target, and finally complete the detection of target by using a simple adaptive threshold. The algorithm combines three different structuring elements, and has four adjustable parameters. By controlling the structure elements' size and related parameters, the target and the background can be quickly separated, the use of weighted local entropy operation also enhance the target effectively. The experimental results show that the proposed algorithm can effectively suppress different types of interference and accurately detect infrared small targets, and has a good performance in robustness and fastness.

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“…Wavelet transform algorithm [8,9]: The basic idea of wavelet transform algorithm is that the infrared image has a large area continuous distribution state and the intensity of the infrared radiation presents a gradual transition state in background area. Therefore the infrared image has a strong relevance for gray distribution and constitutes a 2-D stationary random process.…”

Section: Introductionmentioning

confidence: 99%

A new infrared small and dim target detection algorithm based on multi-directional composite window

Yang

Zhou

et al. 2015

Infrared Physics & Technology

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An Algorithm of Dim and Small Target Detection Based on Wavelet Transform and Image Fusion

Cited by 13 publications

References 0 publications

Automatic Counting of Large Mammals from Very High Resolution Panchromatic Satellite Imagery

Automatic Counting of Large Mammals from Very High Resolution Panchromatic Satellite Imagery

Infrared Small Target Detection Using Modified Order Morphology and Weighted Local Entropy

A new infrared small and dim target detection algorithm based on multi-directional composite window

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