With the development of remote sensing technology, classification has become a meaningful way to explore the rich information in hyperspectral images (HSIs). However, various environmental factors may cause noise and shadow areas in HSIs, resulting in weak signals and difficulties in fully utilizing information. In addition, classification methods based on deep learning have made considerable progress, but features extracted from most networks have much redundancy. Therefore, a method based on two-dimensional dynamic stochastic resonance (2D DSR) shadow enhancement and convolutional neural network (CNN) classification combined with an attention mechanism (AM) for HSIs is proposed in this paper. Firstly, to protect the spatial correlation of HSIs, an iterative equation of 2D DSR based on the pixel neighborhood relationship was derived, which made it possible to perform matrix SR in the spatial dimension of the image, instead of one-dimensional vector resonance. Secondly, by using the noise in the shadow area to generate resonance, 2D DSR can help increase the signals in the shadow regions by preserving the spatial characteristics, and enhanced HSIs can be obtained. Then, a 3DCNN embedded with two efficient channel attention (ECA) modules and one convolutional block attention module (CBAM) was designed to make the most of critical features that significantly affect the classification accuracy by giving different weights. Finally, the performance of the proposed method was evaluated on a real-world HSI, and comparative studies were carried out. The experimental results showed that the proposed approach has promising prospects in HSIs’ shadow enhancement and information mining.

Accurate detection of power fittings is crucial for identifying defects or faults in these components, which is essential for assessing the safety and stability of the power system. However, the accuracy of fittings detection is affected by a complex background, small target sizes, and overlapping fittings in the images. To address these challenges, a fittings detection method based on the dynamic graph convolutional neural network (DGCNN) and U-shaped network (U-Net) is proposed, which combines three-dimensional detection with two-dimensional object detection. Firstly, the bi-level routing attention mechanism is incorporated into the lightweight U-Net network to enhance feature extraction for detecting the fittings boundary. Secondly, pseudo-point cloud data are synthesized by transforming the depth map generated by the Lite-Mono algorithm and its corresponding RGB fittings image. The DGCNN algorithm is then employed to extract obscured fittings features, contributing to the final refinement of the results. This process helps alleviate the issue of occlusions among targets and further enhances the precision of fittings detection. Finally, the proposed method is evaluated using a custom dataset of fittings, and comparative studies are conducted. The experimental results illustrate the promising potential of the proposed approach in enhancing features and extracting information from fittings images.

The cocktail party problem can be more effectively addressed by leveraging the speaker’s visual and audio information. This paper proposes a method to improve the audio’s separation using two visual cues: facial features and lip movement. Firstly, residual connections are introduced in the audio separation module to extract detailed features. Secondly, considering the video stream contains information other than the face, which has a minimal correlation with the audio, an attention mechanism is employed in the face module to focus on crucial information. Then, the loss function considers the audio-visual similarity to take advantage of the relationship between audio and visual completely. Experimental results on the public VoxCeleb2 dataset show that the proposed model significantly enhanced SDR, PSEQ, and STOI, especially 4 dB improvements in SDR.

Underwater object detection is a key technique for marine research. Traditional red green blue (RGB) imaging suffers from short detection ranges and serious color distortion in complex underwater environments. While hyperspectral images contain detailed spectral information, their high dimensionality increases computational costs and noise vulnerability, and high-quality underwater samples are difficult to obtain. A continuity and label-constrained band selection (CLBS) method is presented to address three common defects of existing underwater band selection techniques: target–background confusion, underutilization of spatial features and poor spectral continuity. Three metrics, namely target–background contrast (TBC), target region entropy (Entropy) and spectral–spatial contrast (SSC), are designed for band evaluation. Geometric mean fusion is adopted to suppress extreme values, and spectral continuity constraints are applied to determine the optimal band number. Meanwhile, a multi-category underwater hyperspectral dataset is constructed. Quantitative experiments are carried out on two fully annotated classes (metal and plastic). CLBS reduces the 300 original bands to 209, delivering a 30.3% dimensionality reduction with well-preserved spectral continuity. On a fixed train-validation partition, the 3DCNN+2DCNN model using CLBS-selected bands reaches an F1-score of 94.29%, a Precision of 100.00% and a Recall of 89.19%. Ten repeated tests with random seeds yield averaged results of 94.96 ± 1.13%, 98.44 ± 1.65% and 91.82 ± 3.31% for F1-score, Precision and Recall, respectively, demonstrating reliable performance. Comparative results show that CLBS outperforms conventional feature extraction and various supervised/unsupervised band selection methods. It achieves an excellent trade-off between dimensionality reduction and spectral feature preservation for underwater hyperspectral image processing.