Automatic detection of electric pylons in optical remote sensing imagery is important for large-scale powerline monitoring, but remains challenging due to complex backgrounds, small target appearances, and large variations in pylon-shadow structures. This paper proposes a Refined Deformable-DETR framework with a Spatial Context-aware Query Modulation (SCQM) module to enhance object query representations. SCQM aggregates image-level contextual information from encoder memory and generates channel-wise modulation vectors to recalibrate object queries before deformable cross-attention, thereby providing image-conditioned channel priors for subsequent query-feature interaction. Experiments on the self-constructed Electric Pylon Remote Sensing Dataset (EPRD) show that the proposed method improves AP from 72.7% to 74.1% and APs from 47.2% to 50.9% compared with the baseline Deformable-DETR. Evaluations on the public Electric Pylon Dataset (EPD) further demonstrate its generalization capability. These results indicate that context-aware query modulation is effective for Transformer-based electric pylon detection in complex remote sensing scenarios.

Traditional diagnostic spectroscopy provides a physically interpretable basis for mineral identification. However, how modern classifiers balance spectral and spatial information remains insufficiently understood. This study investigates this issue using CASI airborne hyperspectral data from the Liuyuan area, China. A geologically constrained ground-truth dataset was constructed based on expert knowledge and a semi-automatic Spectral Hourglass workflow. We evaluated representative shallow machine learning methods and deep learning models, including a three-dimensional convolutional neural network (3D-CNN), Vision Transformer (ViT), and SpectralFormer. The Support Vector Machine (SVM) achieved the highest overall accuracy but showed a strong bias toward dominant background classes and failed to reliably detect rare minerals such as jarosite. Deep learning models improved class balance by incorporating broader spectral features. However, excessive spatial aggregation reduced their sensitivity to small and fragmented alteration zones. SpectralFormer models hyperspectral data as ordered spectral sequences and showed more stable performance for spectrally similar and rare minerals. Multi-scale experiments reveal a spectral-dominant discrimination mechanism. Increasing the spectral receptive field improves classification up to an optimal level. In contrast, overly large spatial patches introduce background interference and obscure diagnostic absorption features. These findings highlight the fundamental role of spectral continuity in airborne hyperspectral alteration mineral mapping and clarify the trade-offs involved in integrating spatial context.

Hyperspectral unmixing is an important technique for mineral mapping because natural geological scenes commonly contain mixed pixels composed of multiple spectrally overlapping materials. In mineral environments, these mixtures are often intimate rather than purely areal, and nonlinear scattering effects may weaken the validity of linear mixing assumptions. Although autoencoder-based hyperspectral unmixing methods can jointly estimate endmembers and abundances in an unsupervised manner, they often suffer from insufficient physical constraints, unstable endmember learning, and limited geological interpretability. To address these issues, this study proposes a physically constrained and library-guided convolutional autoencoder for mineral hyperspectral unmixing. The method retains an interpretable linear reconstruction backbone while introducing a Hapke-consistency regularization term to incorporate physically motivated nonlinear scattering behavior during endmember optimization. In addition, a library-aware endmember anchor module is designed to improve initialization quality, reduce endmember drift, and guide optimization toward spectrally meaningful solutions. The proposed method was evaluated on both simulated hyperspectral datasets and real airborne SASI data. On the simulated datasets, the method achieved improved endmember spectral fidelity and lower abundance estimation error than several representative autoencoder-based baselines, with the advantage being more evident under nonlinear mixing conditions. Ablation experiments further showed that the Hapke-consistency term mainly improved physical plausibility, whereas the anchor module enhanced optimization stability and spectral consistency. On the real airborne dataset, the proposed method produced endmember spectra that were more consistent with field and laboratory mineral references and generated spatially more coherent abundance maps. These results indicate that incorporating physically motivated constraints and mineral-library priors into deep autoencoder frameworks can improve the robustness and interpretability of mineral hyperspectral unmixing. The proposed framework provides a practical direction for hyperspectral mineral mapping in mixed and spectrally complex geological environments.