The fusion of hyperspectral imagery (HSI) and light detection and ranging (LiDAR) data for classification has received widespread attention and has led to significant progress in research and remote sensing applications. However, existing common CNN architectures suffer from the significant drawback of not being able to model remote sensing images globally, while transformer architectures are not able to capture local features effectively. To address these bottlenecks, this paper proposes a classification framework for multisource remote sensing image fusion. First, a spatial and spectral feature projection network is constructed based on parallel feature extraction by combining HSI and LiDAR data, which is conducive to extracting joint spatial, spectral, and elevation features from different source data. Furthermore, in order to construct local–global nonlinear feature mapping more flexibly, a network architecture coupling together multiscale convolution and a multiscale vision transformer is proposed. Moreover, a plug-and-play nonlocal feature token aggregation module is designed to adaptively adjust the domain offsets between different features, while a class token is employed to reduce the complexity of high-dimensional feature fusion. On three open-source remote sensing datasets, the performance of the proposed multisource fusion classification framework improves about 1% to 3% over other state-of-the-art algorithms.

In recent years, the deep learning method based on fully convolution networks (FCNs) has proven to be an effective method for the semantic segmentation of remote sensing images (RSIs). However, the rich information and complex content of RSIs make networks training for segmentation more challenging. Specifically, the observing distance between the space-borne cameras and the ground objects is extraordinarily far, resulting in that some smaller objects only occupy a few pixels in the image. However, due to the rapid degeneration of tiny objects during the training process, most algorithms cannot properly handle these common small objects in RSIs with satisfactory results. In this paper, we propose a novel feature guide network with a context aggregation pyramid (FGN-CAP) for RSIs segmentation to conquer these issues. An innovative edge-guide feature transform module (EGFTM) is designed to take advantage of the edge and body information of objects to strengthen edge contours and the internal consistency in homogeneous regions, which can explicitly enhance the representation of tiny objects and relieve the degradation of small objects. Furthermore, we design a context aggregation pyramid (CAP) pooling strategy to adaptively capture optimal feature characterization that can assemble multi-scale features according to the significance of different contexts. Extensive experiments on three large-scale remote sensing datasets demonstrate that our method can not only outperform the state-of-the-art methods for objects of different scales, but also achieve robust segmentation results, especially for tiny objects.