Ship Target Automatic Detection Based on Hypercomplex Flourier Transform Saliency Model in High Spatial Resolution Remote-Sensing Images

He, Jian; Guo, Yongfei; Yuan, Hangfei

doi:10.3390/s20092536

Cited by 12 publications

(12 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The PFN module and feature fusion strategy are often used to improve the detection accuracy and reduce the false alarms of the small target [26,42,52]. Furthermore, those modules always integrate into the VGG16 and ResNet-50 networks [42,53]; the CNN models are complex and have many parameters to train. The PFN module and feature fusion strategy show effectiveness for small goals in object detectors, but may show poor effectiveness for much less than 32 × 32.…”

Section: Discussionmentioning

confidence: 99%

Ship Detection and Feature Visualization Analysis Based on Lightweight CNN in VH and VV Polarization Images

Geng

Shi

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

Synthetic aperture radar (SAR) is a significant application in maritime monitoring, which can provide SAR data throughout the day and in all weather conditions. With the development of artificial intelligence and big data technologies, the data-driven convolutional neural network (CNN) has become widely used in ship detection. However, the accuracy, feature visualization, and analysis of ship detection need to be improved further, when the CNN method is used. In this letter, we propose a two-stage ship detection for land-contained sea area without a traditional sea-land segmentation process. First, to decrease the possibly existing false alarms from the island, an island filter is used as the first step, and then threshold segmentation is used to quickly perform candidate detection. Second, a two-layer lightweight CNN model-based classifier is built to separate false alarms from the ship object. Finally, we discuss the CNN interpretation and visualize in detail when the ship is predicted in vertical–horizontal (VH) and vertical–vertical (VV) polarization. Experiments demonstrate that the proposed method can reach an accuracy of 99.4% and an F1 score of 0.99 based on the Sentinel-1 images for a ship with a size of less than 32 × 32.

show abstract

Section: Discussionmentioning

confidence: 99%

Ship Detection and Feature Visualization Analysis Based on Lightweight CNN in VH and VV Polarization Images

Geng

Shi

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Humans can quickly focus on the interest target in the visual scene and selectively ignore the regions that are not of interest [51]. Inspired by human visual mechanisms and multi-level perception capabilities, saliency detection is utilized to extract the interesting areas, namely the saliency regions [52]- [54]. Context-based saliency detection [55] has proven to be a useful method for automating the diminution of the detection range.…”

Section: B Pre-classification Based On Saliency Detection and Fcmmentioning

confidence: 99%

Forest Disaster Detection Method Based on Ensemble Spatial–Spectral Genetic Algorithm

Cao

Feng

Quan

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Remote sensing images change detection is the key technology for monitoring forest windfall damages. Genetic algorithm (GA) is a branch of intelligent optimization techniques available to contribute to the surveys of windstorm and wildfire detection in forest areas. However, traditional genetic algorithms remain challenging due to several issues, such as complex calculation, poor noise immunity, and slow convergence. Analysis at the spatial level allows classifications to utilize contextual, and hierarchical information of image objects in addition to solely using spectral information. Additionally, ensemble learning presents a possibility for improving classification accuracy. Ensemble classifiers combined with spatial-based GA offers a promising method (E-nGA) for automating the process of monitoring forest loss. The research in this paper is presented in four parts. First, block-matching and 3D filtering is performed to suppress noises while enhancing valuable information. The difference image is then generated using the image difference method. Afterward, context-based saliency detection and Fuzzy c-means algorithm are conducted on the difference image to reduce the search space. Finally, the proposed E-nGA is executed to further classify the pixels and produce the final change map. Our first proposition is to design improved genetic operators in the GA, relying not only on pixel values but also on spatial information. Our second proposition is to consider an ensemble classification model based on multiple vegetation features for decision integration. Six frequently-used classification methods, as well as the simple genetic algorithm, are executed to demonstrate the effectiveness of the proposed framework in improving the robustness and detection accuracy.

show abstract

“…For example, Li et al [ 7 ] proposed a candidate region method by combining a visual saliency algorithm and a spatial competition algorithm and used the directional chamfer matching method to make symmetry detection of potential targets. Similarly, He et al [ 8 ] extracted ship candidate regions in RSIs by segmenting RSIs using visual saliency analysis. While the background of the land is more complex than the sea.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method of Aircraft Detection under Complex Background Based on Circular Intensity Filter and Rotation Invariant Feature

Chen

Liu

et al. 2022

Sensors

View full text Add to dashboard Cite

Aircraft detection in remote sensing images (RSIs) has drawn widespread attention in recent years, which has been widely used in the military and civilian fields. While the complex background, variations of aircraft pose and size bring great difficulties to the effective detection. In this paper, we propose a novel aircraft target detection scheme based on small training samples. The scheme is coarse-to-fine, which consists of two main stages: region proposal and target identification. First, in the region proposal stage, a circular intensity filter, which is designed based on the characteristics of the aircraft target, can quickly locate the centers of multi-scale suspicious aircraft targets in the RSIs pyramid. Then the target regions can be extracted by adding bounding boxes. This step can get high-quality but few candidate regions. Second, in the stage of target identification, we proposed a novel rotation-invariant feature, which combines rotation-invariant histogram of oriented gradient and vector of locally aggregated descriptors (VLAD). The feature can characterize the aircraft target well by avoiding the impact of its rotation and can be effectively used to remove false alarms. Experiments are conducted on Remote Sensing Object Detection (RSOD) dataset to compare the proposed method with other advanced methods. The results show that the proposed method can quickly and accurately detect aircraft targets in RSIs and achieve a better performance.

show abstract

Ship Target Automatic Detection Based on Hypercomplex Flourier Transform Saliency Model in High Spatial Resolution Remote-Sensing Images

Cited by 12 publications

References 25 publications

Ship Detection and Feature Visualization Analysis Based on Lightweight CNN in VH and VV Polarization Images

Ship Detection and Feature Visualization Analysis Based on Lightweight CNN in VH and VV Polarization Images

Forest Disaster Detection Method Based on Ensemble Spatial–Spectral Genetic Algorithm

A Novel Method of Aircraft Detection under Complex Background Based on Circular Intensity Filter and Rotation Invariant Feature

Contact Info

Product

Resources

About