Hyperspectral image (HSI) unmixing has attracted increasing research interests in recent decades. The major difficulty of it lies in that the endmembers and the associated abundances need to be separated from highly mixed observation data with few a priori information. Recently, sparsity-constrained nonnegative matrix factorization (NMF) algorithms have been proved effective for hyperspectral unmixing (HU) since they can sufficiently utilize the sparsity property of HSIs. In order to improve the performance of NMF-based unmixing approaches, spectral and spatial constrains have been added into the unmixing model, but spectral-spatial joint structure is required to be more accurately estimated. To exploit the property that similar pixels within a small spatial neighborhood have higher possibility to share similar abundances, hypergraph structure is employed to capture the similarity relationship among the spatial nearby pixels. In the construction of a hypergraph, each pixel is taken as a vertex of the hypergraph, and each vertex with its k nearest spatial neighboring pixels form a hyperedge. Using the hypergraph, the pixels with similar abundances can be accurately found, which enables the unmixing algorithm to obtain promising results. Experiments on synthetic data and real HSIs are conducted to investigate the performance of the proposed algorithm. The superiority of the proposed algorithm is demonstrated by comparing it with some state-of-the-art methods.

In order to overcome the difficulty of key management in “one time pad” encryption schemes and also resist the attack of chosen plaintext, a new image encryption algorithm based on chaos and SHA-256 is proposed in this paper. The architecture of confusion and diffusion is adopted. Firstly, the surrounding of a plaintext image is surrounded by a sequence generated from the SHA-256 hash value of the plaintext to ensure that each encrypted result is different. Secondly, the image is scrambled according to the random sequence obtained by adding the disturbance term associated with the plaintext to the chaotic sequence. Third, the cyphertext (plaintext) feedback mechanism of the dynamic index in the diffusion stage is adopted, that is, the location index of the cyphertext (plaintext) used for feedback is dynamic. The above measures can ensure that the algorithm can resist chosen plaintext attacks and can overcome the difficulty of key management in “one time pad” encryption scheme. Also, experimental results such as key space analysis, key sensitivity analysis, differential analysis, histograms, information entropy, and correlation coefficients show that the image encryption algorithm is safe and reliable, and has high application potential.

Hyperspectral unmixing is an important step for material classification and recognition. Recently, nonnegative matrix factorization (NMF) has been utilized to unmix the hyperspectal imagery due to the advantage that it needs no assumption for the presence of pure pixels and can determine the endmembers and abundances simultaneously. In order to improve its unmixing performance, sparsity-constrained NMF has been demonstrated to be an efficient approach. The very recent study on L _1/2 regularization theory in compressive sensing (CS) and sparsity-constrained NMF show that the L _1/2 regularizer can yield stronger sparsity-promoting solutions than L ₁ or L ₂ regularizer. However, the L _1/2 regularization can result in a complex nonconvex optimization problem that is hard to solve efficiently. In this paper, we propose a fast and efficient adaptive half-thresholding algorithm for hyperspectral unmixing based on L _1/2 sparsity-constrained NMF. In the proposed algorithm, iterative half-thresholding procedure that has been proved to be an efficient method for solving L _1/2 regularization problem in CS is hybridized with the multiplicative update rule of standard NMF to deal with the L _1/2 sparsity-constrained NMF, which can give sparser and better unmixing results than the alternative algorithms. Furthermore, the data sparsity information can be incorporated into the algorithm to adaptively adjust the regularization parameter of the model to improve algorithm performance and usability. The effectiveness of proposed algorithm was demonstrated by comparing with the representative algorithms on synthetic and real hyperspectral data.

The YOLO series of object detection methods have achieved significant success in a wide range of computer vision tasks due to their efficiency and accuracy. However, detecting small objects in UAV images remains a formidable challenge due to factors such as a low resolution, complex background interference, and significant scale variations, which collectively degrade the quality of feature extraction and limit detection performance. To address these challenges, we propose the receptive field attention-guided YOLO (RFAG-YOLO) method, an advanced adaptation of YOLOv8 tailored for small-object detection in UAV imagery, with a focus on improving feature representation and detection robustness. To this end, we introduce a novel network building block, termed the receptive field network block (RFN block), which leverages dynamic kernel parameter adjustments to enhance the model’s ability to capture fine-grained local details. To effectively harness multi-scale features, we designed an enhanced FasterNet module based on RFN blocks as the core component of the backbone network in RFAG-YOLO, enabling robust feature extraction across varying resolutions. This approach achieves a balance of semantic information by employing staged downsampling and a hierarchical arrangement of RFN blocks, ensuring optimal feature representation across different resolutions. Additionally, we introduced a Scale-Aware Feature Amalgamation (SAF) component prior to the detection head of RFAG-YOLO. This component employs a scale attention mechanism to dynamically weight features from both higher and lower layers, facilitating richer information flow and significantly improving the model’s robustness to complex backgrounds and scale variations. Experimental results on the VisDrone2019 dataset demonstrated that RFAG-YOLO outperformed state-of-the-art models, including YOLOv7, YOLOv8, YOLOv10, and YOLOv11, in terms of detection accuracy and efficiency. In particular, RFAG-YOLO achieved an mAP50 of 38.9%, representing substantial improvements over multiple baseline models: a 12.43% increase over YOLOv7, a 5.99% improvement over YOLOv10, and significant gains of 16.12% compared to YOLOv8n and YOLOv11. Moreover, compared to the larger YOLOv8s model, RFAG-YOLO achieved 97.98% of its mAP50 performance while utilizing only 53.51% of the parameters, highlighting its exceptional efficiency in terms of the performance-to-parameter ratio and making it highly suitable for resource-constrained UAV applications. These results underscore the substantial potential of RFAG-YOLO for real-world UAV applications, particularly in scenarios demanding accurate detection of small objects under challenging conditions such as varying lighting, complex backgrounds, and diverse scales.

Spectral unmixing is an important technique for quantitatively analyzing hyperspectral remote sensing images. Recently, constrained nonnegative matrix factorization (NMF) has been demonstrated to be a powerful tool for spectral unmixing. However, acquiring the problem-dependent prior knowledge and incorporating it into NMF as effective constraints is a challenging task. In this paper, a multiple clustering guided NMF (MCG-NMF) unmixing approach is proposed under a self-supervised framework which has been used to effectively learn high-level semantic information from the data with a surrogate task in many applications. Specifically, in order to provide self-supervised information to guide the NMF- based unmixing model, multiple clustering is integrated into the optimization process of NMF. Moreover, by introducing interaction between each clustering and the unmixing procedure, more accurate proximate endmember signatures and proximate abundance distributions are expected to be acquired and used to impose self-supervised constraints on endmembers and abundances respectively. Consequently, effective prior information about endmember signatures and abundance distributions is captured and simultaneously integrated into NMF as valuable constraints to promote unmixing performance. Experiments are conducted on both synthetic data and real HSIs, and the superior performance of our method is shown by comparing it with several state-of-the-art algorithms.

Hyperspectral unmixing is an important technique for analyzing remote sensing images which aims to obtain a collection of endmembers and their corresponding abundances. In recent years, non-negative matrix factorization (NMF) has received extensive attention due to its good adaptability for mixed data with different degrees. The majority of existing NMF-based unmixing methods are developed by incorporating additional constraints into the standard NMF based on the spectral and spatial information of hyperspectral images. However, they neglect to exploit the nature of imbalanced pixels included in the data, which may cause the pixels mixed with imbalanced endmembers to be ignored, and thus the imbalanced endmembers generally cannot be accurately estimated due to the statistical property of NMF. To exploit the information of imbalanced samples in hyperspectral data during the unmixing procedure, in this paper, a cluster-wise weighted NMF (CW-NMF) method for the unmixing of hyperspectral images with imbalanced data is proposed. Specifically, based on the result of clustering conducted on the hyperspectral image, we construct a weight matrix and introduce it into the model of standard NMF. The proposed weight matrix can provide an appropriate weight value to the reconstruction error between each original pixel and the reconstructed pixel in the unmixing procedure. In this way, the adverse effect of imbalanced samples on the statistical accuracy of NMF is expected to be reduced by assigning larger weight values to the pixels concerning imbalanced endmembers and giving smaller weight values to the pixels mixed by majority endmembers. Besides, we extend the proposed CW-NMF by introducing the sparsity constraints of abundance and graph-based regularization, respectively. The experimental results on both synthetic and real hyperspectral data have been reported, and the effectiveness of our proposed methods has been demonstrated by comparing them with several state-of-the-art methods.