Saliency Analysis Model Research Articles

SPSNet: Boosting 3D point-based object detectors with stable point sampling

Currently, the primary focus of 3D point-based detectors is to enhance their performance by primarily increasing the recall rate of foreground points at the semantic level. However, this approach is suboptimal. On one hand, ground-truth annotations of 3D bounding boxes are inherently ambiguous due to occlusions, signal distortion, or manual annotation errors, which can obscure genuine semantic information. On the other hand, the saliency of each point varies. In this paper, we propose SPSNet based on Bayes theorem to explore the mapping relationship between the sampled point and the corresponding bounding box, which we call point stability. This is a plug-and-play module and used as the basis for downsampling instead of relying on semantic information obtained through hard supervision of 3D bounding boxes. We incorporate the proposed method into the most popular point-based detector, IA-SSD, called SPSNet-IA. On the challenging KITTI validation set, with only changes to the downsampling strategy, it obtains 1.18, 2.78 and 2.96 gains for the metric of AP3D on the easy, moderate and hard levels, respectively. In addition, SPSNet-IA outperforms all published point-based approaches by a large margin and ranks 1st among single-modal methods. We also design qualitative and quantitative experiments to explain the meaning of stability learned from SPSNet. These additional experiments demonstrate that SPSNet is not only a performance-enhancing model but also a saliency analysis model. The code is available at https://github.com/AlanLiangC/SPSNet.git.

A target detection algorithm for SAR images based on regional probability statistics and saliency analysis

ABSTRACT In comparison with optical images, a Synthetic Aperture Radar (SAR) image has many defects, such as low resolution, strong noise interference and random distribution of the target, which increases the false alarm rate of traditional detection methods. To improve the detection accuracy of the SAR image, a novel detection method is proposed based on regional probability statistics and saliency analysis. A saliency analysis model based on dense and sparse reconstruction (DSR) is reconstructed to locate the target precisely. Firstly, the regional probability of the SAR image is estimated to extract the background region. And then, the extracted background sub-blocks are clustered and employed to replace the corresponding background template set of the DSR model. Subsequently, the reconstructed DSR model is used to extract the target, and the detection accuracy of the proposed method is enhanced greatly. Compared with the constant false alarm rate (CFAR)-based detection method, the proposed method can achieve a high detection accuracy and protect the edges of the SAR image.

Airport Detection and Aircraft Recognition Based on Two-Layer Saliency Model in High Spatial Resolution Remote-Sensing Images

Efficient airport detection and aircraft recognition are essential due to the strategic importance of these regions and targets in economic and military construction. In this paper, a novel airport detection and aircraft recognition method that is based on the two-layer visual saliency analysis model and support vector machines (SVMs) is proposed for high-resolution broad-area remote-sensing images. In the first layer saliency (FLS) model, we introduce a spatial-frequency visual saliency analysis algorithm that is based on a CIE Lab color space to reduce the interference of backgrounds and efficiently detect well-defined airport regions in broad-area remote-sensing images. In the second layer saliency model, we propose a saliency analysis strategy that is based on an edge feature preserving wavelet transform and high-frequency wavelet coefficient reconstruction to complete the pre-extraction of aircraft candidates from airport regions that are detected by the FLS and crudely extract as many aircraft candidates as possible for additional classification in detected airport regions. Then, we utilize feature descriptors that are based on a dense SIFT and Hu moment to accurately describe these features of the aircraft candidates. Finally, these object features are inputted to the SVM, and the aircraft are recognized. The experimental results indicate that the proposed method not only reliably and effectively detects targets in high-resolution broad-area remote-sensing images but also produces more robust results in complex scenes.

Global and Local Saliency Analysis for the Extraction of Residential Areas in High-Spatial-Resolution Remote Sensing Image

Extraction of residential areas plays an important role in remote sensing image processing. Extracted results can be applied to various scenarios, including disaster assessment, urban expansion, and environmental change research. Quality residential areas extracted from a remote sensing image must meet three requirements: well-defined boundaries, uniformly highlighted residential area, and no background redundancy in residential areas. Driven by these requirements, this study proposes a global and local saliency analysis model (GLSA) for the extraction of residential areas in high-spatial-resolution remote sensing images. In the proposed model, a global saliency map based on quaternion Fourier transform (QFT) and a global saliency map based on adaptive directional enhancement lifting wavelet transform (ADE-LWT) are generated along with a local saliency map, all of which are fused into a main saliency map based on complementarities. In order to analyze the correlation among spectrums in the remote sensing image, the phase spectrum information of QFT is used on the multispectral images for producing a global saliency map. To acquire the texture and edge features of different scales and orientations, the coefficients acquired by ADE-LWT are used to construct another global saliency map. To discard redundant backgrounds, the amplitude spectrum of the Fourier transform and the spatial relations among patches are introduced into the panchromatic image to generate the local saliency map. Experimental results indicate that the GLSA model can better define the boundaries of residential areas and achieve complete residential areas than current methods. Furthermore, the GLSA model can prevent redundant backgrounds in residential areas and thus acquire more accurate residential areas.

Region-of-interest extraction based on spectrum saliency analysis and coherence-enhancing diffusion model in remote sensing images

Saliency analysis, which is a fast and efficient method for extracting Regions of Interest (ROIs), has been applied in the field of remote sensing image analysis. In this paper, a novel saliency analysis model is proposed using spectrum saliency analysis (SSA) and coherence-enhancing diffusion model (CED). In SSA, the probability of each pixel intensity value is counted to get the one-dimensional histogram for each band, then information content of each band is calculated according to the one-dimensional histogram and generate spectrum saliency map after the information content weighted fusion of each band. In CED, coherence-enhancing diffusion model is introduced for spectrum saliency map that aims to smooth internal ROIs and eliminate background interference to improve the precision, accuracy and completeness of the resulting saliency map. Finally, ROIs are segmented from the saliency maps of original images by an adaptive threshold segmentation algorithm. Several experiments were conducted to evaluate the overall performance of the proposed model compared with the other eight outstanding models qualitatively and quantitatively. The experimental evaluations show that the proposed model outperforms the relevant outstanding models.