Multimodal image matching (MMIM) poses persistent challenges in remote sensing due to substantial variations in radiometric properties, scale, and rotation across different modalities. Traditional approaches typically employ pyramid scale space constructions to handle scale differences. However, the processing of multi-scale features often incurs a high computational cost and reduced efficiency. To address this issue, this paper proposes a novel MMIM algorithm based on log-polar sampling (LPS), which achieves both scale and rotation invariance while significantly reducing redundant computations. The proposed method begins with a feature detection strategy based on a single-scale edge thinning map (ETM), which is generated by combining Gaussian steerable filtering with an iterative edge thinning process to extract edge points accurately. To mitigate the impact of radiometric discrepancies between modalities, a quantized orientation map (QOM) is constructed using radiometrically invariant feature orientations. LPS is subsequently performed within neighborhoods of keypoints to generate logpolar patches (LPPs) that are inherently invariant to scale and rotation. These LPPs are then encoded into feature descriptors via orientation distribution histograms. A two-stage matching strategy is further introduced, incorporating keypoint elimination and descriptor reconstruction to enhance robustness. Extensive experiments conducted on six types of typical multimodal images demonstrate that the proposed algorithm consistently outperforms state-of-the-art MMIM methods in terms of both accuracy and computational efficiency. The source code and datasets will be publicly available at https://github.com/liujy325/LPSD.

The joint classification of hyperspectral images (HSI) and LiDAR data is an important research direction in the field of remote sensing image processing. However, existing methods are mostly limited to inadequate feature extraction or simple serial fusion strategies, failing to fully exploit the deep cross-modal relationships between LiDAR and HSI data. Meanwhile, global modeling based on Transformers, despite capturing long-range dependencies, faces difficulties in handling high-dimensional remote sensing data due to quadratic computational complexity, leading to constrained global-local feature collaboration. To address these issues, this paper proposes a novel hierarchical multi-scale Mamba network (HMMamba) for joint classification of HSI and LiDAR data. First, the method adopts a hierarchical design to obtain rich multi-level feature representations through multi-scale feature extraction. Second, it utilizes adaptive weight allocation to achieve dynamic cross-modal feature integration. Finally, it constructs a Fused Feature enhancement Mamba Block (FFMB), which integrates a dual-branch attention mechanism and selective state space modeling to achieve efficient global context modeling under linear computational complexity Extensive experiments on four public datasets (Houston2013, MUUFL, Trento, and Augsburg) demonstrate that HMMamba significantly outperforms existing state-of-the-art methods in terms of classification performance. Specifically, on Augsburg dataset, the OA improvement is 3.24% compared to the suboptimal method, fully validating the superior performance of the proposed method. The codelink of the proposed method is https://github.com/leiyeqi/HMMamba.

When hyperspectral image classification encounters high-dimensional spectral channels, the feature utilization rate is often low due to significant redundancy between channels and uneven discriminatory power. Furthermore, different land cover types exhibit notable differences in scale and morphological changes across space. This structural diversity poses significant challenges to spatial feature modeling. To address this problem, this paper proposes a novel framework based on the Mamba model-PS2Mamba for hyperspectral image classification. This framework integrates three strategies: spectral fine modeling, global perceptual scale adaptation, and multi-scale spatial structure modeling. Firstly, this paper designs a statistically enhanced normalized band refinement (SENBR) module, which dynamically enhances or suppresses channel features based on channel correlation, variability, and importance, effectively suppressing redundant and noisy bands. Secondly, a global-aware scale adaptation (GASA) module is proposed. By incorporating a scale scoring network and a multi-directional modeling mechanism, this module enables adaptive perception and directional enhancement modeling of spatial structures at different scales. Finally, a lightweight multi-scale spatial structure extraction module, LightPyramid, is constructed. By enriching spatial semantic information through multi-branch parallel convolutions, it enhances the model’s ability to represent complex surface structures, while maintaining spatial resolution. Experimental results on four representative hyperspectral datasets, including Pavia University, Salinas, and two UAV-based datasets (HongHu and HanChuan), demonstrate that PS2Mamba achieves overall accuracies of 98.77%, 99.61%, 96.20%, and 96.01%, respectively. Compared with existing CNN, GCN, Transformer, and Mamba based models, PS2Mamba achieves up to 12.91% improvement in accuracy, showing superior generalization and robustness, particularly under small-sample conditions.

With the increasing number of remote sensing (RS) data sources, the joint utilization of multimodal data in Earth observation tasks has become a crucial research topic. As a typical representative of RS data, hyperspectral images (HSIs) provide accurate spectral information, while rich elevation information can be obtained from light detection and ranging (LiDAR) data. However, due to the significant differences in multimodal heterogeneous features, how to efficiently fuse HSI and LiDAR data remains one of the challenges faced by existing research. In addition, the edge contour information of images is not fully considered by existing methods, which can easily lead to performance bottlenecks. Thus, a joint classification network of HSI and LiDAR data based on Mamba (HLMamba) is proposed. Specifically, a gradient joint algorithm (GJA) is first performed on LiDAR data to obtain the edge contour data of the land distribution. Subsequently, a multimodal feature extraction module (MFEM) was proposed to capture the semantic features of HSI, LiDAR, and edge contour data. Then, to efficiently fuse multimodal features, a novel deep learning (DL) framework called Mamba, was introduced, and a multimodal Mamba fusion module (MMFM) was constructed. By efficiently modeling the long-distance dependencies of multimodal sequences, the MMFM can better explore the internal features of multimodal data and the interrelationships between modalities, thereby enhancing fusion performance. Finally, to validate the effectiveness of HLMamba, a series of experiments were conducted on three common HSI and LiDAR datasets. The results indicate that HLMamba has superior classification performance compared to other state-of-the-art DL methods. The source code of the proposed method will be available publicly athttps://github.com/Dilingliao/HLMamba.

In the past, deep learning (DL) technologies have been widely used in hyperspectral image classification tasks. Among them, convolutional neural networks (CNNs) use fixed size receptive field (RF) to obtain spectral and spatial features of hyperspectral images (HSIs), showing great feature extraction capabilities, which are one of the most popular DL frameworks. However, the convolution using local extraction and global parameter sharing mechanism pays more attention to spatial content information, which changes the spectral sequence information in the learned features. In addition, CNN is difficult to describe the long-distance correlation between HSI pixels and bands. To solve these problems, a spectral-spatial fusion Transformer network (S2FTNet) is proposed for the classification of hyperspectral images. Specifically, S2FTNet adopts the Transformer framework to build a spatial Transformer module (SpaFormer) and a spectral Transformer module (SpeFormer) to capture image spatial and spectral long-distance dependencies. In addition, an adaptive spectral-spatial fusion mechanism (AS2FM) is proposed to effectively fuse the obtained advanced high-level semantic features. Finally, a large number of experiments were carried out on four datasets, Indian Pines, Pavia, Salinas and WHU-Hi-LongKou, which verified that the proposed S2FTNet can provide better classification performance than other the state-of-the-art networks.

In recent years, convolutional neural networks (CNNs) have achieved excellent performance in hyperspectral image classification and have been widely used. However, the convolution kernel used in traditional CNN has the limitation of single scale which is not conducive to the improvement of hyperspectral classification performance. In addition, training a classification network of high-dimensional data based on limited labeled samples is still one of the challenges of hyperspectral image classification. To solve the above problems, a hyperspectral image classification method based on expansion convolution network (ECNet) is proposed. The expansion convolution injects holes into the standard convolution kernel to expand the receptive field (RF), so as to extract more context features. Because the shallow features of hyperspectral images contain more location and detail information, while the deep features contain stronger semantic information, in order to further enhance the correlation between deep and shallow information, inspired by ResNet, a similar feedback block (SFB) is introduced on the basis of ECNet, and the deep features and shallow features are fused through this feedback mechanism. Thus, an improved version of ECNet method is obtained, which is called FECNet. This study was tested on four commonly used hyperspectral data sets (i.e. Indian Pine (IP), Pavia University (UP), Kennedy Space Center (KSC), Salinas Valley (SV)) and on a higher resolution and complexly distributed land cover data set (University of Houston (HT)). The experimental results show that the proposed method has better classification performance than some state-of-the art methods, which shows that FECNet has a certain potential in hyperspectral image classification.