Abstract
Small target detection from the infrared remote sensing image is a challenge task. In this article, a novel local adaptive threshold algorithm combined with heterogeneity and compactness filters is proposed to detect the small target from the infrared remote sensing images. First, the infrared image is filtered by a heterogeneity filter to enhance the target saliency. Then, the enhanced image is filtered by a compactness filter to generate a target candidate region map. Finally, for each pixel in the target candidate region, a local adaptive threshold is calculated from the enhanced image to determine whether it is a target pixel or not, and thus, the targets are extracted out. The designed heterogeneity filter and compactness filter can effectively suppress the background clutter, enhance the target, and generate target candidate regions. The proposed adaptive thresholding is a local threshold method, which is calculated in a small local window and can effectively reduce the false alarm and missing alarm. Qualitative and quantitative experiments are conducted on synthetic images and real images. The experiment results show that, with good target enhancement and background suppression, and high detection accuracy, the proposed method outperforms other state-of-the-art methods.
Highlights
Inner Layer Middle Layer (a) (b)(b) A 3D diagram of the filter.In the infrared image, the target is bright inside and dark outside
NFRARED dim small target (IDST) detection is a key content in infrared image processing, which has been widely used in investigation, guidance, early warning, and other military fields
We propose a novel target feature-based detection algorithm, namely HCA, for the infrared remote sensing images, which is the combination of adaptive threshold operation with the heterogeneity filter and compactness filter
Summary
NFRARED dim small target (IDST) detection is a key content in infrared image processing, which has been widely used in investigation, guidance, early warning, and other military fields. In the target feature-based detection algorithms, the original image is usually enhanced and a threshold operation is needed to extract the targets from the enhancement image These methods usually use a global threshold, such as NLCD [16], LEF [20], GSS-ELCM [25], and FAMSIS [26], which can lead to missing alarms and false alarms. We propose a novel target feature-based detection algorithm, namely HCA, for the infrared remote sensing images, which is the combination of adaptive threshold operation with the heterogeneity filter and compactness filter. 3) A novel dim small detection algorithm is proposed for the infrared remote sensing image, which outperformed the state-of-the-art methods in detection accuracy, background suppression and target enhancement.
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