Deep learning-based methods have demonstrated significant breakthroughs in the application of hyperspectral image (HSI) classification. However, some challenging issues still exist, such as the overfitting problem caused by the limitation of training size with high-dimensional feature and the efficiency of spectral–spatial (SS) exploitation. Therefore, to efficiently model the relative position of samples within the generative adversarial network (GAN) setting, we proposed a dual-channel SS fusion capsule generative adversarial network (DcCapsGAN) for HSI classification. Dual channels (1-D-CapsGAN and 2-D-CapsGAN) are constructed by integrating the capsule network (CapsNet) with GAN for eliminating the mode collapse and gradient disappearance problem caused by traditional GAN. Meanwhile, octave convolution and multiscale convolution are integrated into the proposed model for further reducing the parameters of the CapsNet and extracting multiscale features. To further boost the classification performance, the SS channel fusion model is constructed to composite and switch the feature information of different channels, thereby facilitating the accuracy and robustness of the whole classification performance. Three commonly used HSI data sets are utilized to investigate the performance of the proposed DcCapsGAN model, and the performance of the experiment demonstrates that the proposed model can efficiently improve the classification accuracy and performance.

Deep learning (DL) has become a hot topic in the research field of hyperspectral image (HSI) classification. However, with increasing depth and size of deep learning methods, its application in mobile and embedded vision applications has brought great challenges. In this article, we address a network architecture search (NAS)-guided lightweight spectral–spatial attention feature fusion network (LMAFN) for HSI classification. The overall architecture of the proposed network is guided by several conclusions of NAS, which achieves fewer parameters and lower computation cost with deeper network structure by exploiting multiscale Ghost grouped with efficient channel attention (ECA) module for adaptively adjusting the weights of different channels. It helps fully extract spectral–spatial discriminant features to avoid information loss of the dimension reduction operation. Specifically, a multilayer feature fusion method is proposed to extract the fusion information of the spectral–spatial features of each layer by considering complementary information of different hierarchical structures. Therefore, high-lever spectral–spatial attributes are gradually exploited along with the increase in layers and the fusion of layers. The experimental verification on three real HSI data sets demonstrates that the proposed framework presents more satisfying classification performance and efficiency with deeper network structure and lower parameter size.

Building extraction is a popular topic in remote sensing image processing. Efficient building extraction algorithms can identify and segment building areas to provide informative data for downstream tasks. Currently, building extraction is mainly achieved by deep convolutional neural networks (CNNs) based on the U-shaped encoder–decoder architecture. However, the local perceptive field of the convolutional operation poses a challenge for CNNs to fully capture the semantic information of large buildings, especially in high-resolution remote sensing images. Considering the recent success of the Transformer in computer vision tasks, in this paper, first we propose a shifted-window (swin) Transformer-based encoding booster. The proposed encoding booster includes a swin Transformer pyramid containing patch merging layers for down-sampling, which enables our encoding booster to extract semantics from multi-level features at different scales. Most importantly, the receptive field is significantly expanded by the global self-attention mechanism of the swin Transformer, allowing the encoding booster to capture the large-scale semantic information effectively and transcend the limitations of CNNs. Furthermore, we integrate the encoding booster in a specially designed U-shaped network through a novel manner, named the Swin Transformer-based Encoding Booster- U-shaped Network (STEB-UNet), to achieve the feature-level fusion of local and large-scale semantics. Remarkably, compared with other Transformer-included networks, the computational complexity and memory requirement of the STEB-UNet are significantly reduced due to the swin design, making the network training much easier. Experimental results show that the STEB-UNet can effectively discriminate and extract buildings of different scales and demonstrate higher accuracy than the state-of-the-art networks on public datasets.

Anomaly detection (AD) has attracted remarkable attention in hyperspectral image (HSI) processing fields, most existing deep learning (DL) based algorithms indicate dramatic potential for detecting anomaly samples through specific training process under current scenario. However, the limited prior information and the catastrophic forgetting problem indicate crucial challenges for existing DL structure in open scenarios cross-domain detection. In order to improve the detection performance, a novel capsule differential adversarial continual learning framework (CL-CaGAN) is proposed to elevate the cross-scenario learning performance for facilitating the real application of DL-based structure in hyperspectral anomaly detection (HAD) task. First, a modified capsule structure with adversarial learning network is constructed to estimate the background distribution for surmounting the deficiency of prior information. To mitigate the catastrophic forgetting phenomenon, clustering-based sample replay strategy and a designed extra self-distillation regularization are integrated for merging the history and future knowledge in continual AD task, while the discriminative learning ability from previous detection scenario to current scenario are retained by the elaborately designed structure with continual learning strategy. In addition, the differentiable enhancement is enforced to augment the generation performance of the training data for further stabilizing the training process with better convergence, this procedure further efficiently consolidates the reconstruction ability of background samples. To verify the effectiveness of our proposed CL-CaGAN, we conduct experiments on several real HSIs, the results indicate that the proposed CL-CaGAN demonstrates higher detection performance and continuous learning capacity for mitigating the catastrophic forgetting under cross-domain scenarios.

Deep learning (DL) algorithms have demonstrated important breakthroughs for hyperspectral image (HSI) classification. Despite the remarkable success of DL, the burden of a manually designed DL structure with increased depth and size aroused the difficulty for the application in the mobile and embedded devices in a real application. To tackle this issue, in this paper, we proposed an efficient lightweight attention network architecture search algorithm (EL-NAS) for realizing an efficient automatic design of a lightweight DL structure as well as improving the classification performance of HSI. First, aimed at realizing an efficient search procedure, we construct EL-NAS based on a differentiable network architecture search (NAS), which can greatly accelerate the convergence of the over-parameter supernet in a gradient descent manner. Second, in order to realize lightweight search results with high accuracy, a lightweight attention module search space is designed for EL-NAS. Finally, further for alleviating the problem of higher validation accuracy and worse classification performance, the edge decision strategy is exploited to perform edge decisions through the entropy of distribution estimated over non-skip operations to avoid further performance collapse caused by numerous skip operations. To verify the effectiveness of EL-NAS, we conducted experiments on several real-world hyperspectral images. The results demonstrate that the proposed EL-NAS indicates a more efficient search procedure with smaller parameter sizes and high accuracy performance for HSI classification, even under data-independent and sensor-independent scenarios.

Occlusions and complex backgrounds are common factors that hinder many computer vision applications. In a street scene, the challenge of accurately predicting pedestrian trajectories comes from the complexity of human behavior and the diversity of the external environment. It is difficult, if not impossible, to extract relevant information to accurately predict pedestrian trajectories in dynamic scenes. Synthetic aperture imaging (SAI) uses an array of cameras to mimic a camera with a large virtual convex lens by projecting images of a scene from different views onto a virtual focal plane. It is commonly used to reconstruct occluded objects, and in a street scene, can provide observation of pedestrians occluded by other objects and pedestrians. In this paper, we propose a joint prediction method based on autofocusing of SAI to predict pedestrian trajectories in dynamic scenes. The main contributions of this paper include: 1) The task of pedestrian trajectory prediction in dynamic scenarios is redefined as pedestrian trajectory prediction and SAI autofocusing from a practical but more challenging perspective. 2) The proposed method is based on an existing SAI-based method to extract information in heavily occluded views, which can obtain more accurate results but with less computational cost and without using other sensors such as LiDAR or depth cameras. 3) A new pedestrian trajectory prediction model, an attention-based trajectory prediction variational autoencoder (ATP-VAE), is proposed to extract complex human behavior and social interactions in dynamic scenes through a new Intention Attention Unit. The experimental results on multiple public datasets show that the proposed method achieves state-of-the-art results in the first-person perspective and in aerial view.