Image Block Research Articles

Satellite–Terrestrial Collaborative Object Detection via Task-Inspired Framework

Recently, buoyed by advances in the space industry, low Earth orbit (LEO) satellites have become an important part of the Internet of Things (IoT). LEO satellites have entered the era of a big data link with IoT, how to deal with the data from the satellite IoT is a problem worthy of consideration. Conventional object detection method in optical remote sensing simply transmits the raw data to the ground. However, it ignores the properties of the images and the connection with the downstream task. To obtain efficient data transmission and accurate object detection, we propose a task-inspired satellite-terrestrial collaborative object detection framework called STCOD. It detects regions of interest (ROIs) and adopts a block-based adaptive sampling method to compress the background (BG) in optical remote sensing images by introducing satellite edge computing (SEC) on satellites. The STCOD framework also sets the transmission priority of image blocks according to their contributions to the task and uses fountain code to ensure the reliable transmission of important image blocks. We build a whole software simulation framework to validate our method, including the satellite module, the transmission module, and the terrestrial module. Extensive experimental results show that the STCOD framework can reduce the amount of downlink data decreased by 50.04% while losing the detection accuracy by 0.54%. In our simulated satellite-terrestrial link, the STCOD framework can reduce the number of satellite-to-terrestrial transmissions by half. When the packet loss rate is between 5% and 20%, the detection accuracy is lost only 0.05% to 0.5%.

Enhancing of coverless image steganography capacity based on image block features

Enhancing of coverless image steganography capacity based on image block features

Activation extending based on long-range dependencies for weakly supervised semantic segmentation.

Weakly supervised semantic segmentation (WSSS) principally obtains pseudo-labels based on the class activation maps (CAM) to handle expensive annotation resources. However, CAM easily involves false and local activation due to the the lack of annotation information. This paper suggests weakly supervised learning as semantic information mining to extend object mask. We proposes a novel architecture to mining semantic information by modeling through long-range dependencies from in-sample and inter-sample. Considering the confusion caused by the long-range dependencies, the images are divided into blocks and carried out self-attention operation on the premise of fewer classes to obtain long-range dependencies, to reduce false predictions. Moreover, we perform global to local weighted self-supervised contrastive learning among image blocks, and the local activation of CAM is transferred to different foreground area. Experiments verified that superior semantic details and more reliable pseudo-labels are captured through these suggested modules. Experiments on PASCAL VOC 2012 demonstrated the proposed model achieves 76.6% and 77.4% mIoU in val and test sets, which is superior to the comparison baselines.

Social concerns about anxious arousal explain the association between neural responses to anxious arousal pictures and social anxiety

Cognitive theories propose that social anxiety disorder involves heightened attention to anxious arousal symptoms due to worries that they may evoke rejection from others. Supporting this, studies have shown that social anxiety is related to greater attention to representations of anxious arousal and to anxiety sensitivity social concerns, which refers to sensitivity to feelings of anxious arousal during social situations. However, this has not yet been tested using neural indices of attention to images depicting anxious arousal. To examine these associations, the current study examined early and sustained attentional bias to anxious arousal images using the P2 and the late positive potential (LPP), respectively. Electroencephalogram data were collected while a non-clinical sample of undergraduate students (N = 106) viewed images of people exhibiting anxious arousal in addition to blocks of negative and neutral images from the IAPS. The neural response to anxious arousal images was isolated using residual scores (e.g., using linear regression to predict the P2 elicited by anxious arousal images from the P2 elicited by neutral images (P2neutral→AA) or negative images (P2negative→AA), then saving the unstandardized residuals). There was an indirect effect of the P2neutral→AA and P2negative→AA waveforms that was explained by anxiety sensitivity social concerns. Additionally, there was an indirect effect of both LPP waveforms on social anxiety symptoms during the early time window of the LPP (400–700 ms). At the later time window of the LPP (700–1000 ms), there was an indirect effect of the LPPneutral→AA residual waveform, but not the LPPnegative→AA, on social anxiety symptoms.

Cryptanalysis of iterative encryption and image sharing scheme based on the VQ attack

Reversible data hiding in encrypted images (RDHEI) is widely employed to protect privacy in images stored on cloud storage. Recently, Yu et al. [22] proposed an effective image encryption scheme called iterative encryption and image sharing (IEIS), which is highly compatible with RDHEI. In this study, we analyze the characteristics of IEIS and present a cryptanalysis scheme based on vector quantization (VQ) attack. To capture the pixel-changing pattern of an image block, the concept of pixel difference matrix (PDM) is introduced. VQ attack is subsequently utilized to estimate the ciphertext block. Furthermore, we employ a well-trained convolutional neural network (CNN) based discriminator to identify the proper block permutation sequence within the secret key space. Experimental results demonstrate the efficacy of our proposed cryptanalysis scheme. Based on our study, we conclude that the IEIS scheme raises security concerns.

Efficient Super-Resolution System With Block-Wise Hybridization and Quantized Winograd on FPGA

Super-resolution (SR) techniques aim to restore a high-resolution image from low-resolution images, which are often used to assist the enhancement of image/video quality under the rapid development of high-resolution and high-framerate media. Recently, neural network (NN)-based methods perform much better image reconstruction quality than classical approaches. However, the unacceptable computation complexity as well as the huge memory footprints of NNs limit the throughputs and scalability of these SR systems. In this work, we analyze several key issues in the design of NN-based SR systems first. Then, we propose a three-level systematic optimization methodology for SR systems to reduce computation overhead and keep image quality. At the algorithm level, we introduce image blocking to SR tasks and develop a block-wise SR algorithm based on the hybrid of NN and interpolation with a consistent image block evaluation metric. The configurable hybrid parameters help the SR algorithm to achieve a flexible trade-off between the computation overhead and image quality. At the operator level, we focus on the transpose convolution operators commonly used for upsampling in SR neural networks. We propose an efficient Winograd-based transposed convolution acceleration method. Through the efficient sub-convolutions conversion and the Winograd specialization, this methods enables unified Winograd transformations and simplified data access patterns. At the data level, we propose a novel quantization method for Winograd-aware SR neural networks to get better quantized accuracy. Comprehensive evaluations demonstrate the effectiveness of these optimizations. Our SR system reduces a large number of multiplications with great scalability and supports 4K@120fps and 8K@30fps outputs with acceptable image quality degradation.

Nectarine Disease Identification Based on Color Features and Label Sparse Dictionary Learning with Hyperspectral Images

Fruit cracking and rust spots are common diseases of nectarines that seriously affect their yield and quality. Therefore, it is essential to construct fast and accurate disease-identification models for agricultural products. In this paper, a sparse dictionary learning method was proposed to realize the rapid and nondestructive identification of nectarine disease based on multiple color features combined with improved LK-SVD (Label K-Singular Value Decomposition). According to the color characteristics of the nectarine itself and the significant color differences existing in the three categories of nectarine (diseased, normal, and background parts), multiple color spaces of RGB, HSV, Lab, and YCbCr were studied. It was concluded that the G channel in RGB space, Y channel in YCbCr space, and L channel in Lab space can better distinguish the diseased part from the other parts. At the model-training stage, pixels of the diseased, normal, and background parts in the nectarine image were randomly selected as the initial training sets, and then, the neighboring image blocks of the pixels were selected to construct the feature vectors based on the above color space channels. An improved LK-SVD dictionary learning algorithm was proposed that integrated the category label into the process of dictionary learning, and thus, an over-complete feature dictionary with significant discrimination was obtained. At the model-testing stage, the orthogonal matching pursuit (OMP) algorithm was used for sparse reconstruction of the original data, which can obtain the classification categories based on the optimized feature dictionary. The experimental results show that the sparse dictionary learning method based on multi-color features combined with improved LK-SVD can identify fruit cracking and rust spot diseases of nectarines quickly and accurately, and the average overall classification accuracies were 92.06% and 88.98%, respectively, which were better than those of k-nearest neighbor (KNN), support vector machine (SVM), DeepLabV3+, and Unet++; the identification results of DeepLabV3+ and Unet++ were also relatively high, but their average time costs were much higher, requiring 126.46~265.65 s. It is demonstrated that this study can provide technical support for disease identification in agricultural products.

Underwater image enhancement via complementary advantage fusion of global and local contrast

Underwater images encounter a range of quality degradation challenges due to most wavelengths of light being attenuated by varying degrees of absorption in traveling underwater. To cope with these issues, we present a complementary advantage fusion method of global and local contrast, named CAFM. Successively, CAFM first compensate for the attenuation of each channel by exploiting the pixel intensity and distribution of each channel to get an image without color distortion. Subsequently, we employ the double histogram optimization contrast method to enhance the global contrast of the preprocessed image, while the mean and variance features of image blocks are utilized to enhance the local contrast of the preprocessed image. To get a high-quality underwater image, we employ a complementary advantage fusion method to combine the benefits of the two enhanced images via the complementary advantages between different feature maps. Extensive evaluation of three datasets demonstrates that our CAFM surpasses the compared methods. Additionally, the underwater images are enhanced by our CAFM with authentic colors, heightened contrast, and rich texture detailing.

Loop Closure Detection Method Based on Similarity Differences between Image Blocks.

Variations with respect to perspective, lighting, weather, and interference from dynamic objects may all have an impact on the accuracy of the entire system during autonomous positioning and during the navigation of mobile visual simultaneous localization and mapping (SLAM) robots. As it is an essential element of visual SLAM systems, loop closure detection plays a vital role in eradicating front-end-induced accumulated errors and guaranteeing the map's general consistency. Presently, deep-learning-based loop closure detection techniques place more emphasis on enhancing the robustness of image descriptors while neglecting similarity calculations or the connections within the internal regions of the image. In response to this issue, this article proposes a loop closure detection method based on similarity differences between image blocks. Firstly, image descriptors are extracted using a lightweight convolutional neural network (CNN) model with effective loop closure detection. Subsequently, the image pairs with the greatest degree of similarity are evenly divided into blocks, and the level of similarity among the blocks is used to recalculate the degree of the overall similarity of the image pairs. The block similarity calculation module can effectively reduce the similarity of incorrect loop closure image pairs, which makes it easier to identify the correct loopback. Finally, the approach proposed in this article is compared with loop closure detection methods based on four distinct CNN models with a recall rate of 100% accuracy; said approach performs significantly superiorly. The application of the block similarity calculation module proposed in this article to the aforementioned four CNN models can increase the recall rate's accuracy to 100%; this proves that the proposed method can successfully improve the loop closure detection effect, and the similarity calculation module in the algorithm has a certain degree of universality.

A planar dimension measurement optimization method with metric information compensation

Visual dimension measurement is commonly required in industrial detection. However, monocular vision measurements have a low accuracy with large field of view (FOV), while multi-vision measurement involves multi-source information fusion and incurs a large computation overhead. Hence, we propose a planar dimension measurement optimization method with metric information compensation (PMOM). This method constructs a linear representation model of pixel metric information using images at different heights along the camera’s optical axis; designs a multi-resolution multi-dimensional key point mapping mechanism (KPM) to realize the projection from a coarse resolution image point to a fine resolution image block and the secondary optimization of measurement results. By utilizing pixel-level and subpixel-level object extraction algorithms for both coarse resolution and fine resolution image blocks, the algorithm’s complexity and data processing requirements are notably reduced. The paper includes a theoretical proof of the method’s effectiveness, shown by analyzing the measurement errors of different resolution images. Comparative experiments on the measurement of spacing between planar patterns and workpiece machining accuracy validate the method meeting the requirements of industrial high-precision with a large FOV and low computational overhead.

A Rapid Parallel Mosaicking Algorithm for Massive Remote Sensing Images Utilizing Read Filtering

Mosaicking is a crucial step in the application of remote sensing images. The amount of remote sensing image data has grown rapidly, along with the expansion of observed areas and increased image resolution. As a result, traditional serial mosaicking techniques are facing significant challenges. In recent times, various studies have utilized high-performance computing to hasten image mosaicking and attain favorable outcomes. Nevertheless, the current research only accelerates mosaicking through external technology, without optimizing from the perspective of algorithm flow, which introduces unnecessary data I/O and slows down the mosaicking. This paper introduces a rapid parallel remote sensing image mosaicking algorithm utilizing read filtering. To begin with, the target images are divided into blocks and stored in a distributed file system. Subsequently, the image blocks are read and filtered based on a designated input format. Finally, the overlapping and non-overlapping areas are read and processed asynchronously, reducing the data I/O and computing overhead, thereby improving the efficiency of parallel computing. The experiments indicate that the mosaicking algorithm introduced in this paper enhances throughput and speedup by an average of 1.38 MB/S and 0.87 relative to the current techniques, respectively, concerning various datasets and cores. This study provides a theoretical foundation and novel ideas for processing remote sensing images on cluster platforms.

Image denoising and deringing for fourier single-pixel imaging based on upgraded weighted nuclear norm minimization

Single-pixel imaging techniques have been extensively studied because of the low requirements for the detector resolution and the low equipment cost. However, a large amount of noise and ringing commonly occur in the reconstructed images via previous single-pixel imaging methods at ultra-low sampling rates, which limits the real applications. In this paper, a novel Fourier single-pixel imaging method is proposed to reconstruct high-quality single-pixel imaging images at ultra-low sampling rates, which involves the stages of Fourier single-pixel imaging, block matching, upgraded weight nuclear norm minimization, and aggregation. To decrease the computational burden while important image details, block matching and aggregation are employed for the blocks divided from the low-quality image reconstructed by Fourier single-pixel imaging. To adapt the proposed upgraded weight nuclear norm minimization to different contaminated images, an adaptive scheme for the iterative regularization parameter is proposed to update the traditional weight nuclear norm minimization. Experimental results indicate that the proposed Fourier single-pixel imaging method based on upgraded weight nuclear norm minimization exhibits the capacity to reliably and effectively reconstruct high-quality images for single-pixel imaging at low sampling rates, even at the sampling rate of 1 %. Experimental results also demonstrate that the proposed method performs better image reconstruction than some existing single-pixel imaging.

Enhanced YOLOv5 network-based object detection (BALFilter Reader) promotes PERFECT filter-enabled liquid biopsy of lung cancer from bronchoalveolar lavage fluid (BALF)

Liquid biopsy of cancers, detecting tumor-related information from liquid samples, has attracted wide attentions as an emerging technology. Our previously reported large-area PERFECT (Precise-Efficient-Robust-Flexible-Easy-Controllable-Thin) filter has demonstrated competitive sensitivity in recovering rare tumor cells from clinical samples. However, it is time-consuming and easily biased to manually inspect rare target cells among numerous background cells distributed in a large area (Φ ≥ 13 mm). This puts forward an urgent demand for rapid and bias-free inspection. Hereby, this paper implemented deep learning-based object detection for the inspection of rare tumor cells from large-field images of PERFECT filters with hematoxylin-eosin (HE)-stained cells recovered from bronchoalveolar lavage fluid (BALF). CenterNet, EfficientDet, and YOLOv5 were trained and validated with 240 and 60 image blocks containing tumor and/or background cells, respectively. YOLOv5 was selected as the basic network given the highest mAP@0.5 of 92.1%, compared to those of CenterNet and EfficientDet at 85.2% and 91.6%, respectively. Then, tricks including CIoU loss, image flip, mosaic, HSV augmentation and TTA were applied to enhance the performance of the YOLOv5 network, improving mAP@0.5 to 96.2%. This enhanced YOLOv5 network-based object detection, named as BALFilter Reader, was tested and cross-validated on 24 clinical cases. The overall diagnosis performance (~2 min) with sensitivity@66.7% ± 16.7%, specificity@100.0% ± 0.0% and accuracy@75.0% ± 12.5% was superior to that from two experienced pathologists (10–30 min) with sensitivity@61.1%, specificity@16.7% and accuracy@50.0%, with the histopathological result as the gold standard. The AUC of the BALFilter Reader is 0.84 ± 0.08. Moreover, a customized Web was developed for a user-friendly interface and the promotion of wide applications. The current results revealed that the developed BALFilter Reader is a rapid, bias-free and easily accessible AI-enabled tool to promote the transplantation of the BALFilter technique. This work can easily expand to other cytopathological diagnoses and improve the application value of micro/nanotechnology-based liquid biopsy in the era of intelligent pathology.

Dual-Core Adaptive NLM Image Denoising Algorithm Based on Variable-Size Window and Neighborhood Multifeatures

To solve the problem that the similarity calculation between neighbors was easily disturbed by noise in the traditional nonlocal mean (NLM) denoising algorithm, a dual-core NLM denoising algorithm based on neighborhood multifeatures and variable-size search window was proposed. The algorithm first proposed to use the eigenvalues of the structure tensor to classify the region where the target pixel points were located and used different sizes of the search window to search for similar neighborhoods for target pixel points in different categories of the region, thus effectively avoiding the problem of oversmoothing or inadequate denoising of the image caused by the use of the global size. Then, the gradient features between image blocks were defined and combined with grayscale features and spatial features to measure the similarity of neighborhood blocks, which solved the problem of noise interfering with the search of similar blocks. Then, an adaptive algorithm with Gaussian–Sinusoidal dual kernel function and quantitative estimation of the optimal values of the filtering parameters was designed to calculate the neighborhood similarity weights to improve the accuracy of image denoising. Finally, the similarity weights were used to weight and average the search neighborhood of the target pixel points to achieve the denoising of the target pixel points. To test the effectiveness of the algorithm, denoising tests were performed using multiple standard grayscale images with different levels of Gaussian white noise added and compared with several advanced denoising algorithms. The experimental results showed that the algorithm was effective. The algorithm improved the image peak signal-to-noise ratio by more than 56.54% on average when Gaussian white noise was removed, and the structural similarity reached more than 0.701 on average. Compared with the traditional NLM algorithm and other improved algorithms, the algorithm proposed in this paper had strong denoising ability, better protection of edges and texture details, and the quality of the image was greatly improved, which had a good application prospect.

A personalized Monte Carlo study of tumor and critical organ doses for Trans-arterial Radioembolization patients.

Trans-arterial Radioembolization (TARE) is an intra-arterial treatment method for liver malignancies. In this procedure, the therapeutic tumor dose is significant for predicting the treatment effectiveness while the dose absorbed in an organ at risk provides an understanding of its tolerance to radiation. This study proposes a Monte Carlo (MC) approach for determining absorbed organ doses for patients undergoing TARE treatment. The technique is based on the use of a voxel-based partial body model generated for each patient from his/her anatomical image data to represent the critical body structures more realistically. These structures are first segmented from image slices to create an image block which is then incorporated into a radiation transport package (MCNP6.2) to perform MC simulations. When used along with the parameters specific to a patient's treatment, such as lung-shunt factor (LSF), tumor-to-normal liver ratio (TLR), fractional uptakes, and administered activity, this approach allowed more accurate simulation of radiation interactions and hence provided absorbed doses specific to a TARE patient. The MC method also calculated the absorbed doses in organs or tissues that were close to target tissues for which the MIRD formalism makes no predictions. MIRD calculations were found to overestimate the absorbed doses by as much as 11% in lungs, 5% in liver, and 20% in tumor volumes. This raises concerns about the treatment's efficacy when estimating the correct activity to be administered to a patient. When each patient simulation was repeated with a 90Y source spectrum to reflect the distribution of varying beta energies, the liver and the lungs were observed to receive relatively lower doses than those obtained with monoenergetic beta particles. Thus, it can be stated that the approach adopted in this study offers a more precise model of the patient's critical tissues and serves as a personalized dosimetric tool for TARE treatment planning.

Semi-automatic fine delineation scheme for pancreatic cancer.

Pancreatic cancer fine delineation in medical images by physicians is a major challenge due to the vast volume of medical images and the variability of patients. A semi-automatic fine delineation scheme was designed to assist doctors in accurately and quickly delineating the cancer target region to improve the delineation accuracy of pancreatic cancer in computed tomography (CT) images and effectively reduce the workload of doctors. A target delineation scheme in image blocks was also designed to provide more information for the deep learning delineation model. The start and end slices of the image block were manually delineated by physicians, and the cancer in the middle slices were accurately segmented using a three-dimensional Res U-Net model. Specifically, the input of the network is the CT image of the image block and the delineation of the cancer in the start and end slices, while the output of the network is the cancer area in the middle slices of the image block. Meanwhile, the model performance of pancreatic cancer delineation and the workload of doctors in different image block sizes were studied. We used 37 3D CT volumes for training, 11 volumes for validating and 11 volumes for testing. The influence of different image block sizes on doctors' workload was compared quantitatively. Experimental results showed that the physician's workload was minimal when the image block size was 5, and all cancer could be accurately delineated. The Dice similarity coefficient was 0.894±0.029, the 95% Hausdorff distance was 3.465±0.710mm, the normalized surface Dice was 0.969±0.019. By completing the accurate delineation of all the CT images, the speed of the new method is 2.16 times faster than that of manual sketching. Our proposed 3D semi-automatic delineative method based on the idea of block prediction could accurately delineate CT images of pancreatic cancer and effectively deal with the challenges of class imbalance, background distractions, and non-rigid geometrical features. This study had a significant advantage in reducing doctors' workload, and was expected to help doctors improve their work efficiency in clinical application.

Transforaminal Endoscopic Lumbar Foraminotomy for Juxta-Fusional Foraminal Stenosis.

Adjacent segment foraminal stenosis is a significant adverse event of lumbar fusion. Conventional revision surgery with an extended fusion segment may result in considerable surgical morbidity owing to extensive tissue injury. Transforaminal endoscopic lumbar foraminotomy (TELF) is a minimally invasive surgical approach for symptomatic foraminal stenosis. This study aimed to demonstrate the surgical technique and clinical outcomes of TELF for the treatment of juxta-fusional foraminal stenosis. Full-scale foraminal decompression was performed via a transforaminal endoscopic approach under local anesthesia. A total of 22 consecutive patients who had undergone TELF were evaluated. The included patients had unilateral foraminal stenosis at the juxta-fusional level of the previous fusion surgery, intractable lumbar radicular pain despite at least six months of non-operative treatment, and verified pain focus by imaging and selective nerve root block. The visual analog scale and Oswestry Disability Index scores significantly improved after the two-year follow-up period. The modified MacNab criteria were excellent in six patients (27.27%), good in 12 (55.55%), fair in two (9.09%), and poor in two (9.09%), with a 90.91% symptomatic improvement rate. No significant surgical complications were observed. The minimally invasive TELF is effective for juxta-fusional foraminal stenosis.

A Spatial–Temporal Block-Matching Patch-Tensor Model for Infrared Small Moving Target Detection in Complex Scenes

Detecting infrared (IR) small moving targets in complex scenes quickly, accurately, and robustly remains a challenging problem in the current research field. To address this issue, this paper proposes a novel spatial–temporal block-matching patch-tensor (STBMPT) model based on a low-rank sparse decomposition (LRSD) framework. This model enhances the traditional infrared patch-tensor (IPT) model by incorporating joint spatial–temporal sampling to exploit inter-frame information and constructing a low-rank patch tensor using image block matching. Furthermore, a novel prior-weight calculation is introduced, utilizing the eigenvalues of the local structure tensor to suppress interference such as strong edges, corners, and point-like noise from the background. To improve detection efficiency, the tensor is constructed using a matching group instead of using a traditional sliding window. Finally, the background and target components are separated using the alternating direction method of multipliers (ADMM). Qualitative and quantitative experimental analysis in various scenes demonstrates the superior detection performance and efficiency of the proposed algorithm for detecting infrared small moving targets in complex scenes.

Time Image De-Noising Method Based on Sparse Regularization

The blurring of texture edges often occurs during image data transmission and acquisition. To ensure the detailed clarity of the drag-time images, we propose a time image de-noising method based on sparse regularization. First, the image pixel sparsity index is set, and then an image de-noising model is established based on sparse regularization processing to obtain the neighborhood weights of similar image blocks. Second, a time image de-noising algorithm is designed to determine whether the coding coefficient reaches the standard value, and a new image de-noising method is obtained. Finally, the images of electronic clocks and mechanical clocks are used as two kinds of time images to compare different image de-noising methods, respectively. The results show that the sparsity regularization method has the highest peak signal-to-noise ratio among the six compared methods for different noise standard deviations and two time images. The image structure similarity is always above which shows that the proposed method is better than the other five image de-noising methods.

Enhancing UAV-SfM Photogrammetry for Terrain Modeling from the Perspective of Spatial Structure of Errors

UAV-SfM photogrammetry is widely used in remote sensing and geoscience communities. Scholars have tried to optimize UAV-SfM for terrain modeling based on analysis of error statistics like root mean squared error (RMSE), mean error (ME), and standard deviation (STD). However, the errors of terrain modeling tend to be spatially distributed. Although the error statistic can represent the magnitude of errors, revealing spatial structures of errors is still challenging. The “best practice” of UAV-SfM is lacking in research communities from the perspective of spatial structure of errors. Thus, this study designed various UAV-SfM photogrammetric scenarios and investigated the effects of image collection strategies and GCPs on terrain modeling. The error maps of different photogrammetric scenarios were calculated and quantitatively analyzed by ME, STD, and Moran’s I. The results show that: (1) A high camera inclination (20–40°) enhances UAV-SfM photogrammetry. This not only decreases the magnitude of errors, but also mitigates its spatial correlation (Moran’s I). Supplementing convergent images is valuable for reducing errors in a nadir camera block, but it is unnecessary when the image block is with a high camera angle. (2) Flying height increases the magnitude of errors (ME and STD) but does not affect the spatial structure (Moran’s I). By contrast, the camera angle is more important than the flying height for improving the spatial structure of errors. (3) A small number of GCPs rapidly reduce the magnitude of errors (ME and STD), and a further increase in GCPs has a marginal effect. However, the structure of errors (Moran’s I) can be further improved with increasing GCPs. (4) With the same number, the distribution of GCPs is critical for UAV-SfM photogrammetry. The edge distribution should be first considered, followed by the even distribution. The research findings contribute to understanding how different image collection scenarios and GCPs can influence subsequent terrain modeling accuracy, precision, and spatial structure of errors. The latter (spatial structure of errors) should be routinely assessed in evaluations of the quality of UAV-SfM photogrammetry.