Dayaoshan, as an important metal ore-producing area in China, is faced with the dilemma of resource depletion due to long-term exploitation. In this paper, remote sensing methods are used to circle the favorable metallogenic areas and find new ore points for Gulong. Firstly, vegetation interference was removed by using mixed pixel decomposition method with hyperplane and genetic algorithm (GA) optimization; then, altered mineral distribution information was extracted based on principal component analysis (PCA) and support vector machine (SVM) methods; thirdly, the favorable areas of gold mining in Gulong was delineated by using the ant colony algorithm (ACA) optimization SVM model to remove false altered minerals; and lastly, field surveys verified that the extracted alteration mineralization information is correct and effective. The results show that the mineral alteration extraction method proposed in this paper has certain guiding significance for metallogenic prediction by remote sensing.

Change detection aims to identify the difference between dual-temporal images and has garnered considerable attention over the past decade. Recently, deep learning methods have shown robust feature extraction capabilities and have achieved improved detection results; however, they exhibit limitations in preserving clear boundaries for the identified regions, which is attributed to the inadequate contextual information aggregation capabilities of feature extraction, and fail to adequately constrain the delineation of boundaries. To address this issue, a novel dual-branch feature interaction backbone network integrating the CNN and Transformer architectures to extract pixel-level change information was developed. With our method, contextual feature aggregation can be achieved by using a cross-layer feature fusion module, and a dual-branch upsampling module is employed to incorporate both spatial and channel information, enhancing the precision of the identified change areas. In addition, a boundary constraint is incorporated, leveraging an MLP module to consolidate fragmented edge information, which increases the boundary constraints within the change areas and minimizes boundary blurring effectively. Quantitative and qualitative experiments were conducted on three benchmarks, including LEVIR-CD, WHU Building, and the xBD natural disaster dataset. The comprehensive results show the superiority of the proposed method compared with previous approaches.

Anaerobic digestion (AD) technology offers significant advantages in addressing environmental issues arising from the intensification of livestock production since it enables waste reduction and energy recovery. However, the molecular composition of dissolved organic matter (DOM) and its linkages to microbial biodiversity during the industrial-scale AD process of chicken manure (CM) remains unclear. In this study, the chemical structure of CM digestate-derived DOM was characterized by using multi-spectroscopic techniques and ultrahigh-resolution mass spectrometry, and the microbial composition was detected by using 16S rRNA gene sequencing. The results revealed that the DOM contained abundant free amino acids and protein-like compounds but fewer humic-like substances, identified as lignin/carboxylate-rich alicyclic molecules, lipids, and proteins/aliphatic compounds featuring enriched S_5–6O₁ and N_1–5O_X fragments. In addition, the 16S rRNA results revealed microorganisms that were centered on metabolic function in the production of volatile fatty acids, H₂S/CH₄, and the hydrolysis reaction in the AD process. Free amino acids and protein-like compounds were mainly associated with hydrolysis reactions and H₂S production functional microorganisms. Lignin/carboxylate-rich alicyclic molecules were linked to microorganisms possessing hydrolysis reactions and, indirectly, CH₄ production. This study elucidates the linkage with the microbial and molecular composition of DOM, establishing a theoretical foundation for employing AD in the disposal of CM.

Hyperspectral image (HSI) clustering has attracted significant attention due to its broad applications in agricultural monitoring, environmental protection, and other fields. However, the integration of high-dimensional spectral and spatial information remains a major challenge, often resulting in unstable clustering and poor generalization under noisy or redundant conditions. To address these challenges, we propose a Joint Spectral–Spatial Representation Learning (JSRL) framework for robust hyperspectral image clustering. We first perform spectral clustering to generate pseudo-labels and guide a residual Graph Attention Network (GAT) that jointly refines pixel-level spectral and spatial features. We then aggregate pixels into superpixels and employ a Variational Graph Autoencoder (VGAE) to learn structure-aware representations, further optimized via a quantum-behaved particle swarm optimization (QPSO) strategy. This hierarchical architecture not only mitigates spectral redundancy and reinforces spatial coherence, but also enables more robust and generalizable clustering across diverse HSI scenarios. Extensive experiments on multiple benchmark HSI datasets demonstrate that JSRL consistently achieves state-of-the-art performance, highlighting its robustness and generalization capability across diverse clustering scenarios.

Hyperspectral images (HSI) contain abundant spectral information, while the spatial resolution is usually limited. To obtain high-spatial-resolution hyperspectral images, various HSI Super-Resolution (SR) methods are proposed. Currently, deep learning based super-resolution reconstruction methods are studied in depth, which take different measures to make full use of the spatial and spectral information of hyperspectral images, and optimize the network with lots of trainings. Although they have achieved satisfying spatial resolution, the spectral consistency before and after reconstruction is difficult to guarantee. In this paper, we proposed a self-attention-based recurrent feedback network for hyperspectral super-resolution reconstruction, utilizing pixel-aware weights and pseudo 3D convolution to enhance the spatial and spectral consistency during the reconstruction process. In addition, group reconstruction is used to reduce the redundancy of information. Spectral consistency regularization is proposed to ensure the spectral consistency before and after reconstruction. The effectiveness of the proposed method is tested on one set of natural image and three hyperspectral remote sensing image datasets