Tree species identification is a critical component of forest resource monitoring, and timely and accurate acquisition of tree species information is the basis for sustainable forest management and resource assessment. Airborne hyperspectral images have rich spectral and spatial information and can detect subtle differences among tree species. To fully utilize the advantages of hyperspectral images, we propose a double-branch spatial–spectral joint network based on the SimAM attention mechanism for tree species classification. This method achieved high classification accuracy on three tree species datasets (93.31% OA value obtained in the TEF dataset, 95.7% in the Tiegang Reservoir dataset, and 98.82% in the Xiongan New Area dataset). The network consists of three parts: spectral branch, spatial branch, and feature fusion, and both branches make full use of the spatial–spectral information of pixels to avoid the loss of information. In addition, the SimAM attention mechanism is added to the feature fusion part of the network to refine the features to extract more critical features for high-precision tree species classification. To validate the robustness of the proposed method, we compared this method with other advanced classification methods through a series of experiments. The results show that: (1) Compared with traditional machine learning methods (SVM, RF) and other state-of-the-art deep learning methods, the proposed method achieved the highest classification accuracy in all three tree datasets. (2) Combining spatial and spectral information and incorporating the SimAM attention mechanism into the network can improve the classification accuracy of tree species, and the classification performance of the double-branch network is better than that of the single-branch network. (3) The proposed method obtains the highest accuracy under different training sample proportions, and does not change significantly with different training sample proportions, which are stable. This study demonstrates that high-precision tree species classification can be achieved using airborne hyperspectral images and the methods proposed in this study, which have great potential in investigating and monitoring forest resources.

With the continuous development of surface observation methods and technologies, we can acquire multiple sources of data more effectively in the same geographic area. The quality and availability of these data have also significantly improved. Consequently, how to better utilize multi-source data to represent ground information has become an important research question in the field of geoscience. In this paper, a novel model called multi-modal transformer cascaded fusion net (MMTCFN) is proposed for fusion and classification of multi-modal remote sensing data, Hyperspectral Imagery (HSI) and LiDAR data. Feature fusion and feature extraction are the two stages of the model. First, in the feature extraction stage, a three-branch cascaded Convolutional Neural Network (CNN) framework is employed to fully leverage the advantages of convolutional operators in extracting shallow-level local features. Based on this, we generated multi-modal long-range integrated deep features utilizing the transformer-based vectorized pixel group transformer (VPGT) module during the feature fusion stage. In the VPGT block, we designed a vectorized pixel group embedding that preserves the global features extracted from the three branches in a non-overlapping multi-space manner. Moreover, we introduce the DropKey mechanism into the multi-head self-attention (MHSA) to alleviate overfitting caused by insufficient training samples. Finally, we employ a probabilistic decision fusion strategy to integrate multiple class estimations, assigning a specific category to each pixel. This model was experimented on three HSI-LiDAR datasets with balanced and unbalanced training samples. The proposed model outperforms the other seven SOTA approaches in terms of OA performance, proving the superiority of MMTCFN for the HSI-LiDAR classification task.

Deep learning is increasingly used for the classification of hyperspectral images (HSI) thanks to its ability to completely utilize the rich characteristics of this type of imagery.However, at present, most classification models proposed for processing HSI data are based on standard convolution neural networks, which prefer to learn local information rather than global information, so that it is difficult to achieve ideal accuracy in the case of insufficient training samples in real applications. In this paper, we propose a novel expansion spectral-spatial attention network (ESSAN) for HSI data classification in cases of insufficient training samples. First, a dual- branch network based on expansion convolution is employed as the model backbone to extract spectral and spatial information. All feature maps produced during the dual-branch process are superimposed to combine deep and shallow features by the ResNet concept. With the design philosophy of the superposition of expansion convolutional layers, the network can increase the receptive field to gather more global contextual information. Second, the model also includes a coordinate attention block, which directs the network to weight features according to their significance and suppress those that are irrelevant. Finally, the method was tested on the four data sets from Matiwan Village, Pavia Centre, Pavia University, and Shenzhen University, utilizing 1%, 1%, 5%, and 0.2% training samples respectively. The results showed overall accuracies, in order, 97.96%, 99.12%, 98.73%, and 99.36%. The preliminary results demonstrate the higher efficacy and accuracy of the proposed ESSAN in HSI data classification than the other State-of-the-art.

In recent years, deep learning-based classification methods for hyperspectral images (HSIs) have gained widespread popularity in fields such as agriculture, environmental monitoring, and geological exploration. This is owing to their ability to automatically extract features and deliver outstanding performance. This study provides a new Dilated Spectral–Spatial Gaussian Transformer Net (DSSGT) model. The DSSGT model incorporates dilated convolutions as shallow feature extraction units, which allows for an expanded receptive field while maintaining computational efficiency. We integrated transformer architecture to effectively capture feature relationships and generate deep fusion features, thereby enhancing classification accuracy. We used consecutive dilated convolutional layers to extract joint low-level spectral–spatial features. We then introduced Gaussian Weighted Pixel Embedding blocks, which leverage Gaussian weight matrices to transform the joint features into pixel-level vectors. By combining the features of each pixel with its neighbouring pixels, we obtained pixel-level representations that are more expressive and context-aware. The transformed vector matrix was fed into the transformer encoder module, enabling the capture of global dependencies within the input data and generating higher-level fusion features with improved expressiveness and discriminability. We evaluated the proposed DSSGT model using five hyperspectral image datasets through comparative experiments. Our results demonstrate the superior performance of our approach compared to those of current state-of-the-art methods, providing compelling evidence of the DSSGT model’s effectiveness.