Lithologic unit classification is essential for resource surveys and infrastructure planning. Remote sensing provides a rapid and scalable alternative to traditional field surveys but faces challenges in vegetation-covered areas due to limited lithological information. To address this, we propose AMSNet, an attention-based multi-scale a priori knowledge-guided lithologic classification framework, which integrates Multi-Scale Enhanced Cross-Spatial Attention (MSECA) and Wavelet-Enhanced Crisscross Attention (WCCA). MSECA extracts lithologic features by aggregating multi-scale neighborhood information and introducing cross-spatial learning, while WCCA enhances long-range contextual understanding using wavelet transforms to mitigate information loss during key-query generation. Experiments on the Qichun and Tieshan datasets demonstrate that AMSNet achieves an overall accuracy (OA) of 69.03% and 83.77%, a mean intersection over union (mIoU) of 48.41% and 48.18%, and a Macro-F1 score of 63.65% and 60.68%, outperforming baseline models. Ablation studies further confirm the effectiveness of MSECA and WCCA in improving lithologic feature extraction. These findings demonstrate the potential of AMSNet to improve lithologic classification in complex terrains, contributing to advancements in geoscience applications.

In the conventional change detection (CD) pipeline, two manually registered and labeled remote sensing datasets serve as the input of the model for training and prediction. However, in realistic scenarios, data from different periods or sensors could fail to be aligned as a result of various coordinate systems. Geometric distortion caused by coordinate shifting remains a thorny issue for CD algorithms. In this paper, we propose a reusable self-supervised framework for bitemporal geometric distortion in CD tasks. The whole framework is composed of Pretext Representation Pre-training, Bitemporal Image Alignment, and Down-stream Decoder Fine-Tuning. With only single-stage pre-training, the key components of the framework can be reused for assistance in the bitemporal image alignment, while simultaneously enhancing the performance of the CD decoder. Experimental results in 2 large-scale realistic scenarios demonstrate that our proposed method can alleviate the bitemporal geometric distortion in CD tasks.

Hyperspectral image (HSI) captures rich spectral information in more than hundreds of spectral bands, which allows far better discrimination between ground objects compared with the conventional optical images. Therefore, HSIs find a number of applications in Earth observation, precision agriculture and environmental monitoring. However, due to the effect of poor imaging condition, hardware limitation and sensor noise, the acquisition of HSIs is inevitably affected by noise, which hinders accurate interpretation of HSI in real applications. A number of denoising methods have been proposed for HSI, including BM4D [1] , LRMR [2] , LRTV [3] . Recent works such as HSID-CNN [4] , which adopt deep learning based technique, have yielded the advanced performance. However, most of them design neural network architecture to process noisy data in a spatial domain. It is known that HSI has significantly different properties in high-frequency and low-frequency domains. For instance, in low-frequency domain, smoothing regions of HSI are more relevant while the details of HSI and noise are often more salient in the high-frequency domain. Current methods restore clean HSIs in a spatial domain, and thus neglect the properties of HSI in different frequency domains, which lead to limited denoising performance.

The fusion and classification of hyperspectral images (HSIs) and light detection and ranging (LiDAR) data have been extensively studied using deep learning. However, traditional real-valued deep learning methods have limitations in distinguishing internal and external relations and capturing fine spatial characteristics. To break through these limitations, this letter proposes a quaternion convolutional neural network (QCNN) with extended morphological attribute profile (EMAP) quaternion representation (called EQR) for multisource remote sensing (RS) data classification by utilizing quaternion properties. Specifically, we first propose the EQR for each single-source data, which encodes the multi-attribute features in a compact yet comprehensive manner, highlighting the internal relations. Secondly, we embed EQR into QCNN to preserve the internal relations and enable the interaction of multi-attribute features. Then we develop the 3-D quaternion convolution to better exploit the 3-D characteristic of HSI. Finally, we design different attention mechanisms and a two-level fusion strategy for multisource data to learn enhanced features. Experiments on two multisource RS data sets show that the proposed method achieved better performance than other state-of-the-art classification methods.

Convolutional long short-term memory (ConvLSTM) has received much attention for hyperspectral image (HSI) classification due to its ability of modeling long-range correlations, which, however, is vulnerable to too many parameters and insufficient training, limiting its classification accuracy, especially for small samples. Different from it, traditional hand-crafted methods extract the features with basic attributes of HSIs, which can provide the lack of details and interpretability of deep semantic features. However, existing methods fail to incorporate their complementarity for HSI classification. As such, a Pseudo complex-valued (CV) Deformable ConvLSTM Neural Network with mutual Attention learning (APDCLNN) is proposed, providing a new way to realize the collaborative learning of hand-crafted and deep features for HSI classification. First, a 2-D pseudo CV deformable ConvLSTM (PDConvLSTM2D) cell is designed using deformable convolution and complex operations, with which a spatial–spectral PDConvLSTM2D neural network (SSPDCL2DNN) is built to extract scale- and spectral-enhanced deep spatial–spectral features. Then, 3-D Gabor filter is used to extract hand-crafted features, and a mutual attention-based multimodality feature learning and fusion (MAMLF) module is designed to integrate them into deep features for training and optimization of SSPDCL2DNN. Finally, an attention loss subnetwork is designed to refine the classification results. As we know, this is the first attempt to apply the idea of mutual attention learning to fuse hand-crafted and deep features for HSI classification. Extensive experiments on three widely used HSI datasets show the advantages of our model over other deep methods in terms of both quantitative and visual quality.

Deep learning methods have demonstrated excellent performance in hyperspectral image (HSI) classification. However, these methods mainly focus on improving the classification accuracy while ignoring their high complexity. By considering that the data formats of both HSIs and network weights can be represented in the form of tensors, we develop a new lightweight tensorized neural network for HSI classification that takes advantage of low-rank tensor decomposition techniques to reduce complexity. Firstly, inspired by tensor train (TT)-based tensorized convolutional layers, a new tensorized 2D convolutional layer based on chain calculation (with better expression ability) is introduced. Based on this innovation, a new lightweight 2D tensorized neural network (2D-TNN) is designed for HSI classification. Furthermore, to better preserve the intrinsic structure of HSI data, a new lightweight 3D tensorized neural network (3D-TNN) is proposed by extending the tensorized 2D convolutional layers to their 3D versions. Quantitative and comparative experiments on three widely used data sets show that the proposed models are able to achieve state-of-the-art performance (with a low number of model parameters) for different training sample sizes, especially for very small training sets.

Hyperspectral unmixing is a critical processing step for many remote sensing applications. Nonnegative matrix factorization (NMF) has drawn extensive attention in hyperspectral image analysis recently. Considering that the abundance matrix is generally sparse and smooth, we propose a sparsity-constrained NMF with adaptive total variation (SNMF-ATV) algorithm for hyperspectral unmixing. Specifically, the ATV could promote the smoothness of the estimated abundances while avoid the staircase effect caused by TV model. The comparison with other unmixing methods on both synthetic and real data sets demonstrates the effectiveness and superiority of the proposed SNMF-ATV algorithm with regard to the other considered methods.

In this work, we propose a semi-supervised method for classification of polarimetric synthetic aperture radar (PolSAR) images. In the proposed method, a 2-level mixture model is constructed by associating each component density with a unique Wishart mixture model (instead of a single Wishart distribution as that in the conventional Wishart mixture model). This modeling scheme facilitates the accurate description of data for the categories, each of which includes multiple subcategories. The learning algorithm for the proposed model is developed based on variational inference and all the update equations are obtained in closed form. In the learning algorithm, the spatial interdependencies are incorporated by imposing a Markov random field prior on the indicator variable to alleviate the speckle effect on the classification results. The experimental results demonstrate the improved performance of the proposed method compared with the unsupervised version and supervised version of the proposed model as well as an existing method for semi-supervised classification.

Spectral unmixing is an important technique in hyperspectral image applications. Recently, sparse regression has been widely used in hyperspectral unmixing, but its performance is limited by the high mutual coherence of spectral libraries. To address this issue, a new sparse unmixing algorithm, called double reweighted sparse unmixing and total variation (TV), is proposed in this letter. Specifically, the proposed algorithm enhances the sparsity of fractional abundances in both spectral and spatial domains through the use of double weights, where one is used to enhance the sparsity of endmembers in spectral library, and the other is introduced to improve the sparsity of fractional abundances. Moreover, a TV-based regularization is further adopted to explore the spatial-contextual information. As such, the simultaneous utilization of both double reweighted l1minimization and TV regularizer can significantly improve the sparse unmixing performance. Experimental results on both synthetic and real hyperspectral data sets demonstrate the effectiveness of the proposed algorithm both visually and quantitatively.

Spectral unmixing is an important technology in hyperspectral image applications. Recently, sparse regression is widely used in hyperspectral unmixing. This paper proposes a double reweighted sparse regression method for hyperspectral unmixing. The proposed method enhances the sparsity of abundance fraction in both spectral and spatial domains through double weights, in which one is used to enhance the sparsity of endmembers in the spectral library, and the other to improve the sparseness of abundance fraction of every material. Experimental results on both synthetic and real hyperspectral data sets demonstrate effectiveness of the proposed method both visually and quantitatively.

This paper is concerned with semisupervised hyperspectral unmixing using a nonlocal similarity prior on the abundance images. To this end, the nonlocal self-similarity regularization is incorporated into the classical sparse regression formula to propose a new model for hyperspectral sparse unmixing. The rationale is the idea that there are many nonlocal similar patches to the given patch in the abundance images. The effectiveness of the proposed algorithm is illustrated using the synthetic and real data sets.