To address misclassification and reduced accuracy in semantic change detection caused by class imbalance and variable object sizes, this paper improves BGSNet and proposes a new change detection network, SRDFNet (Semantic Refinement and Differential Features). Based on BGSNet’s framework, it introduces three complementary modules: (1) a hierarchical graph module (HGM) that converts multi-scale feature maps into compact semantic graph nodes, using graph attention for intra-layer and cross-level semantic interaction to enhance topological relationship perception; the HGM mitigates the effects of class imbalance by compacting multi-scale features into semantic nodes; (2) a difference enhancement (DE) module that extracts multi-receptive-field difference information from bi-temporal concatenated features via multi-scale parallel convolution branches; (3) a semantic refine (SR) module that performs lightweight residual refinement on bi-temporal semantic features to improve the segmentation accuracy. The DE and SR modules mitigate the degradation in semantic segmentation accuracy caused by variable object sizes. It is trained and tested with BGSNet and three other models on the SECOND and HRSCD datasets. For the SECOND dataset, in terms of five quantitative indicators, namely OA, mIoU, SeK, F1 and recall, SRDFNet achieves 87.64%, 70.31%, 20.36%, 60.25% and 65.27%, respectively. Compared with BGSNet, it gains performance increases of 1.34%, 0.73%, 1.44%, 0.81% and 2.72%, respectively. For the HRSCD dataset, SRDFNet achieves 98.13% (OA), 52.67% (mIoU), 73.77% (SeK), 88.86% (F1) and 88.18% (recall), ranking first among the four methods. Compared with BGSNet, it gains performance increases of 3.96%, 3.93%, 9.69%, 2.33% and 4.00%, respectively.

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.

Monocular depth estimation plays a crucial role in applications such as autonomous driving and mobile 3D reconstruction. However, existing lightweight methods are often constrained by limited computational resources and rely on shallow feature representations for direct depth regression. As a result, cross-scale residual information is insufficiently modeled, which limits their ability to preserve structural consistency and recover fine-grained details in complex scenes. To address these challenges, we propose LapCR-Net, a lightweight monocular depth estimation network based on Laplacian residual reconstruction. Specifically, we formulate a progressive Laplacian residual framework that decomposes depth prediction into a coarse-to-fine multi-scale refinement process. To enhance feature representation in the decoder, we introduce a Structure-aware Feature Recalibration (SFR) module and a Depth-guided Convolution Module (DCM), which strengthen spatial semantic correlations and improve residual prediction across scales. Furthermore, we design an uncertainty-driven collaborative refinement strategy to adaptively adjust residual correction strength. By estimating prediction uncertainty, the proposed strategy sharpens object boundaries while suppressing texture artifacts. Extensive experiments on the NYU-Depth V2 and KITTI benchmarks demonstrate that LapCR-Net achieves competitive performance with only 5.4 M parameters. In particular, it shows clear advantages in structural preservation and detail reconstruction, achieving a favorable trade-off between accuracy and computational efficiency.

Retrieving non-optically active water quality variables, such as total hardness (TH) and permanganate index (CODMn), from hyperspectral data remains challenging because these parameters are not directly linked to spectral reflectance. To improve their estimation from UAV hyperspectral imagery, a GA-MHSA-BPNN framework was developed by combining a genetic algorithm (GA), multi-head self-attention (MHSA), and a backpropagation neural network (BPNN). In this framework, MHSA was introduced to strengthen the representation of informative spectral features, while GA was applied to optimize the initial network parameters and thus enhance convergence stability. The proposed framework was evaluated against BPNN, GA-BPNN, MHSA-BPNN, and 1D-CNN models. Among the tested approaches, GA-MHSA-BPNN produced the most favorable results for both TH and CODMn, with R2 values of 0.878 and 0.843, respectively. Additional experiments using different proportions of training samples showed that the model remained relatively stable when the training data were reduced to 70% and 50% of the original dataset. These results indicate that integrating GA and MHSA into a UAV hyperspectral retrieval framework can improve the estimation of non-optically active water quality variables and provide useful methodological support for efficient and refined monitoring of drinking water source areas.

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.

Fine-grained object recognition represents a practical requirement for intelligent interpretation of high-resolution remote sensing imagery. Existing research primarily concentrates on the detection of stumpy targets. Nevertheless, slender objects characterized by a height significantly exceeding their length or width are also common in practical applications. Current research is inadequate to tackle the challenges presented by slender targets, and there is an urgent need for effective methodologies to address this issue. To this end, this paper proposes a model, named Generalized Adaptive Rotation Faster R-CNN (GA-RFRCNN). The GA-RFRCNN optimizes feature representation across multiple scales by integrating selective enhancement feature pyramid network (SE-FPN). Besides, it introduces an enhanced rotation region proposal network (ERRPN) to enhance the object localization. Furthermore, a dynamically adjusted training process is used to handle difficult-to-detect samples by introducing the adaptive slide loss (ASLoss). We conduct extensive experiments on the transmission tower custom dataset (TT-OBB) and the HRSC2016 dataset, and the results show that our model achieves significant improvements in recognition accuracy and oriented bounding box detection.

Electric pylons are crucial components of power infrastructure, requiring accurate detection and identification for effective monitoring of transmission lines. This paper proposes an innovative model, the EP-YOLOv8 network, which incorporates new modules: the DSLSK-SPPF and EMS-Head. The DSLSK-SPPF module is designed to capture the surrounding features of electric pylons more effectively, enhancing the model’s adaptability to the complex shapes of these structures. The EMS-Head module enhances the model’s ability to capture fine details of electric pylons while maintaining a lightweight design. The EP-YOLOv8 network optimizes traditional YOLOv8n parameters, demonstrating a significant improvement in electric pylon detection accuracy with an average mAP@0.5 value of 95.5%. The effective detection of electric pylons by the EP-YOLOv8 demonstrates its ability to overcome the inefficiencies inherent in existing optical satellite image-based models, particularly those related to the unique characteristics of electric pylons. This improvement will significantly aid in monitoring the operational status and layout of power infrastructure, providing crucial insights for infrastructure management and maintenance.

The alterations in vegetation cover in the Three North Protection Forest Project area influence its ecological and environmental management. It helps to study soil erosion, environmental change, and ecosystem protection to provide basic data support. Based on the Google Earth Engine cloud platform, this paper selects MODIS 3Q1 data from 2000–2020 and uses the image element dichotomous model to estimate the fractional vegetation cover (FVC) of the Three North Protection Forest Project area, evaluates the stability and temporal and spatial variation of FVC, investigates the coupling relationship between the FVC and temperature and rainfall through evaluation indexes such as the slope of inter-annual variation and partial correlation coefficient, and then analyzes the influence of land use changes on the FVC. The results show that the fractional vegetation cover of the Three North Protection Forest Project area as a whole has increased significantly over the past 20 years, the medium and high vegetation coverage areas have reached 36.4%, the high vegetation FVC has increased from 20.66% in 2000 to 21.59% in 2020, and the vegetation activity is increasing. The vegetation cover is significantly affected by the topographic effect, with the vegetation cover improving relatively well at slopes between 5–13° and elevations ranging from 2000–2500 m. The overall positive correlation between vegetation and temperature and vegetation and precipitation in the study area was 54.08% and 70.04%, respectively, and precipitation was the key factor influencing vegetation growth in the research region. Human activities have a stronger effect on vegetation construction than destruction, and this study contributes to the evaluation of the benefits of the Three North Protection Forest Project and the understanding of human influence on environmental change.

With the launch of high-resolution series satellites and the development of UAV hyperspectral, the available hyperspectral data are further expanded. Hyperspectral unmixing is a crucial task to improve hyperspectral images' fine utilization value. With the rapid development of computer and artificial intelligence technology, deep learning theory has been introduced into the image processing field. The autoencoder network has been taken into hyperspectral unmixing because of its great feature extraction ability. This study improves the autoencoder structure and proposes a deep stack autoencoder network (DSAE) for hyperspectral image unmixing. The network consists of endmember extraction (EDSAE) and abundance estimation (ADSAE). Firstly, the EDSAE network is constructed by adding batch normalization, sparse constraint, " sum-to-one" constraint and deleting the bias term. Then unsupervised training is carried out for endmember extraction. Secondly, the obtained endmember spectral data are enhanced based on the HAPKE and LINEAR models. Finally, the supervised training network ADSAE is constructed based on the original stack autoencoder network, and the activation function of the last layer is set as the Softmax function. The simulated dataset is used as a training set, and the hyperspectral images are used as a test set. Based on the DSAE method proposed in this study, end member extraction and abundance estimation are carried out on three hyperspectral images, including Samson, Jasper Ridge and Urban. The results are compared with those obtained by the traditional methods N-FINDR, VCA, MVC-NMF and other deep learning methods SNSA and EndNet. The experimental results show that theD SAE method has obvious advantages over the other five methods in endmember extraction for the three real hyperspectral data set. It also shows the best abundance estimation results based on the synthetic datasets generated by the HAPKE mixing model. The DSAE method has good stability and robustness, which provides a new idea for the quantitative analysis and utilization of hyperspectral images.

Under the influence of the physicochemical characteristics of the crops and the space-time environmental factors, even the same crop will show some oscillation in the spectrum. Previous studies mostly used arithmetic mean value to reduce the uncertainty caused by spectral oscillation, but the characterization ability of mean value is susceptible to the degree of numerical difference. To solve these problems and to explore the relationship between the accuracy of typical crop identification and the growth period and spectral characteristics, we proposed a spectral construction algorithm based on Spectral Domain Interpolation (SDI). Using SDI and traditional Arithmetic Mean (AM) method, the characteristic spectra of typical crops (winter wheat, oilseed grape) and the main background vegetation (trees, grasses, rice stubble) were constructed at different growth stages (March, April and May). Then, feature parameters were extracted based on the constructed characteristic spectrum. The importance evaluation and linear discriminant analysis of the extracted feature parameters were carried out at last. The optimal identifying growth period and identifying feature parameters of typical crops were obtained through comparative analysis, at the same time, the availability and superiority of SDI were verified. The following conclusions were drawn: (1) SDI has a good resistance to extremes, and can retain the spectral characteristics of crops well, and construct a more characterizing characteristic spectrum. (2) The best identifying growth periods of oilseed grape and winter wheat are early flowering period in March and heading period in April, respectively. The best identifying characteristics of winter wheat and oilseed grape are yellow edge position and red edge amplitude, respectively. (3) Winter wheat and oilseed grape can be well identified by using the position of the yellow edge in March and the blue edge area and the red edge amplitude in April.

航空热红外高光谱遥感作为近年新发展起来的对地观测技术,同时具有高空间分辨率和高光谱分辨率的技术特点,具备从空间大尺度上精细探测和分析地表造岩矿物成分的能力.基于航空热红外高光谱TASI数据,在分析常见矿物发射波谱特征的基础上,开展了柳园研究区岩性识别和分类研究,充分发掘TASI数据图谱合一特点,图像分类,光谱定性,从每种岩体、地层岩性单元的光谱角度对所属岩性进行判定.结合研究区花岗岩地质背景,系统分析标型矿物SiO2受岩石共生矿物影响的光谱规律变化特性,进行岩性识别和评价试验.结果表明:航空热红外高光谱可全面提高遥感岩矿识别的种类和精度,为大区域的矿物识别和岩性分类提供了一种新的快速有效的手段.

Monitoring the lake waterbody area in the Qinghai–Tibet Plateau (QTP) is significant in dealing with global climate change. The latest released Landsat-9 data, which has higher radiation resolution and can be complemented with other Landsat data to improve imaging temporal resolution, have great potential for applications in lake area extraction. However, no study is published on identifying waterbodies and lakes in large-scale plateau scenes based on Landsat-9 data. Therefore, we relied on the Google Earth Engine (GEE) platform and selected ten waterbody extraction algorithms to evaluate the quantitative evaluation of waterbody and lake area extraction results on the QTP and explore the usability of Landsat-9 images in the relationship between the extraction accuracy and the algorithm. The results show that the random forest (RF) algorithm performs best in all models. The overall accuracy of waterbody extraction is 95.84%, and the average lake waterbody area extraction error is 1.505%. Among the traditional threshold segmentation waterbody extraction algorithms, the overall accuracy of the NDWI waterbody extraction method is 89.89%, and the average error of lake waterbody area extraction is 3.501%, which is the highest performance model in this kind of algorithm. The linear regression coefficients of NDVI and reflectance of Landsat-8 and Landsat-9 data are close to 1, and R2 is more significant than 0.91. At the same time, the overall accuracy difference of water extraction between the two data is not better than 1.1%. This study proves that Landsat-9 and Landsat-8 data have great consistency, which can be used for collaborative analysis to identify plateau waterbodies more efficiently. With the development of cloud computing technologies, such as Gee, more complex models, such as RF, can be selected to improve the extraction accuracy of the waterbody and lake area in large-scale research.

Automatic building extraction from high-resolution aerial and satellite images has many practical applications, such as urban planning and disaster management. However, the complex appearance and various scales of buildings in remote-sensing images bring a challenge for building extraction. In this study, we developed a novel multiscale building extraction method based on refined attention pyramid networks (RAPNets). We built an encoder–decoder structure, and combine atrous convolution, deformable convolution, attention mechanism, and pyramid pooling module to improve the performance of feature extraction in the encoding path. Moreover, the salient multiscale features were extracted by embedding the convolutional block attention module into the lateral connections. Finally, the refined feature pyramid structure was adopted in the decoding path to fuse the multiscale features to obtain the final extraction results. Experiments on two standard data sets (Inria aerial image labeling data set and xBD data set) show that our method achieves reliable results and outperforms the comparing methods.

航空高光谱遥感数据具有"空谱合一"的优势,深入挖掘并应用高光谱数据中蕴含的蚀变矿物地质信息具有重要意义.本文应用航空高光谱CASI-SASI数据,建立了基于"整体谱形-特征参量协同"的蚀变矿物信息提取流程,并在甘肃省敦煌市小金窝子地区开展了蚀变矿物信息提取、地质涵义分析和野外查证.基于高光谱CASI-SASI数据成功提取了褐铁矿、绿泥石、绿帘石、短波云母、中短波云母、中长波云母、长波云母共7种蚀变矿物.高光谱CASI-SASI数据提取的大面积区域性分布的蚀变矿物多为区域热液作用产物,小范围定向分布的蚀变矿物与断裂、岩脉等成矿热液活动关系密切,局部产出且具定向性的中短波云母与断裂构造及酸性脉体关系密切,基于野外地质调查和ASD光谱实测的蚀变矿物野外查证工作证实蚀变矿物提取结果可信.褐铁矿+中短波云母的蚀变矿物组合具有金矿化指示意义,为基础地质调查和矿产资源勘查提供了重要的遥感信息.

高光谱遥感能够根据光谱指纹特征提取蚀变矿物,在地质领域应用成效十分显著.针对最新的资源一号02D卫星高光谱数据特点,首先进行了高光谱数据预处理,选取蚀变矿物光谱特征显著波段进行数据降维,并应用三次样条插值函数对短波红外数据进行光谱波段增值,将光谱采样间隔提高到了2 nm.在优选出矿物填图端元光谱后,应用改进的SAM填图算法提取头吊泉-南大滩地区的褐铁矿、绿泥石、方解石、白云石、短波云母、中短波云母、中长波云母共7种蚀变矿物,详细分析高光谱蚀变矿物的分布特征并开展蚀变矿物的野外地质验证.研究区蚀变矿物与地质体岩性关系密切,多集中在特定地质体的局部地段.方解石和白云石蚀变矿物的定向展布指示了碳酸盐类沉积/变质地层的走向.根据资源一号02D高光谱数据提取的褐铁矿和中短波云母的分布情况,在南大滩-西红滩地段新发现1条长约15 km的金矿化蚀变带,东部的西红滩地段经综合查证存在地表金矿化异常.资源一号02D高光谱数据可以提取褐铁矿等多种蚀变矿物,从而为矿产资源勘查和基础地质调查提供重要的遥感信息.

[研究目的]"图谱合一"的GF-5 AHSI国产卫星高光谱数据可以根据光谱精细特征进行蚀变矿物的直接识别,一次过境成像即可获取宽幅大面积高光谱数据,能够为陆域自然资源调查提供重要的数据支撑,本文开展了GF-5高光谱数据蚀变矿物提取、分析与验证研究,以期推动国产卫星高光谱数据在地质领域的深化应用.[研究方法]建立了GF-5高光谱数据预处理和蚀变矿物填图技术流程,在改进传统光谱角匹配算法基础上提出了一种整体谱形-吸收峰位协同的光谱匹配新方法,应用python语言开发了相应的算法模块,在甘肃省花牛山地区开展了矿物填图应用,综合分析了岩性-构造-蚀变多源地学要素,应用野外地质调查和ASD光谱测量方法对蚀变矿物提取结果进行了验证.[研究结果]应用GF-5高光谱数据成功提取了花牛山地区的9种蚀变矿物,分别为褐铁矿、赤铁矿、绿泥石、方解石、白云石、短波云母、中短波云母、中长波云母、长波云母,编制了整景3600 km2的蚀变矿物分布图.该地区定向分布的白云石和方解石矿物反映了碳酸盐成分的沉积/变质地层的地表出露情况,绿泥石矿物揭示了富含角闪石矿物地质体的分布,褐铁矿和赤铁矿矿物反映了与酸性花岗岩有关的热液活动,短波云母主要分布在二长花岗岩体内,中短波云母反映了中酸性岩体与敦煌岩群老地层接触带等特殊构造部位的热液活动,中长波云母与北东向断裂构造关系密切,长波云母暗示存在早石炭世之前的热液活动.[结论]GF-5高光谱数据能够提取褐铁矿等9种蚀变矿物,结果证实蚀变矿物填图结果可信.不同蚀变矿物在地质涵义方面存在较大差异,碳酸盐矿物与沉积/变质地层关系密切,云母矿物更多反映了与中酸性岩体及断裂构造有关的热液活动.本文认为GF-5高光谱数据可有效服务于地质体快速识别和界线修编、新鲜样品采样定位等自然资源领域基础地质调查和矿产地质勘查工作.