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This article introduces a special issue on hyperspectral remote sensing, which involves advanced satellite sensors that provide data in three categories: hyperspatial, superspectral, and hyperspectral. These data types offer higher resolutions but also present challenges in handling and analysis. The special issue aims to explore the characteristics and limitations of hyperspectral data and their opportunities for advancing remote sensing science. The selected papers in the issue cover various applications and use advanced techniques to provide valuable knowledge in the field. The document discusses three research papers related to remote sensing and hyperspectral data, focusing on mapping building shadows, identifying asphalt pavement aging, and separating wheat crops from similar crops. It emphasizes the potential of hyperspectral data in advancing remote sensing science while acknowledging the challenges associated with data analysis and calibration. [Extracted from the article]

In this paper, we propose a novel approach for the extraction of highquality frames to enhance the fidelity of videogrammetry by combining fuzzy frames removal and baseline constraints. We first implement a gradient-based mutual information method to filter out low-quality frames while preserving the integrity of the videos. After frame pose estimation, the geometric properties of the baseline are constrained by three aspects to extract the keyframes: quality of relative orientation, baseline direction, and base to distance ratio. The three-dimensional model is then reconstructed based on these extracted keyframes. Experimental results demonstrate that our approach maintains a strong robustness throughout the aerial triangulation, leading to high levels of reconstruction precision across diverse video scenarios. Compared to other methods, this paper improves the reconstruction accuracy by more than 0.2 mm while simultaneously maintaining the completeness. [ABSTRACT FROM AUTHOR]

In this paper, wavelets are applied to develop new models for the self-calibration of aerial metric camera systems. It is well known and mathematically proven that additional parameters (APs) can compensate image distortions and remaining error sources by a rigorous photogrammetric bundle-block adjustment. Thus, kernel functions based on orthogonal wavelets (e.g., asymmetric Daubechies wavelets, least asymmetric Daubechies wavelets, Battle-Lemarié wavelets, Meyer wavelets) are used to build the wavelets-based family of APs for self-calibrating digital frame cameras. These new APs are called wavelet APs. Its applications in rigorous tests are accomplished by using aerial images taken by an airborne digital mapping camera in situ and practical calibrations. The test results demonstrate that these orthogonal wavelet APs are applicable and largely avoid the risk of over-parameterization. Their external accuracy is evaluated using reliable and high precision check points in the calibration field. [ABSTRACT FROM AUTHOR]

Star identification algorithms can be applied to resident space object (RSO) surveillance, which includes a large number of stars and false stars. This paper proposes an efficient, robust star identification algorithm for RSO surveillance based on a neural network. First, a feature called equal-frequency binning radial feature (EFB-RF) is proposed for guide stars, and a superficial neural network is constructed for feature classification. Then the training set is generated based on EFB-RF. Finally, the remaining stars are identified using a residual star matching method. The simulation experiment and results show that the identification rate of our algorithm can reach 99.82% under 1 pixel position noise, and it can reach 99.54% under 5% false stars. When the percentage of missing stars is 15%, it can reach 99.40%. The algorithm is verified by RSO surveillance. [ABSTRACT FROM AUTHOR]

The aging condition of asphalt pavement is an invaluable basis for traffic infrastructure evaluation. Due to the amount of time and high cost of monitoring and identifying asphalt pavement aging, many current studies focus on satellite remote sensing methods. In this paper, some methods and technologies for monitoring asphalt pavement degradation by optical satellite remote sensing are introduced as a literature review. Many researchers have developed spectrum libraries based on the actual aging of asphalt pavements, and it is possible to construct pavement health indices based on spectrum changes. Some indexes can extract different aging degrees of asphalt pavement from optical satellite images. Of course, current research can only preliminarily reflect the aging phenomenon of asphalt pavement and cannot accurately describe the distress characteristics of asphalt pavement. Future research needs to further strengthen mechanism research, develop higher resolution images, improve image processing technology, and adopt multi-means fusion analysis methods. [ABSTRACT FROM AUTHOR]

The power grid plays a vital role in the construction of livelihood projects by transmitting electrical energy. In the event of insulator explosions on power grid towers, these insulators may detach, presenting potential safety risks to transmission lines. The identification of such failures relies on the examination of images captured by unmanned aerial vehicles (UAVs). However, accurately detecting insulator defects remains challenging, particularly when dealing with variations in size. Existing methods exhibit limited accuracy in detecting small objects. In this paper, we propose a novel detection method that incorporates the convolutional block attention module (CBAM) as an attention mechanism into the backbone of the "you only look once" version 5 (YOLOv5) model. Additionally, we integrate a residual structure into the model to learn additional information and features related to insulators, thereby enhancing detection efficiency. Experimental results demonstrate that our proposed method achieved F1 scores of 0.87 for insulator detection and 0.89 for insulator defect detection. The improved YOLOv5 network shows promise in detecting insulators and their defects in UAV images. [ABSTRACT FROM AUTHOR]

Remote sensing image segmentation is a crucial technique in the field of land management. However, existing semantic segmentation networks require a large number of floating-point operations (FLOPs) and have long run times. In this paper, we propose a dual-path feature aggregation network (DPFANet) specifically designed for the low-latency operations required in land management applications. Firstly, we use four sets of spatially separable convolutions with varying dilation rates to extract spatial features. Additionally, we use an improved version of MobileNetV2 to extract semantic features. Furthermore, we use an asymmetric multi-scale fusion module and dual-path feature aggregation module to enhance feature extraction and fusion. Finally, a decoder is constructed to enable progressive up-sampling. Experimental results on the Potsdam data set and the Gaofen image data set (GID) demonstrate that DPFANet achieves overall accuracy of 92.2% and 89.3%, respectively. The FLOPs are 6.72 giga and the number of parameters is 2.067 million. [ABSTRACT FROM AUTHOR]

Debris flow susceptibility evaluation plays a crucial role in the prevention and control of debris flow disasters. Therefore, this paper proposes a convolutional neural network model named multi-level feature extraction network (MFENet). First, a dual-channel CNN architecture incorporating the Embedding Channel Attention mechanism is used to extract shallow features from both digital elevation model images and multispectral images. Subsequently, channel shuffle and feature concatenation are applied to the features from the two channels to obtain fused feature sets. Following this, a deep feature extraction is performed on the fused feature sets using a residual module improved by maximum pooling. Finally, the susceptibility index of gullies to debris flows is calculated based on the similarity scores. Experimental results demonstrate that the model exhibits favorable classification performance, with an accuracy of 73.45%. Furthermore, the percentage of debris flow valleys in high and very high susceptibility zones reaches 93.97%. [ABSTRACT FROM AUTHOR]

A devastating landslide incident occurred on 24 June 2017, causing huge losses for Xinmo Village in western Sichuan. In this paper, we used two interferometric synthetic aperture radar (InSAR) methods, permanent scatterer (PS)-InSAR and small baseline subset (SBAS)- InSAR, to analyze deformation signals in the area in the 2 years leading up to the landslide event using Sentinel-1A ascending data. Our experimental findings from PS-InSAR and SBAS-InSAR revealed that the deformation rates in the study region ranged between -50 to 20 mm/year and -30 to 10 mm/year, respectively. Furthermore, the deformation rates of the same points, as determined by these methods, exhibited a significant increase prior to the event. We also investigated the causal relationship between rainfall and landslide events, demonstrating that deformation rates correlate with changes in rainfall, albeit with a time lag. Therefore, using time-series InSAR for landslide monitoring in Xinmo Village is a viable approach. [ABSTRACT FROM AUTHOR]

This paper proposes a framework that combines the improved "You Only Look Once" version 5 (YOLOv5) and SegFormer to extract tailings ponds from multi-source data. Points of interest (POIs) are crawled to capture potential tailings pond regions . Jeffries--Matusita distance is used to evaluate the optimal band combination. The improved YOLOv5 replaces the backbone with the PoolFormer to form a PoolFormer backbone. The neck introduces the CARAFE operator to form a CARAFE feature pyramid network neck (CRFFPN). The head is substituted with an efficiency decoupled head. POIs and classification data optimize improved YOLOv5 results. After that, the SegFormer is used to delineate the boundaries of tailings ponds. Experimental results demonstrate that the mean average precision of the improved YOLOv5s has increased by 2.78% compared to the YOLOv5s, achieving 91.18%. The SegFormer achieves an intersection over union of 88.76% and an accuracy of 94.28%. [ABSTRACT FROM AUTHOR]

To extract terraced fields in hilly areas on a large scale in an automated and high-precision manner, this paper proposes a terrace extraction method that combines the Digital Elevation Model (DEM), Sentinel-2 imagery, and the improved U-Net semantic segmentation model. The U-Net model is modified by introducing Attention Gate modules into its decoding modules to suppress the interference of redundant features and adding Dropout and Batch Normalization layers to improve training speed, robustness, and fitting ability. In addition, the DEM band is combined with the red, green, and blue bands of the remote sensing images to make full use of terrain information. The experimental results show that the Precision, Recall, F1 score, and Mean Intersection over Union of the proposed method for terrace extraction are improved to other mainstream advanced methods, and the internal information of the terraces extracted is more complete, with fewer false positive and false negative results. [ABSTRACT FROM AUTHOR]

The density and uniformity of lidar data play crucial roles in the corresponding data processing steps. One factor influencing point density and spacing in lidar data is the presence of empty pulses, where no return is detected. Missing returns can occur due to atmospheric absorption, specular and diffusive reflection, etc. To address this issue and enhance point density, this paper introduces a novel method for approximating missing returns in airborne lidar data collected over urban areas. This technique focuses on approximating returns for empty pulses that hit spots near abrupt slope changes on building and ground surfaces. The proposed methodology is validated through experiments using a lidar data set from downtown Dublin, Ireland. The collected data contained numerous gaps associated with wet surfaces, as well as missing returns on vertical and oblique surfaces. [ABSTRACT FROM AUTHOR]

High altitude lidar scans allow for rapid acquisition of big spatial data representing entire city blocks. Unfortunately, the raw point clouds acquired by this method are largely incomplete due to object occlusions and restrictions in scanning angles and sensor resolution, which can negatively affect the obtained results. In recent years, many new solutions for 3D point cloud completion have been created and tested on various objects; however, the application of these methods to high-altitude lidar point clouds of buildings has not been properly investigated yet. In the above context, this paper presents the results of applying several state-of-the-art point cloud completion networks to various building exteriors acquired by simulated airborne laser scanning. Moreover, the output point clouds generated from partial data are compared with complete ground-truth point clouds. The performed tests show that the SeedFormer network trained on the ShapeNet-55 data set provides promising shape completion results. [ABSTRACT FROM AUTHOR]

Deep learning has been widely used in remote sensing image classification and achieves many excellent results. These methods are all based on relatively balanced data sets. However, in real-world scenarios, many data sets belong to the long-tailed distribution, resulting in poor performance. In view of the good performance of contrastive learning in long-tailed image classification, a new dualbranch fusion learning classification model is proposed to fuse the discriminative features of remote sensing images with spatial data, making full use of valuable image representation information in imbalance data. This paper also presents a hybrid loss, which solves the problem of poor discrimination of extracted features caused by large intra-class variation and inter-class ambiguity. Extended experiments on three long-tailed remote sensing image classification data sets demonstrate the advantages of the proposed dual-branch model based on contrastive learning in long-tailed image classification. [ABSTRACT FROM AUTHOR]

With the rapid development of lidar, the accuracy and density of the Digital Elevation Model (DEM) point clouds have been continuously improved. However, in some applications, dense point cloud has no practical meaning. How to effectively sample from the dense points and maximize the preservation of terrain features is extremely important. This paper will propose a DEM sampling algorithm that utilizes terrain complexity and maximal Poisson-disk sampling to extract key feature points for adaptive DEM sampling. The algorithm estimates terrain complexity based on local terrain variation and prioritizes points with high complexity for sampling. The sampling radius is inversely proportional to terrain complexity, while ensuring that points within the radius of accepted samples are not considered new samples. This way makes more points of concern in the rugged regions. The results show that the proposed algorithm has higher global accuracy than the classic six sampling methods. [ABSTRACT FROM AUTHOR]

This document is a call for submissions for a special issue of the journal "Photogrammetric Engineering & Remote Sensing" focused on the advancements in hyperspectral remote sensing. The document highlights the new generation of hyperspectral sensors, including those already in orbit and those planned for launch in the future. The goal of the special issue is to gather scientific papers that utilize data from these sensors for various scientific applications and compare their performance with multispectral broadband sensors. The document also outlines specific topics of interest for submissions, such as data processing techniques, hyperspectral data analysis, and the development of hyperspectral vegetation indices. The submission deadline for manuscripts is March 31, 2024, and the tentative publication date for the special issue is August 2024. [Extracted from the article]