Attention-based Sparse and Collaborative Spectral Abundance Learning for Hyperspectral Subpixel Target Detection.

The subpixel target detection in hyperspectral image processing persists as a formidable challenge. In this paper, we present a novel subpixel target detector termed attention-based sparse and collaborative spectral abundance learning for subpixel target detection in hyperspectral images. To help suppress background during subpixel target detection, the proposed method presents a pixel attention-based background sample selection method for background dictionary construction. Besides, the proposed method integrates a band attention-based spectral abundance learning model, replete with sparse and collaborative constraints, in which the band attention map can contribute to enhancing the discriminative ability of the detector in identifying targets from backgrounds. Ultimately, the detection result of the proposed detector is achieved by the learned target spectral abundance after solving the designed model using the alternating direction method of multipliers algorithm. Rigorous experiments conducted on four benchmark datasets, including one simulated and three real-world datasets, validate the effectiveness of the detector with the probability of detection of 90.88%, 96.86%, and 97.79% on the PHI, RIT Campus, and Reno Urban data, respectively, under fixed false alarm rate equal 0.01, indicating that the proposed method yields superior hyperspectral subpixel detection performance and outperforms existing methodologies.

Learning Single Spectral Abundance for Hyperspectral Subpixel Target Detection.

Due to the limitation of target size and spatial resolution, targets of interest in hyperspectral images (HSIs) often appear as subpixel targets, which makes hyperspectral target detection still faces an important bottleneck, that is, subpixel target detection. In this article, we propose a new detector by learning single spectral abundance for hyperspectral subpixel target detection (denoted as LSSA). Different from most existing hyperspectral detectors that are designed based on a match of the spectrum assisted by spatial information or focusing on the background, the proposed LSSA addresses the problem of detecting subpixel targets by learning a spectral abundance of the target of interest directly. In LSSA, the abundance of the prior target spectrum is updated and learned, while the prior target spectrum is fixed in a nonnegative matrix factorization (NMF) model. It turns out that such a way is quite effective to learn the abundance of subpixel targets and contributes to detecting subpixel targets in hyperspectral imagery (HSI). Numerous experiments are conducted on one simulated dataset and five real datasets, and the results indicate that the LSSA yields superior performance in hyperspectral subpixel target detection and outperforms its counterparts.

Collaborative-guided spectral abundance learning with bilinear mixing model for hyperspectral subpixel target detection.

Detecting subpixel targets is a considerably challenging issue in hyperspectral image processing and interpretation. Most of the existing hyperspectral subpixel target detection methods construct detectors based on the linear mixing model which regards a pixel as a linear combination of different spectral signatures. However, due to the multiple scattering, the linear mixing model cannot​ illustrate the multiple materials interactions that are nonlinear and widespread in real-world hyperspectral images, which could result in unsatisfactory performance in detecting subpixel targets. To alleviate this problem, this work presents a novel collaborative-guided spectral abundance learning model (denoted as CGSAL) for subpixel target detection based on the bilinear mixing model in hyperspectral images. The proposed CGSAL detects subpixel targets by learning a spectral abundance of the target signature in each pixel. In CGSAL, virtual endmembers and their abundance help to achieve good accuracy for modeling nonlinear scattering accounts for multiple materials interactions according to the bilinear mixing model. Besides, we impose a collaborative term to the spectral abundance learning model to emphasize the collaborative relationships between different endmembers, which contributes to accurate spectral abundance learning and further help to detect subpixel targets. Plentiful experiments and analyses are conducted on three real-world and one synthetic hyperspectral datasets to evaluate the effectiveness of the CGSAL in subpixel target detection. The experiment results demonstrate that the CGSAL achieves competitive performance in detecting subpixel targets and outperforms other state-of-the-art hyperspectral subpixel target detectors.

PMMA-Grafted Calcium Sulfate Whiskers for Applications as Fillers in PVC.

Calcium sulfate whiskers (CSWs) were hydroxylated with a sodium hydroxide (NaOH) solution and isolated for subsequent treatment with an ethanolic 3-(methacryloxy)propyltrimethoxysilane (KH570) solution to introduce C=C double bonds on the CSWs' surfaces. Then, CSW-g-PMMA was prepared by grafting polymethyl methacrylate (PMMA) onto the surface of modified CSW using in situ dispersion polymerization. The CSW-g-PMMA was used as a filler and melt-blended with polyvinyl chloride (PVC) to prepare PVC-based composites. The surface chemical structure, PMMA grafting rate, and hydrophobic properties of CSW-g-PMMA were analyzed using X-ray diffraction, diffuse reflectance Fourier-transform infrared spectroscopy, thermogravimetric analysis, and water contact angle measurements, respectively. The effects of the CSW-g-PMMA filler on the mechanical properties of the CSW-PMMA/PVC composites were also investigated. The results showed that NaOH treatment significantly increased the number of hydroxyl groups on the surface of the CSWs, which facilitated the introduction of KH570. PMMA was successfully grafted onto the KH570 with a grafting rate of 14.48% onto the surface of the CSWs. The CSW-g-PMMA had good interfacial compatibility and adhesion properties with the PVC matrix. The tensile, flexural, and impact strengths of the CSW-g-PMMA/PVC composite reached 39.28 MPa, 45.69 MPa, and 7.05 kJ/m2, respectively, which were 38.55%, 30.99%, and 20.10% higher than those of the CSW/PVC composite and 54.52%, 40.80%, and 32.52% higher than those of pure PVC, respectively. This work provides a new method for surface modification of inorganic fillers, resource utilization, and high value-added application of CSWs from phosphogypsum.