No abstract

No abstract

We proposed the Retinex-based fast algorithm (RBFA) to achieve low-light image enhancement in this paper, which can restore information that is covered by low illuminance. The proposed algorithm consists of the following parts. Firstly, we convert the low-light image from the RGB (red, green, blue) color space to the HSV (hue, saturation, value) color space and use the linear function to stretch the original gray level dynamic range of the V component. Then, we estimate the illumination image via adaptive gamma correction and use the Retinex model to achieve the brightness enhancement. After that, we further stretch the gray level dynamic range to avoid low image contrast. Finally, we design another mapping function to achieve color saturation correction and convert the enhanced image from the HSV color space to the RGB color space after which we can obtain the clear image. The experimental results show that the enhanced images with the proposed method have better qualitative and quantitative evaluations and lower computational complexity than other state-of-the-art methods.

Due to the complexity of airport background and runway structure, the performances of most runway extraction methods are limited. Furthermore, at present, the military fields attach greater importance to semantic changes of some objects in the airport, but few studies have been done on this subject. To address these issues, this paper proposes an accurate runway change analysis method, which comprises two stages: airport runway extraction and runway change analysis. For the former stage, some airport knowledge, such as chevron markings and runway edge markings, are first applied in combination with multiple features of runways to improve the accuracy. In addition, the proposed method can accomplish airport runway extraction automatically. For the latter, semantic information and vector results of runway changes can be obtained simultaneously by comparing bi-temporal runway extraction results. In six test images with about 0.5-m spatial resolution, the average completeness of runway extraction is nearly 100%, and the average quality is nearly 89%. In addition, the final experiment using two sets of bi-temporal very high-resolution (VHR) images of runway changes demonstrated that semantic results obtained by our method are consistent with the real situation and the final accuracy is over 80%. Overall, the airport knowledge, especially chevron markings for runways and runway edge markings, are critical to runway recognition/detection, and multiple features of runways, such as shape and parallel line features, can further improve the completeness and accuracy of runway extraction. Finally, a small step has been taken in the study of runway semantic changes, which cannot be accomplished by change detection alone.