Modified Graph Laplacian Model With Local Contrast and Consistency Constraint for Small Target Detection

Xia, Chaoqun; Li, Xiaorun; Zhao, Liaoying; Yu, Shaoqi

doi:10.1109/jstars.2020.3024642

Cited by 10 publications

(5 citation statements)

References 49 publications

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“…The methods based on human vision take the saliency of the target in the adjacent area as the detection basis. Based on the spatially discontinuous features of the target [12], Chen et al developed the local contrast map (LCM) algorithm [13]. The gray difference of a 3 × 3 neighborhood is utilized to estimate the saliency of pixels in the neighborhood.…”

Section: Single-frame Image-detection Methodsmentioning

confidence: 99%

Total Variation Weighted Low-Rank Constraint for Infrared Dim Small Target Detection

Chen

Tao

et al. 2022

Remote Sensing

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Infrared dim small target detection is the critical technology in the situational awareness field currently. The detection algorithm of the infrared patch image (IPI) model combined with the total variation term is a recent research hotspot in this field, but there is an obvious staircase effect in target detection, which reduces the detection accuracy to some extent. This paper further investigates the problem of accurate detection of infrared dim small targets and a novel method based on total variation weighted low-rank constraint (TVWLR) is proposed. According to the overlapping edge information of image background structure characteristics, the weights of constraint low-rank items are adaptively determined to effectively suppress the staircase effect and enhance the details. Moreover, an optimization algorithm combined with the augmented Lagrange multiplier method is proposed to solve the established TVWLR model. Finally, the experimental results of multiple sequence images indicate that the proposed algorithm has obvious improvements in detection accuracy, including receiver operating characteristic (ROC) curve, background suppression factor (BSF) and signal-to-clutter ratio gain (SCRG). Furthermore, the proposed method has stronger robustness under complex background conditions such as buildings and trees.

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Section: Single-frame Image-detection Methodsmentioning

confidence: 99%

Total Variation Weighted Low-Rank Constraint for Infrared Dim Small Target Detection

Chen

Tao

et al. 2022

Remote Sensing

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“…Detecting objects of clutter distribution and extreme sizes in aerial images is another prevalent issue [4,25]. R 2 -CNN [26] constructs a lightweight backbone called Tiny-Net and takes global attention as feature self-reinforcement for tiny object detection.…”

Section: A Object Detection In Aerial Imagesmentioning

confidence: 99%

“…So far, benefitting from the powerful feature representation capabilities of neural networks, a large number of deeplearning-based object detection methods have been proposed to detect objects in aerial images [1][2][3][4], and there are also some works focusing on bridge detection [5][6][7]. In this paper, we concentrate on detecting bridges on the water in optical aerial images.…”

Section: Introductionmentioning

confidence: 99%

Accurate Bridge Detection in Aerial Images With an Auxiliary Waterbody Extraction Task

Guo¹,

Zhang²,

Wang³

et al. 2021

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

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Bridge detection in aerial images is to determine whether a given aerial image contains one or more bridges and locate them. However, the arbitrary orientations, extreme aspect ratios, and variable backgrounds pose great challenges for bridge detection and positioning. In this paper, we tackle these problems by combining the strengths of semantic-segmentation-based auxiliary supervision, waterbody constraint, and instance-switchingbased data augmentation. More precisely, we make three main contributions: (i) We propose an oriented bridge detection model with an auxiliary task of waterbody segmentation, which performs as guidance for bridge localization. The network is specifically designed in cascade style to handle the bridge detection and waterbody segmentation task end-to-end. (ii) We make use of the semantic features of waterbody as spatial attention to distinguish bridges from cluttered backgrounds, and then generate the waterbody segmentation map as the waterbody constraint, which introduces the prior knowledge of bridge distribution to refine the network predictions. (iii) We propose a background consistent instance switching method for online data augmentation to further improve the robustness of bridge detection. To verify the effectiveness of the proposed method, we introduce a dataset named BridgeDetV1 containing 5,000 well-annotated images with two kinds of bridge representations, i.e., the horizontal bounding box and the oriented bounding box. Extensive experiments demonstrate that our approach outperforms the state-of-the-art methods on this challenging benchmark. Dataset and code are available at https://github.com/whughw/BridgeDet.

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“…In contrast to the abovementioned methods, IPI-based methods use the background non-local self-correlation property to transform the small target detection problem into an optimization problem of the recovery of low-rank and sparse matrices and use principal component pursuit to solve the problem [13][14][15][16]. Xia et al considered both the global sparsity and local contrast of small targets and proposed a modified graph Laplacian model (MGLM) with local contrast and consistency constraints [17]. Because a point target with a low SNR lacks effective spatial information, the above methods cannot separate the target from the background.…”

Section: Introductionmentioning

confidence: 99%

Moving Point Target Detection Based on Temporal Transient Disturbance Learning in Low SNR

et al. 2023

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Moving target detection in optical remote sensing is important for satellite surveillance and space target monitoring. Here, a new moving point target detection framework under a low signal-to-noise ratio (SNR) that uses an end-to-end network (1D-ResNet) to learn the distribution features of transient disturbances in the temporal profile (TP) formed by a target passing through a pixel is proposed. First, we converted the detection of the point target in the image into the detection of transient disturbance in the TP and established mathematical models of different TP types. Then, according to the established mathematical models of TP, we generated the simulation TP dataset to train the 1D-ResNet. In 1D-ResNet, the structure of CBR-1D (Conv1D, BatchNormalization, ReLU) was designed to extract the features of transient disturbance. As the transient disturbance is very weak, we used several skip connections to prevent the loss of features in the deep layers. After the backbone, two LBR (Linear, BatchNormalization, ReLU) modules were used for further feature extraction to classify TP and identify the locations of transient disturbances. A multitask weighted loss function to ensure training convergence was proposed. Sufficient experiments showed that this method effectively detects moving point targets with a low SNR and has the highest detection rate and the lowest false alarm rate compared to other benchmark methods. Our method also has the best detection efficiency.

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Modified Graph Laplacian Model With Local Contrast and Consistency Constraint for Small Target Detection

Cited by 10 publications

References 49 publications

Total Variation Weighted Low-Rank Constraint for Infrared Dim Small Target Detection

Total Variation Weighted Low-Rank Constraint for Infrared Dim Small Target Detection

Accurate Bridge Detection in Aerial Images With an Auxiliary Waterbody Extraction Task

Moving Point Target Detection Based on Temporal Transient Disturbance Learning in Low SNR

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