Hyperspectral image (HSI) has received considerable attention in the field of target detection due to its powerful ability to capture the spectral information of land covers, and plenty of detection algorithms have been explored. However, these methods generally leverage the difference between the spectrum of the target to be detected and the background spectrum to accomplish target detection, and so are susceptible to the problem of spectral variability. In this article, we propose a global-to-local hierarchical detection algorithm for HSI (G2LHTD). Firstly, extended morphological attribute profile (EMAP) is first used to model global spatial texture information from HSI. Subsequently, a diverse-direction constrained energy minimization (D ² CEM) detector is developed to consider the spatial information within eight neighborhoods around each pixel in HSI, yielding comprehensive local spatial information. More substantially, to effectively discriminate the neighborhood information in diverse directions, we devise an adaptive neighborhood feature aggregation (ANFA) strategy, which will comprehensively evaluate the significance of neighborhood information in diverse directions. As a result, the spatial features of HSI can be comprehensively considered for hyperspectral target detection (HTD). Extensive experiments, conducted on four standard datasets, demonstrate the effectiveness of the proposed method. The codes of this work will be available at https://github.com/zhonghaocheng/G2LHTD_Master for the sake of reproducibility.

With the abundant emergence of remote sensing data sources, multimodal remote sensing observation has become an active field. Extracting valuable information from multi-modal data has the potential to make a significant contribution to applications such as urban planning and monitoring. However, existing studies are deficient in extracting spectral and spatial features from hyperspectral remote sensing data. Meanwhile, the method of fusing multimodal features has limitations and poses a challenge to the convergence of the model loss function, which increases the complexity of the network model optimisation process. Therefore, this paper proposes an Adaptive Multi-scale Spatial–Spectral Enhancement Network for Classification of Hyperspectral and LiDAR Data called AMSSE-Net. First, we perform deep mining of spectral features in hyperspectral images by the involution operator. The main idea is to take full advantage of the involution operator in characterising spectral features by using the property that the convolution kernel shares the feature channels within the group. Furthermore, the multi-branching approach is used to extract the multi-scale information, and then the spectral-spatial features are formed with the strategy of hierarchical fusion. Meanwhile, we employ three-layer convolution for extracting shallow features from LiDAR data, offering supplementary information. Finally, we propose the ”Adaptive Feature Fusion Module,” an innovative and comprehensive mechanism designed for the fusion of features from diverse sources in multi-source data fusion. These dynamically assigned weights guide the selection of the optimal model, which is determined by the joint loss across the three methods, ultimately leading to the generation of an accurate prediction map. This approach not only helps to deeply explore the spectral spatial information in the hyperspectral data, but also effectively fuses the hyperspectral information with the elevation information from the LiDAR data. The expression ability of model features is rapidly improved by adaptive weighting, which in turn enhances the performance and generalisation ability of the model. Compared with some existing methods, extensive experiments on three popular HSI and LiDAR datasets show that our proposed AMSSE-Net can achieve better classification performance. The codes will be available at https://github.com/haofeng0003/AMSSE-Net, contributing to the RS community.

The land cover classification of single-modal remote sensing (RS) data has recently reached a bottleneck. The joint use of multi-modal RS data to improve classification performances has received much attention. Convolutional Neural Networks are powerful tools in feature extraction and contextual modeling. While they have attendant drawbacks to capture the sequence attributes of spectral signatures and struggle to acquire discriminative spectral-spatial features from a global perspective due to limitations inherent in their network backbones. The transformer backbone is a promising approach for addressing these challenges and generating novel insights in multi-modal RS image classification. In this article, we present a new model called Multi-modal Transformer Network (MTNet) that leverages transformer advantages to capture both the specific and shared characteristics of hyperspectral (HS) and light detection and ranging (LiDAR) data. HS images contain a wide range of bands with rich spectral information and LiDAR data provide accurate elevation information without affecting by environmental factors. The well-designed module Hyperspectral Spectral Transformer can learn spectrally local sequence information from neighbouring bands of HS images, yielding group-wise spectral embeddings comprising rich diagnostic information about land covers. Furthermore, the HS and LiDAR spatial transformers aim to mine the pixel-wise feature embedding relationships in a global manner, capturing spatial and elevation information of HS and LiDAR, respectively. Finally, the feature embedding tokens of two modalities are integrated jointly and a new transformer encoder is redesigned to explore the shared spatial characteristics between the two modalities. We evaluate the classification performances of the proposed MTNet on three public HS-LiDAR datasets by conducting extensive experiments, exhibiting superiority over conventional classifiers and state-of-the-art networks.

The detection of airborne small targets amidst cluttered environments poses significant challenges. Factors such as the susceptibility of a single RGB image to interference from the environment in target detection and the difficulty of retaining small target information in detection necessitate the development of a new method to improve the accuracy and robustness of airborne small target detection. This paper proposes a novel approach to achieve this goal by fusing RGB and infrared (IR) images, which is based on the existing fusion strategy with the addition of an attention mechanism. The proposed method employs the YOLO-SA network, which integrates a YOLO model optimized for the downsampling step with an enhanced image set. The fusion strategy employs an early fusion method to retain as much target information as possible for small target detection. To refine the feature extraction process, we introduce the Self-Adaptive Characteristic Aggregation Fusion (SACAF) module, leveraging spatial and channel attention mechanisms synergistically to focus on crucial feature information. Adaptive weighting ensures effective enhancement of valid features while suppressing irrelevant ones. Experimental results indicate 1.8% and 3.5% improvements in mAP over the LRAF-Net model and Infusion-Net detection network, respectively. Additionally, ablation studies validate the efficacy of the proposed algorithm’s network structure.