Recently, deep learning methods based on the combination of spatial and spectral features have been successfully applied in hyperspectral image (HSI) classification. To improve the utilization of the spatial and spectral information from the HSI, this paper proposes a unified network framework using a three-dimensional convolutional neural network (3-D CNN) and a band grouping-based bidirectional long short-term memory (Bi-LSTM) network for HSI classification. In the framework, extracting spectral features is regarded as a procedure of processing sequence data, and the Bi-LSTM network acts as the spectral feature extractor of the unified network to fully exploit the close relationships between spectral bands. The 3-D CNN has a unique advantage in processing the 3-D data; therefore, it is used as the spatial-spectral feature extractor in this unified network. Finally, in order to optimize the parameters of both feature extractors simultaneously, the Bi-LSTM and 3-D CNN share a loss function to form a unified network. To evaluate the performance of the proposed framework, three datasets were tested for HSI classification. The results demonstrate that the performance of the proposed method is better than the current state-of-the-art HSI classification methods.

Most current research primarily focuses on improving experimental accuracy using large models, often neglecting the deployment challenges. There is a growing need for lightweight algorithms in certain remote sensing devices. Moreover, remote sensing images (RSIs) often contain numerous small, densely distributed targets, which pose significant detection challenges. To address these issues, we have improved the YOLOv8s network and developed a lightweight remote sensing object detection (RSOD) network based on multiscale features fusion and context information (MFFCI-YOLOv8). This network combines multiscale feature fusion and contextual information to accurately detect objects in RSIs. Firstly, we introduce the lightweight CSP bottleneck with attention (LCA) module, which utilizes partial convolution calculation and SimAM attention mechanisms to decrease the number of param-eters and computational complexity while enhancing feature extraction capabilities. Secondly, we design the gate spatial pyramid pooling fast (GSPPF) module to enhance the model's perception of scale and contextual information, thus improving the detection of small objects. Lastly, we employ the multiscale fusion lightweight neck (MFLNeck) module for more efficient multiscale feature fusion, preventing the loss of small objects. Compared to YOLOv8s, our overall model reduces the number of parameters by 7.7% and FLOPs by 11.9%. We validated the accuracy of MFFCI-YOLOv8 on two remote sensing datasets, NWPU VHR-10 and VisDrone. The experimental results demons-trate that our model offers a low computational cost and high detection accuracy compared to other RSOD models and other YOLO models.

The convolutional neural network (CNN) is widely used in the task of hyperspectral image (HSI) classification. However, for the HSI of three-dimensional characteristics, the 2D CNN-based methods will result in losing spatial-spectral information. To solve this problem, this paper proposes a multi-branch 3D-densely connected network for HSI classification. This network is able to reuse features to fully exploit the shallow spatial-spectral information of HSI. Meanwhile, the convolutional kernels of different sizes are used to extract multi-scale spatial-spectral features. Subsequently, spatial attention mechanisms are used to emphasize spatial features and increase the diversity of features. By introducing global average pooling instead of a fully connected layer, the number of parameters in the whole network will be reduced. In order to verify the performance of the proposed method, the experiment results conducted in the Indian Pines, the University of Pavia, Salinas Valley and Houston 2013 datasets show that the proposed method is better than the state-of-the-art methods.

Rural residential area is the core component of rural land, and the process of its transfer exists to guide the change of rural land use. Its evolution is closely related to population migration, so exploring the spatial and temporal evolution of the coupling type of human-land relationship and its driving mechanism is the core scientific basis for the optimal allocation of rural land resources. From the perspective of human-land relationship, this paper introduces spatio-temporal big data on the basis of 3S technology and constructs an elasticity coefficient model. The composite index method is also used to further explore the coupling types of rural human-land relations in the Luoyang region. At the same time, spatial autocorrelation and spatial autoregressive models were used to reveal the driving mechanism of the spatio-temporal evolution of rural human-land relationship types in the Luoyang region. The results showed that: (1) Against the background of the continuous reduction of the rural population in Luoyang, the scale of rural residential area first increases and then decreases, and spatially they continue to gather around the central city (2) At the township scale, the conflict between people and land tends to be moderated. The elasticity coefficient shows a zonal distribution pattern in the southwest of Luoyang region and the townships around the central city. (3) There is a non-negligible spatial correlation between rural human-land relations in Luoyang region. The initial scale of rural residentials, arable land area, rural resident population and industrial output value are positively correlated with the coordination of people-land relationship, and the slope, elevation and distance from the central city have an inverse effect on the harmony of the relationship between people and land.

Currently, convolutional neural network (CNN) and transformer-based hyperspectral image (HSI) classification methods have attracted significant attention owing to their effective feature representation capabilities. However, methods based on CNN pay insufficient attention to valuable pixels in 3D HSI samples and cannot adapt to variations in these samples. Transformer-based methods also suffer from high computational complexity and a tendency for low-level spatial-spectral features of the shallow attention layer to vanish as the number of attention layers increases. To address these issues, we proposed a local–global feature extraction network with dynamic 3D convolution and residual attention transformer (LGDRNet). The LGDRNet primarily consists of multiscale 3D conv, dynamic local feature extraction, residual global feature extraction, and feature fusion modules. Specifically, a multiscale 3D conv module is used for low-level multiscale spectral information extraction. Then, the dynamic local feature extraction module utilizes dynamic 3D convolution, which can adapt to different samples. This allows the network to focus on valuable pixels in 3D samples. The residual global feature extraction module utilizes a convolutional projection unit and convolutional multihead self-attention to reduce computational complexity. It employs a residual attention connection to enable the network to effectively transmit and accumulate attention information across consecutive multihead attention layers. This prevents the vanishing of shallow spatial–spectral features. Finally, local and global HSI information may be efficiently integrated using the feature fusion module, which also improves performance during subsequent classification. The proposed model achieves overall classification accuracies of 89.24%, 92.01%, and 94.53% on three benchmark datasets, respectively, outperforming state-of-the-art approaches with limited training samples

Convolutional neural networks (CNNs) and graph convolutional networks (GCNs) have led to promising advancements in hyperspectral image (HSI) classification; however, traditional CNNs with fixed square convolution kernels are insufficiently flexible to handle irregular structures. Similarly, GCNs that employ superpixel nodes instead of pixel nodes may overlook pixel-level features; both networks tend to extract features locally and cause loss of multilayer contextual semantic information during feature extraction due to the fixed kernel. To leverage the strengths of CNNs and GCNs, we propose a multiscale pixel-level and superpixel-level (MPAS)-based HSI classification method. The network consists of two sub-networks for extracting multi-level information of HSIs: a multi-scale hybrid spectral–spatial attention convolution branch (HSSAC) and a parallel multi-hop graph convolution branch (MGCN). HSSAC comprehensively captures pixel-level features with different kernel sizes through parallel multi-scale convolution and cross-path fusion to reduce the semantic information loss caused by fixed convolution kernels during feature extraction and learns adjustable weights from the adaptive spectral–spatial attention module (SSAM) to capture pixel-level feature correlations with less computation. MGCN can systematically aggregate multi-hop contextual information to better model HSIs’ spatial background structure using the relationship between parallel multi-hop graph transformation nodes. The proposed MPAS effectively captures multi-layer contextual semantic features by leveraging pixel-level and superpixel-level spectral–spatial information, which improves the performance of the HSI classification task while ensuring computational efficiency. Extensive evaluation experiments on three real-world HSI datasets demonstrate that MPAS outperforms other state-of-the-art networks, demonstrating its superior feature learning capabilities.