Digital elevation model (DEM) of difference (DoD) produced by unmanned aerial vehicle (UAV)-borne laser scanning (ULS) data has been one of the important methods for monitoring catchment-scale landscape change processes, while its accuracy has been limited by the lack of understanding for the spatially variable uncertainties from systematic errors and random errors included in the DoD. In this study, the DoD uncertainty derivation (DUD) method was improved by undertaking an exhaustive error analysis, estimation of residual systematic errors, and incorporating different DoD uncertainty elimination strategies, based on multitemporal ULS data acquired from a small catchment of the Chinese Loess Plateau. The adapted method was employed to estimate the soil erosion and deposition of the catchment, while the reliability of the method was verified by the volume of mass movement and the depths of gullies measured through field surveys. Results showed that mean systematic biases were 0.025, 0.008, -0.074, and 0.051 m for multitemporal point clouds, respectively. After coregistration, the corresponding systematic bias were 0.001, 0.008, -0.016, and -0.021 m, respectively. The change results showed a significant relationship with the results of mass movement and gully depths ($R^{2}~\gt 0.8$,$p~\lt 0.01$). The adapted DUD method was able to capture different erosion processes, including gully headcut retreat, gully development, mass movement, and localized deposition, while it also achieved an underestimation of the changes compared to field survey results. In the catchment, the area of human activity contributed the highest percentage of the volumetric changes, followed by the gully slope and gully bottom, and the hillslope normally contributed the lowest. Overall, the adapted DUD method provided a reliable way for estimating geomorphic changes at the catchment scale.

High-resolution topographic data acquired by remote aerial vehicles (RAVs) have facilitated the use of digital elevation model (DEM) and DEM of difference (DoD) methods for studying geomorphic processes in complex terrain. However, insufficient understanding of systematic bias and random errors for DEMs constrained the application. In this study, we comprehensively analyzed the spatial pattern and magnitude of errors (including systematic and random errors) of DEMs derived from RAV-acquired point clouds for a topographically complex area (a subcatchment of Qiaogou in the hilly and gully loess plateau (SC_QG), China). The relationships between random errors and influential factors associated with topography, point cloud density, vegetation, and interpolation algorithms were also evaluated. On this basis, an error source thresholding (EST) method was adapted through incorporating residual systematic errors and including more impacting factors in the fuzzy inference system for random error estimation. The adapted EST (AEST) method was then employed to quantify the DoD uncertainty and geomorphic changes in two small catchments with complex terrain (i.e., SC_QG and a sub-catchment of Telagou (SC_TLG) in the hilly and gully Loess Plateau, China), while the results were verified by the changes measured by terrestrial laser scanning (TLS) and erosion pins, respectively. Results showed that mean value of systematic errors of DEMs were 0.065 and 0.005 m for SC_QG and SC_TLG, while the residual errors were reduced to 0.002 and 0.001 m after co-registration, respectively. Significant statistical relationships (${p} \lt 0.01$) were found between random errors and influential factors. The erosional volume of two study sites detected by the adapted method were −252.29 and −981.07 m3 and the corresponding depositional volume were 30.57 and 1594.32 m3, respectively. The adapted method achieved a comparable pattern and magnitude of volumetric changes with TLS results, which was superior to the original EST method in SC_QG. Besides, our method showed a lower absolute error (0.034 m) compared to the original method (0.087 m) through a comparison with erosion pins measurement in the SC_TLG. Overall, the AEST method provided a reliable tool for geomorphic change detection in areas associated with complex terrain.

Accurate monitoring of ground subsidence caused by underground coal mining is crucial for environmental protection. This article proposes a correction method for coal mining subsidence, termed subsidence correction based on horizontal displacement (SCHD), utilizing unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) technology. This method leverages the strengths of image subpixel correlation technology in obtaining high-precision horizontal displacement information while addressing the limitations of the traditional digital elevation model (DEM) differential-difference of difference (DOD) method, which often yields incomplete and inaccurate deformation data. Through simulation experiments and field verifications, we quantitatively analyze the effects of surface slope, the angle between the aspect and the horizontal displacement vector, and the magnitude of horizontal displacement on subsidence modeling accuracy. We validate the reliability of the multiimages correspondances par méthodes automatiques de corrélation (MicMac) image subpixel correlation technique for extracting horizontal displacement information from the ground surface before and after coal mining, and we correct the subsidence errors introduced by neglecting horizontal displacement using the proposed SCHD method. The results indicate that overlooking horizontal displacement can lead to significant overestimations or underestimations of vertical deformation, with error magnitude closely related to the aforementioned topographic parameters and displacement characteristics. The high-precision horizontal displacement information obtained through the image subpixel correlation technique meets subsidence monitoring requirements. The SCHD method effectively reduces subsidence modeling errors and improves monitoring accuracy by 15.6%–40%, achieving centimeter-level precision. This study underscores the significant impact of horizontal displacement on monitoring coal mining subsidence, demonstrating that the SCHD method enhances the accuracy of such monitoring and provides a robust technical approach for extracting detailed 3-D deformation information from mining area surfaces.

Hyperspectral unmixing can handle the mixed pixels in hyperspectral images (HSIs). Shadows of objects in observed areas are recorded by sensors, resulting in an HSI contaminated by shadows. Therefore, shadow pollution is a grievous obstacle for unmixing applications. Although shadow pollution occurs frequently in HSIs, previous unmixing studies have never considered the interference caused by shadows. Hence, mitigating shadow interference for unmixing will be significant for further acquiring subpixel information. In this letter, we employ a generative adversarial autoencoder (GAA) to develop a supervised unmixing method that can substantially reduce the impacts of shadow for unmixing. Specifically, we adopt the GAA to establish an anti-shadow unmixing network (GAA-AS), where the encoder block is used to feature reinforcement, and the decoder serves for abundance estimation. Moreover, we adopt a spectral-aware loss (SAL) as the loss function of adversarial training, which makes the discriminator better capture the difference between pixels. Finally, a softmax layer is adopted for the abundance sum-to-one constraint (ASC). Several experiments verify the effectiveness and advantages of our GAA-AS. In the experiment with shadow-polluted data, the proposed GAA-AS improves accuracies by approximately 70% compared to SOTA approaches in the quantitative experiment with synthetic data, and the impacts of shadow pollution are also significantly alleviated in the experiment with real shadow-polluted HSIs. Additionally, note that the proposed GAA-AS is competitive even when no shadow exists in HSIs, verified by the experiment with shadowless data.

In recent years, deep learning (DL) has accelerated the development of hyperspectral image (HSI) processing, expanding the range of applications further. As a typical model of unsupervised DL, the autoencoder framework has been extensively applied for spectral unmixing due to its strong representation ability and scalability. Nowadays, most DL-based unmixing approaches adopt the linear mixture model (LMM) to estimate pure spectral signatures (endmembers) and their corresponding abundance fractions. However, since sunlight scattering is an inevitable physical phenomenon, the spectral mixture problem is inherently nonlinear. Moreover, most existing nonlinear unmixing approaches focus exclusively on spectral information, neglecting the spatial distribution of materials and the intrinsic correlation between pixels, making it challenging to explore latent features. To address these issues, this article develops a new deep autoencoder-based augmented network (DAAN). The proposed DAAN employs the multilinear mixture model (MLMM) to handle the nonlinear influence caused by multiple scattering. Meanwhile, the proposed DAAN constraints homogenous smoothing in the autoencoder architecture, enabling the aggregation of intrinsic correlations by means of spatial relationships to enhance the performance of abundance estimation. We achieve unsupervised nonlinear hyperspectral unmixing by combining spectral and spatial information. The effectiveness and advantages of DAAN are confirmed by several experiments with synthetic and real HSI datasets. The results indicate that the proposed method outperforms other DL-based unmixing approaches. The source codes of the proposed DAAN will be provided in the following linkhttps://github.com/yuanchaosu/TGRS-daan.

Deep learning (DL) has brought many new trends for hyperspectral image classification (HIC). Graph neural networks (GNNs) are models that fuse DL and structured data. Although GNN-based methods have focused on modeling relations, most of them are susceptible to noise, being adverse to capturing hidden correlations from data. Moreover, existing related approaches typically adopt changeless graph structures, which might lead to poor generalization. To solve the problems mentioned above, this paper develops an adaptive cuckoo refinement-based graph transfer network (ACGT-Net) that introduces a meta-heuristic optimization strategy to refine the graph structure. Specifically, we first pre-train a graph convolutional network (GCN) to learn transferable weight parameters. In the undirected graph, nodes are associated with pixels, and edges correspond to similarities between nodes. Afterward, we integrate a cuckoo search strategy (CSS) into the trained GCN to adaptively refine the graph structure. The graph structure refinement (GSR) with the CSS can pay more attention to significant channels by global optimization to improve the generalization of the GNN. Several experiments with real datasets verify the effectiveness and competitiveness of our ACGT-Net compared with other state-of-the-art (SOTA) methods.

This work proposes a new hyperspectral image classification method based on chaotic cuckoos (CC) search with graph convolution network (CC-GCN). Although GCNs can extract inherent features by node embeddings, most models neglect fragmented relations in the spectral domain. The proposed CC-GCN can refine the graph structure and reduce the redundancy information of spectral dimension, further improving the classification accuracy of hyperspectral images. The experiment results demonstrate the effectiveness of the proposed CC-GCN.

Dimensionality reduction (DR) is a common preprocessing technology for hyperspectral images (HSIs). Recently, many neural networks can implement DR to remove the re-dundant information by node embedding. However, numer-ous hidden-layer parameters limit the generalization ability of the node embedding. In this paper, we develop a graph-cut-based node embedding (GCNE) that can be used for DR of HSIs. The embedding can refine correlations by a graph-cut strategy, and it can avoid numerous parameters when using graph models. Moreover, we combine the graph-cut strategy and extreme learning machine (ELM) to achieve HSI classi-fication. The effectiveness of the proposed method is verified by using real HSIs. Compared with other state-of-the-art DR and classification methods, the proposed approach demon-strates very competitive performance.

Node embedding (NE) is conducive to aggregating correlations and relieving the influence of the Hughes phenomenon when processing high-dimensional data. Although some graph neural networks can capture correlations during achieving NE, the application of NE still faces two rigorous challenges: numerous model parameters and poor generalization. In this letter, we propose a new approach for hyperspectral image (HSI) classification, called the graph-cut-based collaborative NEs (GCCNE). Specifically, we develop a graph-cut-based NE (GCNE) to achieve low-dimensional feature representation, which avoids numerous model parameters when using a graph structure. Considering that the graph-cut in a low-dimensional space does not need to set anchors to decrease the calculation amount, we adopt an ensemble framework based on random subspaces (RSs) to implement the GCNE to obtain the collaborative feature sets, enhancing the generalization of feature representation. Afterward, the collaborative feature sets are input in several kernel-based extreme learning machines (KELMs), respectively, classifying pixels. The number of RSs is the same as the number of KELMs. Finally, we acquire an ensemble result associated with each class. The effectiveness and competitiveness of the proposed method are evaluated by using real HSI datasets.