Improving hyperspectral subpixel target detection using hybrid detection space

Li, Ruixing; Latifi, Shaahram

doi:10.1117/1.jrs.12.015022

Cited by 6 publications

(6 citation statements)

References 48 publications

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“…where TP is the number of anomalies that were also predicted as anomalies, TN is the number of background pixels that were predicted correctly, FN is the number of anomalies but wrongly predicted as background pixels, and FP is the number of background pixels but wrongly predicted as anomalies [52].…”

Section: B Evaluation Criteria and Experimental Settingsmentioning

confidence: 99%

A Spectral–Spatial Anomaly Target Detection Method Based on Fractional Fourier Transform and Saliency Weighted Collaborative Representation for Hyperspectral Images

Zhao

Feng

et al. 2020

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

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Anomaly target detection methods for hyperspectral images (HSI) often have the problems of potential anomalies and noise contamination when representing background. Therefore, a spectral-spatial hyperspectral anomaly detection method is proposed in this article, which is based on fractional Fourier transform (FrFT) and saliency weighted collaborative representation. First, hyperspectral pixels are projected to the fractional Fourier domain by the FrFT, which can enhance the capability of the detector to suppress the noise and make anomalies to be more distinctive. Then, a saliency weighted matrix is designed as the regularization matrix referring to context-aware saliency theory and combined with the FrFT-based collaborative representation detector. The saliencyweighted regularization matrix assigns different pixels with different weights by using both spectral and spatial information, which can reduce the influence of the potential anomalous pixels embedded in the background when applying collaborative representation theory. Finally, to further improve the performance of the proposed method, a spectral-spatial detection procedure is employed to calculate final anomaly scores by using both spectral information and spatial information. The proposed method is compared with nine state-of-the-art hyperspectral anomaly detection methods on six HSI datasets, including two synthetic HSI datasets and four real-world HSI datasets. Extensive experimental results illustrate that the proposed method's detection performance outperforms other nine well-known compared methods in terms of area under the receiver operating characteristic (ROC) curve values, visual detection characteristics, ROC curve, and separability.

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Section: B Evaluation Criteria and Experimental Settingsmentioning

confidence: 99%

A Spectral–Spatial Anomaly Target Detection Method Based on Fractional Fourier Transform and Saliency Weighted Collaborative Representation for Hyperspectral Images

Zhao

Feng

et al. 2020

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

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“…The former is called endmember extraction, and the latter is called abundance estimation. Hyperspectral unmixing has been used in many fields to improve the related performance, such as subpixel target detection, precise land cover mapping and Earth change detection [2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Quadratic Clustering-Based Simplex Volume Maximization for Hyperspectral Endmember Extraction

2022

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The existence of intra-class spectral variability caused by differential scene components and illumination conditions limits the improvement of endmember extraction accuracy, as most endmember extraction algorithms directly find pixels in the hyperspectral image as endmembers. This paper develops a quadratic clustering-based simplex volume maximization (CSVM) approach to effectively alleviate spectral variability and extract endmembers. CSVM first adopts spatial clustering based on simple linear iterative clustering to obtain a set of homogeneous partitions and uses spectral purity analysis to choose pure pixels. The average of the chosen pixels in each partition is taken as a representative endmember, which reduces the effect of local-scope spectral variability. Then an improved spectral clustering based on k-means is implemented to merge homologous representative endmembers to further reduce the effect of large-scope spectral variability, and final endmember collection is determined by the simplex with maximum volume. Experimental results show that CSVM reduces the average spectral angle distance on Samson, Jasper Ridge and Cuprite datasets to below 0.02, 0.06 and 0.09, respectively, provides the root mean square errors of abundance maps on Samson and Jasper Ridge datasets below 0.25 and 0.10, and exhibits good noise robustness. By contrast, CSVM provides better results than other state-of-the-art algorithms.

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“…In addition, there are some detection methods deigned based on machine learning, such as sparse target detection [14][15][16], sub-pixel target detection [17,18], and visual saliency target detection [19,20]. In recent years, there also have been efforts to apply and improve these target detection models in UAVs for offshore monitoring [21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

A YOLO-Based Target Detection Model for Offshore Unmanned Aerial Vehicle Data

Wang

Zhang

et al. 2021

Sustainability

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Target detection in offshore unmanned aerial vehicle data is still a challenge due to the complex characteristics of targets, such as multi-sizes, alterable orientation, and complex backgrounds. Herein, a YOLO-based detection model (YOLO-D) was proposed for target detection in offshore unmanned aerial vehicle data. Based on the YOLOv3 network, the residual module was improved by establishing dense connections and adding a dual-attention mechanism (CBAM) to enhance the use of features and global information. Then, the loss function of the YOLO-D model was added to the weight coefficients to increase detection accuracy for small-size targets. Finally, the feature pyramid network (FPN) was replaced by the secondary recursive feature pyramid network to reduce the impacts of a complicated environment. Taking the car, boat, and deposit near the coastline as the targets, the proposed YOLO-D model was compared against other models, including the faster R-CNN, SSD, YOLOv3, and YOLOv5, to evaluate its detection performance. The results showed that the evaluation metrics of the YOLO-D model, including precision (Pr), recall (Re), average precision (AP), and the mean of average precision (mAP), had the highest values. The mAP of the YOLO-D model increased by 37.95%, 39.44%, 28.46%, and 5.08% compared to the faster R-CNN, SSD, YOLOv3, and YOLOv5, respectively. The AP of the car, boat, and deposit reached 96.24%, 93.70%, and 96.79% respectively. Moreover, the YOLO-D model had a higher detection accuracy than other models, especially in the detection of small-size targets. Collectively, the proposed YOLO-D model is a suitable model for target detection in offshore unmanned aerial vehicle data.

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Improving hyperspectral subpixel target detection using hybrid detection space

Cited by 6 publications

References 48 publications

A Spectral–Spatial Anomaly Target Detection Method Based on Fractional Fourier Transform and Saliency Weighted Collaborative Representation for Hyperspectral Images

A Spectral–Spatial Anomaly Target Detection Method Based on Fractional Fourier Transform and Saliency Weighted Collaborative Representation for Hyperspectral Images

Quadratic Clustering-Based Simplex Volume Maximization for Hyperspectral Endmember Extraction

A YOLO-Based Target Detection Model for Offshore Unmanned Aerial Vehicle Data

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