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The article discusses important methods in preparation for writing a scientific, peer-reviewed journal paper. It highlights the need to consider the appropriate journals, understand its various components and the professional ethics, co-authors and internal review. Moreover, the significance of being a paper reviewer in gaining knowledge of what constitutes as a high-quality research paper is also emphasized.

Reports on several recipients of the Leica Geosystems Award for Best Scientific Paper in Remote Sensing during the American Society for Photogrammetry and Remote Sensing 2004 Annual Conference in Denver, Colorado. John Rogan; Jennifer Miller; Doug Stow.

To address the problem of weak robustness against geometric attacks of remote sensing images' digital watermarking, a robust watermarking algorithm based on template watermarking is proposed in this paper, which improves the robustness of digital watermarking against geometric attacks by constructing stable geometric attack invariant features. In this paper, the Discrete Fourier Transform domain template watermark is used as the invariant feature against geometric attacks, and the embedding of the cyclic watermark is used to improve the watermark robustness for recovering the watermark synchronization relationship. To achieve blind extraction of the watermark, a parameter extraction method based on noise extraction is designed. The experimental results demonstrate that the proposed method can effectively improve the robustness of digital watermarking of remote sensing images against geometric attacks. Meanwhile, it can also resist common image processing attacks and compound attacks. [ABSTRACT FROM AUTHOR]

The article announces awards given to various science researchers from the Environmental Systems Research Institute, Inc. (ESRI) through the American Society for Photogrammetry and Remote Sensing (ASPRS) Foundation including the first place for Rongxing Li, Kaichang Di, and Xutong Niu for their paper "A WebGIS for Spatial Data Processing, Analysis, and Distribution for the MER 2003 Mission," another for Rifaat Abdalla, C. Vincent Tao, and Jonathan Li for their paper "A Network-centric Modeling Approach for Infrastructure Interdependency," and another for Pravara Thanapura, Dennis L. Helder, and Eric Warmath for their paper "Mapping Urban Land Cover Using Quickbird NDVI and GIS Spatial Modeling for Runoff Coefficient Determination."

The article announces awards given to various science researchers from Leica Geosystems GIS & Mapping through the American Society for Photogrammetry and Remote Sensing (ASPRS) Foundation including the first place for Frank Crosby for his paper "Adaptive Correlation Analysis with Nonoverlapping Imagery Indication," another for Zhong Lu for his paper "InSAR Imaging of Volcanic Deformation Over Cloudprone Areas - Aleutian Islands," and another by A. Baccini, M. A. Friedi, and C. E. Woodcock for their paper "Scaling Field Data to Calibrate and Validate Moderate Spatial Resolution Remote Sensing Models."

The article offers information on the winners of the ESRI Award for Best Scientific Paper in GIS awards. It states that the American Society for Photogrammetry and Remote Sensing (ASPRS) ESRI Award Selection Committee made the announcement concerning the winners of this award. Winners include Rongxing Li, Jue Wang, and Sanchit Agarwal for their paper "A WebGIS for Spatial Data Processing, Analysis, and Distribution for the MER 2003 Mission," Qiuming Cheng and C. Vincent Tao for their paper "A Network-centric Modeling Approach for Infrastructure Interdependency," and Pravara Thanapura and Dennis L. Helder for their work "Mapping Urban Land Cover Using Quickbird NDVI and GIS Spatial Modeling for Runoff Coefficient Determination."

A call for papers on remote sensing of global croplands and their water use for security of food in the Twenty-First Century for August 2012 special issue of journal "Photogrammetric Engineering & Remote Sensing," is presented.

The article provides information about the ASPRS Awards Program for 2023, which includes scholarships for undergraduate and graduate students, as well as awards for professionals in the field of spatial and image sciences. The awards are determined by committee selection and cover categories such as outstanding papers, professional achievement, and service activities. The article also mentions specific individuals who have received awards for their contributions to the society. Additionally, the article highlights the winners of the Outstanding Paper Awards and the Talbert Abrams Award, as well as the recipients of the Robert E. Altenhofen Memorial Scholarship and the John O. Behrens Institute for Land Information Memorial Scholarship. The ASPRS Foundation aims to encourage and support individuals pursuing studies and research in the field of geospatial sciences, including remote sensing, photogrammetry, and GIS. The scholarships are awarded to students and researchers who display exceptional interest, ability, and aptitude in these fields and have a special interest in practical applications of geospatial technologies. The awards consist of certificates, monetary grants, and one-year membership renewals in the Society. [Extracted from the article]

The article announces the 2009 Boeing Award for Best Paper in Image Analysis and Interpretation given to Robert A. Chastain Jr., Matthew A. Struckhoff, and David R. Larsen from the American Society for Photogrammetry and Remote Sensing (ASPRS).

The article provides information on the John I. Davidson President's Award for Practical Papers

The article provides information on the Boeing Award for Best Paper in Image Analysis and Interpretation.

The article offers information on the winners of the Boeing Award for Best Paper in Image Analysis and Interpretation. The American Society for Photogrammetry and Remote Sensing (ASPRS) Boeing Award Selection Committee has announced that Xiaoliang Lu, Ronggao Liu, Jiyuan Liu, and Shunling Liang have received this award. Relative to this, it is posed that presentation of this award will take place in May 2008 during the ASPRS Annual Conference to be conducted in Portland, Oregon. The award aims to induce development and recognize achievement in image interpretation and analysis through acknowledgement of efficient publication in this field.

Reports on several recipients of the ESRI Award for Best Scientific Paper in GIS during the American Society for Photogrammetry and Remote Sensing 2004 Annual Conference in Denver, Colorado. Lucie Plourde; Russell Congalton; Ross Lunetta.

Reports on several recipients of the John I. Davidson President's Award for Practical Papers during the American Society for Photogrammetry and Remote Sensing 2004 Annual Conference in Denver, Colorado. Ross Nelson; Geoffrey Parker; Milton Hom.

Reports that Paul Sutton, Chris Elvidge and Tom Obremski were given the Boeing Autometric Award for Best Paper in Image Analysis and Interpretation during the American Society for Photogrammetry and Remote Sensing 2004 Annual Conference in Denver, Colorado.

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]

For remote sensing image object detection tasks in the small object feature, extraction ability is insufficient and difficult to locate, and other problems. This paper proposes an improved algorithm for small object detection in remote sensing images based on a window self-attention mechanism. On the basis of You Only Look Once (YOLO)v5s, a shallow feature extraction layer with four times downsampling is added to the feature fusion pyramid and the window self-attention mechanism is added to the Path Aggregation Network. Experiments show that the improved model obtained the Mean Average Precision (mAP) of 78.3% and 91.8% on the DIOR and Remote Sensing Object Detection public data sets with frames per second of 65 and 51, respectively. Compared with the basal YOLOv5s network, the mAP has improved by 5.8% and 3.3%, respectively. Compared with other object detection methods, the detection accuracy and real-time performance have been improved. [ABSTRACT FROM AUTHOR]

This study aims at applying the improved You Only Look Once V5s model for the assessment of regional poverty using remote sensing image target detection. The model was improved from structure, algorithm, and components. Objects in the remote sensing images were used to identify poverty, and the poverty alleviation situation could be predicted according to the existing detection results. The results showed that the values of Precision, Recall, mean Average Precision (mAP)@0.5, and mAP@0.5:0.95 of the model increased 7.3%, 0.7%, 1%, and 7.2%, respectively on the Common Objects in Context data set in the detection stage; the four values increased 3.1%, 2.2%, 1.3%, and 5.7%, respectively on the custom remote sensing image data set in the verification stage. The loss values decreased 2.6% and 37.4%, respectively, on the two data sets. Hence, the application of the improved model led to the more accurate detection of the targets. Compared with the other papers, the improved model in this paper proved to be better. Artificial poverty alleviation can be replaced by remote sensing image processing because it is inexpensive, efficient, accurate, objective, does not require data, and has the same evaluation effect. The proposed model can be considered as a promising approach in the assessment of regional poverty. [ABSTRACT FROM AUTHOR]

The author discusses various issues related to the "Photogrammetric Engineering & Remote Sensing" journal, including its Applications Paper component.

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, an improved aerosol optical depth (AOD) retrieval algorithm is proposed based on Sentinel-2 and AErosol RObotic NETwork (AERONET) data. The surface reflectance for AOD retrieval was estimated from the image that had minimal aerosol contamination in a temporal window determined by AERONET data. Validation of the Sentinel-2 AOD retrievals was conducted against four Aerosol Robotic Network (AERONET) sites located in Beijing. The results show that the Sentinel-2 AOD retrievals are highly consistent with the AERONET AOD measurements (R = 0.942), with 85.56% falling within the expected error. The mean absolute error and the root-mean-square error are 0.0688 and 0.0882, respectively. In addition, the AOD distribution map obtained by this algorithm well reflects the fine-spatial-resolution changes in AOD distribution. These results suggest that the improved high-resolution AOD retrieval algorithm is robust and has the potential advantage of retrieving high-resolution AOD over urban areas. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to demonstrate how geospatial technologies, especially remote sensing, can play a leading role in defining urban sustainability based on the evaluation of demand and supply of ecosystem services (ES). A brief review of sustainable development and urban sustainability will be given followed by demonstrations of the need for green spaces in cities, and the consequences of fragmentation of green spaces on biodiversity. Although there are no substantive figures for desirable levels of green spaces in urban areas for the benefit of inhabitants, the paper proposes minimum desirable areal percentages. The paper defines natural capital and ES and the procedures adopted by researchers in balancing the supply and demand for ES for urban areas. The genuine progress indicator is presented as a measure of assessing human welfare, but it is not pursued as an indicator of sustainability. Examples of the applications of remote sensing technologies for determining supply and demand of ES are reviewed as are the potential of the supply and demand of ES to support decision-making in urban areas, to ensure that development decisions are sustainable and are in the best interests of the urban residents who depend on ES for their life support. [ABSTRACT FROM AUTHOR]

The article discusses a white paper published by a manufacturer of unmanned aircraft systems (UAS) claiming that their system can create orthophotos with a positional accuracy of 1.0 cm without using ground control points (GCPs). However, the author of the article finds the manufacturer's claim to be exaggerated and not plausible. The author points out several issues with the accuracy assessment conducted by the manufacturer, including the use of a small number of checkpoints, failure to meet accuracy standards, and reliance on aerial triangulation processing rather than actual measurements on the final orthorectified imagery. The author concludes that GCPs are necessary for achieving high product accuracy. [Extracted from the article]

Correspondence selection is an indispensable process in point cloud registration. The success of point cloud registration largely depends on a good correspondence selection method. For this purpose, a novel correspondence selection method is proposed in this paper. First, two geometric constraints, one of which is proposed in this paper, are used to compute the compatibility score between two correspondences. Then, the feature vectors of the correspondences are constructed according to the compatibility scores between the correspondence and others. A support vector machine classifier is trained to classify the correct and incorrect correspondences by using the feature vectors. The experimental results demonstrate that our method can choose the right correspondences well and get high precision and F-score performance. Also, our method has the best robustness to noise, point density variation, and partial overlap compared to the other methods. [ABSTRACT FROM AUTHOR]

It was difficult to accurately obtain crop planting structure by using the spectral information of high spatial resolution and low spatial resolution multispectral images of panchromatic images at the same time. In this paper, we propose a method of planting structure extraction based on indices and an improved U-Net semantic segmentation network. Based on the original band of Landsat-8, we used an image fusion algorithm to highlight the characteristics of vegetation, water, and soil respectively by three indices added, and the improved U-Net network was used to classify the type of planting structure. The results showed that the overall accuracy of classification was more than 91.6%, and the accuracy of crops was up to 93.8%. Automated water extraction index in image fusion effectively improved the classification accuracy. This method could extract a variety of information about planting structures automatically and accurately. It provided theoretical support for adjusting and optimizing regional planting structures. [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]

Deep learning (dl), especially convolutional neural networks (cnns), has been proven to be an excellent feature extractor and widely applied to hyperspectral image (hsi) classification. However, dl is a computationally demanding algorithm with many parameters and a high computational burden, which seriously restricts the deployment of dl-based hsi classification algorithms on mobile and embedded systems. In this paper, we propose an extremely lightweight conditional three-dimensional (3D) hsi with a double-branch structure to solve these problems. Specifically, we introduce a lightweight conditional 3D convolution to replace the conventional 3D convolution to reduce the computational and memory cost of the network and achieve flexible hsi feature extraction. Then, based on lightweight conditional 3D convolution, we build two parallel paths to independently exploit and optimize the diverse spatial and spectral features. Furthermore, to precisely locate the key information, which is conducive to classification, a lightweight attention mechanism is carefully designed to refine extracted spatial and spectral features, and improve the classification accuracy with less computation and memory costs. Experiments on three public hsi data sets show that the proposed model can effectively reduce the cost of computation and memory, achieve high execution speed, and better classification performance compared with several recent dl-based models. [ABSTRACT FROM AUTHOR]

In this paper, the frequency shift system calibration of the wind imaging interferometer is analyzed. By establishing the frequency shift system vibration and reflectivity models, the single factor and comprehensive factors models are used to invert the wind speed and temperature, respectively. The parameters of the frequency shift system that meet the design accuracy requirement of the instrument are determined. The conclusion of this paper provides theoretical instructions for the calibration process of wind imaging interferometer. [ABSTRACT FROM AUTHOR]

In this paper we review current state-of-the-art in 3D point cloud classification, present a new 3D point cloud classification benchmark data set of single scans with over four billion manually labeled points, and discuss first available results on the benchmark. Much of the stunning recent progress in 2D image interpretation can be attributed to the availability of large amounts of training data, which have enabled the (supervised) learning of deep neural networks. With the data set presented in this paper, we aim to boost the performance of CNNs also for 3D point cloud labeling. Our hope is that this will lead to a breakthrough of deep learning also for 3D (geo-) data. The semantic3D.net data set consists of dense point clouds acquired with static terrestrial laser scanners. It contains eight semantic classes and covers a wide range of urban outdoor scenes, including churches, streets, railroad tracks, squares, villages, soccer fields, and castles. We describe our labeling interface and show that, compared to those already available to the research community, our data set provides denser and more complete point clouds, with a much higher overall number of labeled points. We further provide descriptions of baseline methods and of the first independent submissions, which are indeed based on CNNs, and already show remarkable improvements over prior art. We hope that semantic3D. net will pave the way for deep learning in 3D point cloud analysis, and for 3D representation learning in general. [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]

The detection of changes in synthetic aperture radar (SAR) images based on deep learning has been widely used in landslides detection, flood disaster monitoring, and other fields of change detection due to its high classification accuracy. However, the inherent speckle noise in SAR images restricts the performance of existing SAR image change detection algorithms by clustering analysis. Therefore, this paper proposes a novel method for SAR image change detection based on clustering fusion and deep neural networks. We first used hierarchical fuzzy c-means clustering (HFCM) to process two different images to obtain HFCM classification results. Then a fusion strategy is designed to obtain the fused image from the two HFCM classified images as the pre-classification result. Furthermore, a lightweight deep neural network composed of a decomposition convolution module and an auxiliary classification module was proposed; the former module could reduce network parameters by 28%, and the latter could reduce network parameters by 33.3%. To improve the recognition performance of the network, the classification layer was replaced by the regression layer at the outcome of the network. By comparing the experiments of different methods on five data sets, the performance of our proposed method is superior. [ABSTRACT FROM AUTHOR]

Orthorectification of high-resolution satellite images using a terraindependent rational function model (RFM) is a difficult task requiring a well-distributed set of ground control points (GCPs), which is often time-consuming and costly operation. Further, RFM is sensitive to over-parameterization due to its many coefficients, which have no physical meaning. Optimization-based meta-heuristic algorithms appear to be an efficient solution to overcome these limitations. This paper presents a complete automated RFM terrain-dependent orthorectification for satellite images. The proposed method has two parts; the first part suggests automating the GCP extraction by combing Scale- Invariant Feature Transform and Speeded Up Robust Features algorithms; and the second part introduces the cascaded meta-heuristic algorithm using genetic algorithms and particle swarm optimization. In this stage, a modified K-means clustering selection technique was used to support the proposed algorithm for finding the best combinations of GCPs and RFM coefficients. The obtained results are promising in terms of accuracy and stability compared to other literature methods. [ABSTRACT FROM AUTHOR]

Terrestrial water storage (TWS) plays a vital role in climatological and hydrological processes. Most of the developed drought indices from the Gravity Recovery and Climate Experiment (GRACE) over Africa neglected the influencing roles of individual water storage components in calculating the drought index and thus may either underestimate or overestimate drought characteristics. In this paper, we proposed a Weighted Water Storage Deficit Index for drought assessment over the major river basins in Africa (i.e., Nile, Congo, Niger, Zambezi, and Orange) with accounting for the contribution of each TWS component on the drought signal. We coupled the GRACE data and WaterGAP Global Hydrology Model through utilizing the component contribution ratio as the weight. The results showed that water storage components demonstrated distinctly different contributions to TWS variability and thus drought signal response in onset and duration. The most severe droughts over the Nile, Congo, Niger, Zambezi, and Orange occurred in 2006, 2012, 2006, 2006, and 2003, respectively. The most prolonged drought of 84 months was observed over the Niger basin. This study suggests that considering the weight of individual components in the drought index provides more reasonable and realistic drought estimates over large basins in Africa from GRACE. [ABSTRACT FROM AUTHOR]

Although most deep learning-based methods have achieved excellent performance for hyperspectral image (HSI) classification, they are often limited by complex networks and require massive training samples in practical applications. Therefore, designing an efficient, lightweight model to obtain better classification results under small samples situations remains a challenging task. To alleviate this problem, a novel, lightweight parallel octave convolutional neural network (LPOCNN) for HSI classification is proposed in this paper. First, the HSI data is preprocessed to construct two three-dimensional (3D) patch cubes with different spatial and spectral scales for each central pixel, removing redundancy and focusing on extracting spatial features and spectral features, respectively. Next, two nondeep parallel branches are created for the two inputs, which design octave convolution rather than classical 3D convolution to facilitate light weighting of the model. Then two-dimensional convolutional neural network is used to extract deeper spectral-spatial features when fusing spectral-spatial features from different parallel layers. Moreover, the spectral-spatial attention is designed to promote the classification performance even further by adaptively adjusting the weights of different spectral-spatial features according to their contribution to classification. Experiments show that our suggested LPOCNN acquires a significant advantage on classification performance over other competitive methods under small sample situations. [ABSTRACT FROM AUTHOR]

The precise geo-positioning of high-resolution satellite imagery (HRSI) without ground control points (GCPs) is an important and fundamental step in global mapping, three-dimensional modeling, and so on. In this paper, to improve the efficiency of large-scale bundle adjustment (BA), we propose a combined Preconditioned Conjugate Gradient (PCG) and Graphic Processing Unit (GPU) parallel computing approach for the BA of large-scale HRSI without GCPs. The proposed approach consists of three main components: 1) construction of a BA model without GCPs; 2) reduction of memory consumption using the Compressed Sparse Row sparse matrix format; and 3) improvement of the computational efficiency by the use of the combined PCG and GPU parallel computing method. The experimental results showed that the proposed method: 1) consumes less memory consumption compared to the conventional full matrix format method; 2) demonstrates higher computational efficiency than the single-core, Ceressolver and multi-core central processing unit computing methods, with 9.48, 6.82, and 3.05 times faster than the above three methods, respectively; 3) obtains comparable BA accuracy with the above three methods, with image residuals of about 0.9 pixels; and 4) is superior to the parallel bundle adjustment method in the reprojection error [ABSTRACT FROM AUTHOR]

Remote sensing scene images are characterized by intra-class diversity and inter-class similarity. When recognizing remote sensing images, traditional image classification algorithms based on deep learning only extract the global features of scene images, ignoring the important role of local key features in classification, which limits the ability of feature expression and restricts the improvement of classification accuracy. Therefore, this paper presents a multi-channel attention fusion network (MCAFNet). First, three channels are used to extract the features of the image. The channel "spatial attention module" is added after the maximum pooling layer of two channels to get the global and local key features of the image. The other channel uses the original model to extract the deep features of the image. Second, features extracted from different channels are effectively fused by the fusion module. Finally, an adaptive weight loss function is designed to automatically adjust the losses in different types of loss functions. Three challenging data sets, UC Merced Land-Use Dataset (UCM), Aerial Image Dataset (AID), and Northwestern Polytechnic University Dataset (NWPU), are selected for the experiment. Experimental results show that our algorithm can effectively recognize scenes and obtain competitive classification results. [ABSTRACT FROM AUTHOR]