Falls are a leading cause of injury and death among the elderly, making fall detection critically important. Traditional wearable sensors and environmental devices have limitations in terms of comfort, convenience, and accuracy. With the advancement of artificial intelligence and the Internet of Things (IoT), camera-based fall detection has become a research focus, but challenges such as occlusion and poor lighting conditions remain. To address these issues, this study introduces an innovative model named Fall-Mamba. Compared to previous approaches, Fall-Mamba utilizes a Cross-Attention mechanism to fuse video and audio data, significantly enhancing the comprehensive understanding and detection performance of fall events. Additionally, the model incorporates a Multi-Head Temporal Attention mechanism and Frame Masking strategy, improving its ability to capture key frames and increasing its robustness. Extensive experiments conducted on multi-view, multi-scene datasets, including Le2i, URFD, and Multicam, demonstrate the superior performance of Fall-Mamba, achieving an accuracy of 99.63% and exhibiting high robustness. This technology provides strong protection for the safety of the elderly in IoT-enabled smart homes. The code has been published at https://github.com/DHUspeech/fall-mamba.

Conventional audio steganography methods typically require embedding secret information into the carrier, making them vulnerable to steganalysis. To address this issue, we propose a novel coverless audio steganography method that hides information by generating carriers and establishing mapping rules rather than embedding data directly. Our approach leverages a differential privacy clustering algorithm to cluster audio data and select representative audio files, thereby enhancing the security of the steganography. Additionally, we introduce an improved audio feature extraction method that combines traditional Mel-frequency cepstral coefficients (MFCC) with global statistical information, significantly boosting the robustness of the secret information against common audio attacks, particularly time-stretching attacks. Experimental results show that our method achieves a robustness rate of up to 95% against time-stretching and maintains an average security accuracy rate exceeding 97% across various attack scenarios. The proposed method ensures that the audio carrier remains unaltered, thus effectively resisting detection by steganalysis tools. This innovative approach provides a practical and efficient solution for the secure transmission of information in the digital era.

Detecting cracks is a crucial task to ensure the safety of engineering. However, the complexity of the crack background and the highly uneven pixel ratio between the crack and the background significantly limit the effectiveness of detection algorithms. Therefore, an efficient and innovative algorithm for crack detection is proposed, which utilizes a transformer and a multilevel cross-scale weighted feature fusion module, as well as a progressive transfer learning (TL) strategy. Firstly, in terms of existing algorithms, there are limitations in terms of high leakage and false detection rates, as well as a lack of generalization ability, therefore, a F2N-CrackNet model is proposed, which consists of two efficient feature fusion modules and a Nested Multi-level Attention with Atrous Spatial Pyramid Pooling (NMA-ASPP). Secondly, to tackle sparse and unevenly distributed bridge crack data, enhance model detection capabilities, and expedite convergence, a progressive three-stage hybrid TL strategy incorporating across-domain, inter-domain, and inner-domain transferring is proposed, by migrating knowledge from related domains. Finally, to enrich experimental data supporting and validating the proposed model and the transfer learning approach, a new group of experimental data set is offered, comprising hundreds of high-resolution bridge crack images collecting in Shanghai by using a digital camera. Additionally, an efficient human-computer interactive labeling approach combined with morphological processing is devised. Experiments conducted on both open-source and private datasets reveal substantial performance improvements in crack detection achieved by the proposed model and TL strategy. On private data, the model achieves an 81.28% MIoU, exceeding or approaching the performance of state-of-the-art (SOTA) methods, while simultaneously reducing training time by 5–8 seconds per round compared to SOTA. Meanwhile, the performance on multiple sets of open-source data is similarly elevated.

Hypersharpening (namely, hyperspectral (HS) and multispectral (MS) image fusion) aims at enhancing the spatial resolution of HS image via an auxiliary higher resolution MS image. Currently, numerous hypersharpening methods are proposed successively, among which the unmixing-based approaches have been widely researched and demonstrated their effectiveness in the spectral fidelity aspect. However, existing unmixing-based fusion methods substantially employ mathematical techniques to solve the spectral mixture model, without taking full advantage of the collaborative spatial–spectral information that is usually helpful for abundance estimation improvement. To overcome this drawback, in this article, a novel unmixing-based HS and MS image fusion method, via a convolutional neural network (CNN), is proposed to promote spectral fidelity. The main idea of this work is to use CNN to fully explore the spatial information and the spectral information of both HS and MS images simultaneously, thereby enhancing the accuracy of estimating the abundance maps. Experiments on four simulated and real remote sensing data sets demonstrate that the proposed method is beneficial to the spectral fidelity of the fused images compared with some state-of-the-art algorithms. Meanwhile, it is also easy to implement and has a certain advantage in running time.

Owing to the limits of incident energy and hardware system, hyperspectral (HS) images always suffer from low spatial resolution, compared with multispectral (MS) or panchromatic (PAN) images. Therefore, image fusion has emerged as a useful technology that is able to combine the characteristics of high spectral and spatial resolutions of HS and PAN/MS images. In this paper, a novel HS and PAN image fusion method based on convolutional neural network (CNN) is proposed. The proposed method incorporates the ideas of both hyper-sharpening and MS pan-sharpening techniques, thereby employing a two-stage cascaded CNN to reconstruct the anticipated high-resolution HS image. Technically, the proposed CNN architecture consists of two sub-networks, the detail injection sub-network and unmixing sub-network. The former aims at producing a latent high-resolution MS image, whereas the latter estimates the desired high-resolution abundance maps by exploring the spatial and spectral information of both HS and MS images. Moreover, two model-training fashions are presented in this paper for the sake of effectively training our network. Experiments on simulated and real remote sensing data demonstrate that the proposed method can improve the spatial resolution and spectral fidelity of HS image, and achieve better performance than some state-of-the-art HS pan-sharpening algorithms.

Hyperspectral (HS) and multispectral (MS) image fusion has attracted great attention during the past decades. Numerous of fusion methods have been developed and shown their effectiveness particularly on simulated data. Nonetheless, for real remote sensing data, the different acquisition times or conditions result in a serious spectral distortion and severely affect the fusion quality. Yet very few works have considered this issue. In this paper, a spectral modulation (SM) method is proposed to better maintain the spectral information of the HS data when fusing with MS data. The goal is to generate an adjusted MS image that would have been observed under the same imaging conditions with the corresponding HS sensor. Experiments on two HS and MS data sets acquired by different platforms demonstrate that the proposed method is beneficial to the spectral fidelity and spatial enhancement of the fused image compared with some state-of-the-art fusion techniques.

Change detection techniques for remote sensing images are increasingly applied to many fields, such as disaster monitoring, vegetation coverage analysis and so on. How to improve the accuracy of detection has been a critical topic that confuse the researchers for a long time. In this paper, a method combining multiscale segmentation and fusion for high-resolution images is presented. The strategy of multiscale segmentation is to segment the same image several times under different scales, and then extract the features of objects. After, the features are used as inputs of change detection. The final results are achieved by decision-level fusion. The experiments show that, comparing with other typical methods, the method proposed in this paper has a superior performance in change detection for high-resolution images.

Multi-resolution analysis (MRA) is a useful technique for multispectral (MS) image pan-sharpening. To overcome the limitation of the conventional wavelet-based method, multi-directional MRA approaches have been proposed to provide an efficient directional information representation. In this paper, we present a new hybrid method for MS image pan-sharpening that uses non-subsampled contourlet transform (NSCT) as the MRA tool, and followed by a high-pass detail-injection model. Particularly, to further enhance the spatial resolution of the MS image, and in the meantime, produce a high spectral quality, we take account of the dissimilarity between the spatial details of MS image and decomposed panchromatic image of NSCT, and introduce a fast and effective means to deal with the problem. Experiments on four remote sensing data sets show the superiority of the presented approach over other typical pan-sharpening methods.

This paper proposes a target detector based on kernel sparse and spatial constraint for hyperspectral imagery (HSI). Due to the nonlinear and structural features of HSI data, sparse representation and spatial constraint are taken into consideration. Firstly, we construct a dictionary to represent the target pixels within a small neighborhood by a linear combination of samples. Then, these targets pixels are projected into the high-dimensional feature space through kernel function and orthogonal matching pursuit (OMP) are kernelized to obtain recovered sparse coefficient vector. By comparing the residuals of background and target to determine the type of pixel, the preliminary detection result can be achieved. Lastly, a spatial over-complete basis matrix is used to revise the initial detection result. The experimental results show that the proposed detector has better detection performance than several typical detectors.

Region-of-interest (ROI) detection techniques are of great importance in the analysis of remote sensing images, especially in target detection, since the size of the image to be dealt with grows substantially with the improvement of spatial resolution. Most of current studies are not aiming at the specific type of object area detection, and the processed images are rather small compared to the size of the raw data acquired by high-resolution satellite. In this letter, a hierarchical task-driven ROI detection method, based on saliency and density, is proposed to address the detection of the potential object areas in large-scale remote sensing images. The proposed saliency and density-based detection method (SDBD) integrates bottom-up and top-down strategies, where the saliency-based multilevel histogram contrast is presented in the bottom-up phase to obtain the preliminary regions, while the centroid density distribution index (CDDI) is defined in the top-down scheme to refine the previous results. Specifically, superpixel segmentation is introduced in this letter to narrow down the ROI candidates. SDBD is capable of extracting ROI of different objects by adjusting the threshold of CDDI. The experiments are conducted on two data sets to extract ROIs of storage tanks and residence. Experimental results demonstrate that the proposed method is effective in identifying ROI in large-scale data.

The polarized hyperspectral images (PHSI) include the multidimensional information of polarization, spectral, spatial and radiant features, which provide more information about objects and background than traditional spectrum or intensity ones. However, the conventional object detection algorithms of PHSI are based on the Stokes vector, which mainly take advantage of the spectral information, and often ignore the continuous variation characteristics of polarized dimension, being similar to the spectrum. What is more, when the data is large, it will increase the calculative difficulty and error. Hence, a fourth-order tensor matched filtering (FTMF) is proposed in this paper, which is applied to the original data directly without extracting the Stokes vector, and achieves the combined utilization of polarization and spectrum. The experimental results also show that the proposed method in this paper is more suitable for the object detection of the PHSI.

Pan-sharpening is designed to estimate multi-/hyperspectral (MS/HS) images that would have been observed with a sensor at higher resolution. It is a very important issue for many remote sensing and mapping applications. This letter proposes an improved pan-sharpening algorithm based on the ARSIS concept under the assumption that missing information of a low-resolution MS/HS image is linked to the high frequencies of MS/HS and panchromatic (PAN) images. The main object of this letter is to exhibit a multilevel interband structure model that better considers the inherent relationship between the hierarchical structures of MS/HS and PAN images. Several groups of data sets are used to demonstrate the performance of the proposed method. The results show that the proposed method outperforms the existing ARSIS-based and some other fusion techniques and can be extended to HS image sharpening as well.

In this paper, we propose a self-learning approach for remote sensing image classification. The main work of this paper aims at providing a new framework of semisupervised learning technique for multiple-source synergetic classification, thereby improving the classification accuracy under the condition of small samples. Considering the high spectral resolution of a hyperspectral (HS) image and the high spatial resolution of a panchromatic (PAN) image, the proposed approach combines image segmentation with an active learning algorithm and adopts a standard active learning method for a self-learning strategy, in which the learning algorithm automatically selects informative unlabeled samples by itself according to their collaborative spatial-spectral features and the predicted information of a spectral-based classifier. This way, no extra cost of human expertise is required for labeling the selected pixels when compared with conventional active learning methods. Experiments on three data sets, including HS and PAN images, indicate that our proposed approach has a great enhancement on overall classification accuracy compared with classical supervised algorithms and turns out to be a promising strategy in synergetic classification of HS and PAN images.

In change detection analysis, it is important to distinguish the real change targets and pseudo change targets accurately. Supervised change detection has been regarded as the best way to reduce the effects of pseudo change information. This is because human visual system has the ability to find the real changes. By imitating human visual characteristic, visual attention mechanism can bring the improvement of accuracy and speed of unsupervised change detection. In this paper, a change detection approach based on visual attention mechanism is proposed to reduce the influence of pseudo change information. Experiments show that the proposed method significantly reduces the false alarm rate and missed alarm rate and also shows insensitive to noise.

Fusion of remote sensing images has received considerable attention during the last two decades, due to the contradiction between the spatial and spectral resolutions of remote sensing images. In this paper, a new fusion method based on Modified Gram-Schmidt (MGS) and stationary wavelet transform (SWT) has been presented to merge multispectral and panchromatic images. In order to show the effectiveness of the proposed method, experiments have been conducted on two datasets of QuickBird and WorldView-II, compared with several typical methods. Experimental results show that the proposed method performs better than the other methods both in the enhancement of spatial details and the preservation of spectral information.