A Contrastive Learning Based Multiview Scene Matching Method for UAV View Geo-Localization

Agricultural intensification and efficient use and targeting of fertilizer inputs on smallholder farms is key to sustainably improve food security. The objective of this paper is to demonstrate how high-resolution satellite and unmanned aerial vehicle (UAV) images can be used to assess the spatial variability of yield, and yield response to fertilizer. The study included 48 and 50 smallholder fields monitored during the 2014 and 2015 cropping seasons south-east of Koutiala (Mali), cropped with the five major crops grown in the area (cotton, maize, sorghum, millet and peanuts). Each field included up to five plots with different fertilizer applications and one plot with farmer practice. Fortnightly, in-situ in each field data were collected synchronous with UAV imaging using a Canon S110 NIR camera. A concurrent series of very high-resolution satellite images was procured and these images were used to mask out trees. For each plot, we calculated vegetation index means, medians and coefficients of variation. Cross-validated general linear models were used to assess the predictability of relative differences in crop yield and yield response to fertilizer, explicitly accounting for the effects of fertility treatments, between-field and within-field variabilities. Differences between fields accounted for a much larger component of variation than differences between fertilization treatments.Vegetation indices from UAV images strongly related to ground cover (R2 = 0.85), light interception (R2 = 0.79) and vegetation indices derived from satellite images (R2 values of about 0.8). Within-plot distributions of UAV-derived vegetation index values were negatively skewed, and within-plot variability of vegetation index values was negatively correlated with yield. Plots on shallow soils with poor growing conditions showed the largest within-plot variability. GLM models including UAV derived estimates of light interception explained up to 78% of the variation in crop yield and 74% of the variation in fertilizer response within a single field. These numbers dropped to about 45% of the variation in yield and about 48% of the variation in fertilizer response when lumping all fields of a given crop, with Q2 values of respectively 22 and 40% respectively when tested with a leave-field-out procedure. This indicates that remotely sensed imagery doesn’t fully capture the influence of crop stress and management. Assessment of crop fertilizer responses with vegetation indices therefore needs a reference under similar management. Spatial variability in UAV-derived vegetation index values at the plot scale was significantly related to differences in yields and fertilizer responses. The strong relationships between light interception and ground cover indicate that combining vertical photographs or high-resolution remotely sensed vegetation indices with crop growth models allows to explicitly account for the spatial variability and will improve the accuracy of yield and crop production assessments, especially in heterogeneous smallholder conditions.

To improve the efficiency and effectiveness of mosaicking unmanned aerial vehicle (UAV) images, we propose in this paper a rapid mosaicking method based on scale-invariant feature transform (SIFT) for mosaicking UAV images used for crop growth monitoring. The proposed method dynamically sets the appropriate contrast threshold in the difference of Gaussian (DOG) scale-space according to the contrast characteristics of UAV images used for crop growth monitoring. Therefore, this method adjusts and optimizes the number of matched feature point pairs in UAV images and increases the mosaicking efficiency. Meanwhile, based on the relative location relationship of UAV images used for crop growth monitoring, the random sample consensus (RANSAC) algorithm is integrated to eliminate the influence of mismatched point pairs in UAV images on mosaicking and to keep the accuracy and quality of mosaicking. Mosaicking experiments were conducted by setting three types of UAV images in crop growth monitoring: visible, near-infrared, and thermal infrared. The results indicate that compared to the standard SIFT algorithm and frequently used commercial mosaicking software, the method proposed here significantly improves the applicability, efficiency, and accuracy of mosaicking UAV images in crop growth monitoring. In comparison with image mosaicking based on the standard SIFT algorithm, the time efficiency of the proposed method is higher by 30%, and its structural similarity index of mosaicking accuracy is about 0.9. Meanwhile, the approach successfully mosaics low-resolution UAV images used for crop growth monitoring and improves the applicability of the SIFT algorithm, providing a technical reference for UAV application used for crop growth and phenotypic monitoring.

Robust and rapid image dense matching is the key to large-scale three-dimensional (3D) reconstruction for multiple Unmanned Aerial Vehicle (UAV) images. However, the following problems must be addressed: (1) the amount of UAV image data is very large, but ordinary computer memory is limited; (2) the patch-based multi-view stereo-matching algorithm (PMVS) does not work well for narrow-baseline cases, and its computing efficiency is relatively low, and thus, it is difficult to meet the UAV photogrammetry’s requirements of convenience and speed. This paper proposes an Image-grouping and Self-Adaptive Patch-based Multi-View Stereo-matching algorithm (IG-SAPMVS) for multiple UAV imagery. First, multiple UAV images were grouped reasonably by a certain grouping strategy. Second, image dense matching was performed in each group and included three processes. (1) Initial feature-matching consists of two steps: The first was feature point detection and matching, which made some improvements to PMVS, according to the characteristics of UAV imagery. The second was edge point detection and matching, which aimed to control matching propagation during the expansion process; (2) The second process was matching propagation based on the self-adaptive patch. Initial patches were built that were centered by the obtained 3D seed points, and these were repeatedly expanded. The patches were prevented from crossing the discontinuous terrain by using the edge constraint, and the extent size and shape of the patches could automatically adapt to the terrain relief; (3) The third process was filtering the erroneous matching points. Taken the overlap problem between each group of 3D dense point clouds into account, the matching results were merged into a whole. Experiments conducted on three sets of typical UAV images with different texture features demonstrate that the proposed algorithm can address a large amount of UAV image data almost without computer memory restrictions, and the processing efficiency is significantly better than that of the PMVS algorithm and the matching accuracy is equal to that of the state-of-the-art PMVS algorithm.

Recent advances in structure from motion (SfM) and dense matching algorithms enable surface reconstruction from unmanned aerial vehicle (UAV) images with high spatial resolution, allowing for new insights into earth surface processes. However, accuracy issues are inherent in parallel‐axes UAV image configurations. In this study, the quality of digital elevation models (DEMs) is assessed using images from a simulated UAV flight. Five different SfM tools and three different cameras are compared. If ground control points (GCPs) are not integrated into the adjustment process with parallel‐axes image configurations, significant dome‐effect systematic errors are observed, which can be reduced based on calibration parameters retrieved from a testfield captured with convergent images immediately before or after the UAV flight. A comparison between DEMs of a soil surface generated from UAV images and terrestrial laser‐scanning data show that natural surfaces can be very accurately reconstructed from UAV images, even when GCPs are missing and simple geometric camera models are considered.

This paper presents a novel approach to automatically detect and count cars in different aerial images, which can be satellite or unmanned aerial vehicle (UAV) images. Variations in satellite and/or UAV data make it particularly challenging to have a robust method that works properly on a variety of images. A solution based on the bag-of-words (BoW) model is explored in this paper due to its invariance characteristic and highly stable performance in object/scene categorization. Different from categorization tasks, vehicle detection needs to localize the positions of cars in images. To make BoW suitable for this purpose, we extensively improve the methodology in three aspects, namely, by introducing a recently proposed feature representation, i.e., the local steering kernel descriptor, adding spatial structure constraints, and developing an orientation aware scanning mechanism to produce detection with “one-window-one-car” results. Experiments are conducted on various aerial images with large variations, which consist of data from two public databases, e.g., the Overhead Imagery Research Data Set and Vehicle Detection in Aerial Imagery, as well as other satellite and UAV images. The results demonstrate the effectiveness and robustness of the proposed method. Compared with existing techniques, the proposed method is applicable to a wider range of aerial images.

In this paper, we develop an alternative navigation system for Unmanned Aerial Vehicle (UAV) in Global Positioning Systems (GPS)-denied environment. We use two image inputs, one is acquired with an on-board camera placed on the UAV (which is the large-area image) and the other is from satellite images (which is small known image) with GPS information. We use a convolutional neural network (CNN) architecture based on Oxford’s Visual Geometry Group network (VGG-16) and utilize normalized variant mutual information between these two images to obtain position of the UAV. Satellite images are labelled and given to the UAV. When GPS information is lost, our algorithm starts to function and images from UAV camera are searched whether satellite image is seen by cameras on UAV image or not. If the UAV is in that area, our algorithm finds the GPS information from satellite image data.

Less effective information is obtained by the object detection network, due to the small size of the detection object in the entire image, the complex background, and the dense object in unmanned aerial vehicle (UAV) images. In response to the difficulties encountered, a small object detection method in UAV images is proposed as an improved YOLOv5-based algorithm in this paper. First, the space-to-depth(SPD) conv module is introduced into the basic feature extraction network, to improve significant loss of image information during downsampling. Then, various attention mechanisms are added, to intensify the acquisition of regions of interest in UAV images. Finally, the multiscale detection module is improved, to enhance the network's ability to detect small objects in UAV images. By conducting experiments on the VisDrone-DET2019 dataset, the test results of the established model show. The improved algorithm achieved a Mean Average Precision (mAP) of 41.8%, which is 7.8% better than the baseline network. In addition, the detection performance is better than most current mainstream target detection algorithms and is of some practical value.

Remote sensing images often encounter various challenges arising from differences in shooting time, location, equipment, sensors, and other factors. These disparities lead to image distortion and insufficient overlap between pairs of images captured at the same location. Consequently, the accuracy of agricultural remote sensing image registration is significantly compromised. This paper proposes a robust feature matching technique called GAN-based neighborhood representation (RFM-GAN) for the intricate registration of satellite images and unmanned aerial vehicle (UAV) images. The RFM-GAN method leverages a neighborhood representation approach based on a generative adversarial network (GAN) with two discriminators. This representation enhances the distinction between true matches (inliers) and false matches (outliers). Additionally, a dissimilarity measure network employing a self-supervised training approach, eliminating the need for manual labeling, is designed to handle the multi-view transformation of satellite and UAV images. The experimental results confirm that RFM-GAN outperforms seven other state-of-the-art methods in terms of satellite image and UAV image processing.

Pine is one of the important forest tree species in China, with a wide distribution and significant economic value, accounting for over 70% of China's artificial forest area. However, due to the rapid spread of pine wilt disease, a large number of pine resources have been damaged, seriously affecting ecological civilization. The most effective method currently is to promptly detect and accurately locate pine trees infected with pine wilt disease (diseased trees). Manual survey and traditional detection method based on satellite remote sensing image have limitations in monitoring complex forest areas. With the advantages of real-time monitoring, low cost, and high spatial resolution, unmanned aerial vehicle (UAV) image has been widely used for agriculture and forestry. Currently, UAV images are mostly used for monitoring diseased trees. To further improve the accuracy of identifying diseased trees, this paper proposes a diseased tree detection method based on improved YOLOv5s network, which combines the YOLOv5s algorithm with Bi-directional Feature Pyramid Network (BiFPN) and Shuffle Attention (SA). In order to demonstrate the role and mutual influence of various modules in the improved YOLOv5s network, the detection results of the improved YOLOv5s algorithm and other algorithms on the same pine wilt disease dataset are compared. The experimental results show that our improved algorithm increased 2.1% in mAP and 6.1% in recall while the quantity of parameter and GFLOPs have only increased by 3.2% and 2.5%, respectively.

The sparse representation error (SRE) exists when the images are represented sparsely. The SRE is particularly large in unmanned aerial vehicles (UAV) images due to the disturbance of the harsh environment or the instability of its flight, which will bring more noise. In the compressed sensing (CS) system, the projected SRE in the compressed measurement will bring a significant challenge to the recovery accuracy of the images. In this work, a new SRE structure is proposed. Following the new structure, a lower sparse representation error (LSRE) is achieved by eliminating groups of sparse representation. With the proposed LSRE modeling, a robust sensing matrix is designed to compress and encrypt the UAV images. Experiments for UAV images are carried out to compare the recovery performance of the proposed algorithm with the existing related algorithms. The results of the proposed algorithm reveal superior recovery accuracy. The new CS framework with the proposed sensing matrix to address the scenario of UAV images with large SRE is dominant.

Semantic segmentation of weed and crop images is a key component and prerequisite for automated weed management. For weeds in unmanned aerial vehicle (UAV) images, which are usually characterized by small size and easily confused with crops at early growth stages, existing semantic segmentation models have difficulties to extract sufficiently fine features. This leads to their limited performance in weed and crop segmentation of UAV images. We proposed a fine-grained feature-guided UNet, named FG-UNet, for weed and crop segmentation in UAV images. Specifically, there are two branches in FG-UNet, namely the fine-grained feature branch and the UNet branch. In the fine-grained feature branch, a fine feature-aware (FFA) module was designed to mine fine features in order to enhance the model's ability to segment small objects. In the UNet branch, we used an encoder-decoder structure to realize high-level semantic feature extraction in images. In addition, a contextual feature fusion (CFF) module was designed for the fusion of the fine features and high-level semantic features, thus enhancing the feature discrimination capability of the model. The experimental results showed that our proposed FG-UNet, achieved state-of-the-art performance compared to other semantic segmentation models, with mean intersection over union (MIOU) and mean pixel accuracy (MPA) of 88.06% and 92.37%, respectively. The proposed method in this study lays a solid foundation for accurate detection and intelligent management of weeds. It will have a positive impact on the development of smart agriculture. © 2024 Society of Chemical Industry.

Enhancing in-season yield forecast accuracy for film-mulched wheat: A hybrid approach coupling crop model and UAV remote-sensing data by ensemble learning technique

• Introduced a semi-automated annotation workflow for efficient large-scale UAV labeling. • Proposed a lightweight hybrid segmentation framework integrating Mamba, Transformer, and CNN. • Achieved a superior balance between segmentation accuracy and computational efficiency. • Demonstrated strong performance and generalizability across diverse landscapes. • Enabled scalable regional mapping and monitoring of Acacia tortilis from UAV images. Acacia tortilis (Umbrella thorn acacia), native to the dry and semi-arid zones of Africa and the Middle East, can withstand extreme climatic conditions and provides substantial ecological and socioeconomic benefits. Reliable, regional-scale mapping and monitoring are increasingly critical as populations face mounting environmental and anthropogenic pressures. This study presents an efficient and lightweight deep learning framework for the regional-scale delineation of A. tortilis from ultra-high spatial resolution unmanned aerial vehicle (UAV) images. A comprehensive field campaign was conducted to collect ground-truth samples across diverse urban, agricultural, and mountainous settings. Aiming to expedite the labor-intensive labeling process and generate consistent, reliable training data, a semi-automated annotation workflow was developed, expanding the annotated dataset nearly fourfold to approximately 36,800 A. tortilis instances. The study also introduces a hybrid U-shaped Mamba-transformer architecture, designed to maximize the advantages of Mamba units, Transformers, and convolutional networks for joint modeling of local detail and extensive contextual information. The proposed framework was evaluated against several representative CNN–, Transformer-, and Mamba-based models, as well as alternative decoder designs, to assess segmentation quality, efficiency, and generalizability. Using an independent testing dataset, the hybrid model family (tiny, small, and base variants) demonstrated a strong balance between accuracy and efficiency, achieving mean intersection over union values ranging from 85.30% to 85.44% and mean F-scores between 91.52% and 91.61%, maintaining consistent performance across geographically distinct regions. The lightweight CNN-based decoder offered an optimal balance between accuracy and computational cost, with substantially reduced training time compared to more complex decoders. Across an extensive regional survey extent (∼250 km 2 ), the framework consistently delineated A. tortilis crowns, underscoring robust and scalable performance. Thus, the proposed framework can facilitate systematic monitoring of A. tortilis and support the conservation, restoration, and sustainable management of native tree species across arid and semi-arid ecosystems.

In recent years, research on Unmanned Aerial Vehicles (UAVs) has developed rapidly. Compared to traditional remote-sensing images, UAV images exhibit complex backgrounds, high resolution, and large differences in object scales. Therefore, UAV object detection is an essential yet challenging task. This paper proposes a multi-scale object detection network, namely YOLO-DroneMS (You Only Look Once for Drone Multi-Scale Object), for UAV images. Targeting the pivotal connection between the backbone and neck, the Large Separable Kernel Attention (LSKA) mechanism is adopted with the Spatial Pyramid Pooling Factor (SPPF), where weighted processing of multi-scale feature maps is performed to focus more on features. And Attentional Scale Sequence Fusion DySample (ASF-DySample) is introduced to perform attention scale sequence fusion and dynamic upsampling to conserve resources. Then, the faster cross-stage partial network bottleneck with two convolutions (named C2f) in the backbone is optimized using the Inverted Residual Mobile Block and Dilated Reparam Block (iRMB-DRB), which balances the advantages of dynamic global modeling and static local information fusion. This optimization effectively increases the model’s receptive field, enhancing its capability for downstream tasks. By replacing the original CIoU with WIoUv3, the model prioritizes anchoring boxes of superior quality, dynamically adjusting weights to enhance detection performance for small objects. Experimental findings on the VisDrone2019 dataset demonstrate that at an Intersection over Union (IoU) of 0.5, YOLO-DroneMS achieves a 3.6% increase in mAP@50 compared to the YOLOv8n model. Moreover, YOLO-DroneMS exhibits improved detection speed, increasing the number of frames per second (FPS) from 78.7 to 83.3. The enhanced model supports diverse target scales and achieves high recognition rates, making it well-suited for drone-based object detection tasks, particularly in scenarios involving multiple object clusters.

Accurate object detection in unmanned aerial vehicle (UAV) images plays a crucial role in fields such as aviation, transportation, and agriculture. However, UAV images often contain a high proportion of small objects, and the limited resources of UAV platforms make it challenging for existing small object detection algorithms to balance detection performance and resource consumption. To address these issues, a lightweight small object detection algorithm called SRP-YOLO is proposed. Compared to the YOLOv8 network, the following improvements are made: first, a high-resolution detection head is designed; second, a receptive field attention convolution module tailored for small UAV objects is introduced to replace the standard convolution module, enhancing the detection capability for small objects and significantly improving their feature representation; Third, partial convolution is combined with C2f to replace some of the standard convolutions, fully leveraging the high-resolution information from shallow features. Finally, A normalized Gaussian Wasserstein Distance (NWD) metric is introduced to reduce the sensitivity of Intersection over Union (IoU) to minor positional deviations of small objects. On VisDrone-DET2019, SRP-YOLO reduces parameters while improving mAP 0.5 by 7.9%. Generalization tests on TinypersonV2 and RAIVD confirm significant gains in small object detection.