Low Altitude Remote Sensing Research Articles

The Construction and Practice of Big Data Mapping GIS under Low Altitude Remote Sensing

This paper introduces the construction and practice of big data mapping geographic information system under low-altitude remote sensing. The combination of low-altitude remote sensing technology, which obtains high-resolution remote sensing data through unmanned aerial vehicles and satellites, and big data technology, which can process and analyze huge geographic information data, provides new ideas and methods for the development of geographic information systems. Starting from an overview of low-altitude remote sensing technology and the advantages of its application, the article discusses the process of building a big data mapping GIS and introduces a practical case based on the system. At the same time, the current challenges and future development trends are also put forward.

Application Progress of UAV-LARS in Identification of Crop Diseases and Pests

Disease and pest stress is one of the important threats to crop growth and development, which have a very adverse impact on crop yield and quality every year, even leading to crop failure. Currently, the use of plant protection unmanned aerial vehicles (UAVs) for pesticide spraying is the most effective means of controlling crop diseases and pests. However, the areas where crop pests and diseases occur are often presented in “point-like” or “patchy” forms, and the UAV’s full-coverage spraying method results in a great waste of pesticides. Therefore, there is an urgent need to research a method for identifying the area of diseases and pest stress to achieve precise targeted spraying, in order to reduce the amount of pesticides used and improve their utilization. By analyzing the low-altitude remote sensing images of crop fields taken by UAVs, real-time pesticide spraying prescription maps can be generated to meet the demand for precise targeted spraying. This review focuses on the actual needs of precise targeted spraying by plant protection UAVs. Firstly, the RS monitoring mechanism of crop diseases and pests by UAVs is studied. Secondly, a comprehensive investigation of the literature on UAV Low-altitude Remote Sensing (UAV-LARS) technology for monitoring and identifying crop diseases and pests is conducted, summarizing the research progress in monitoring and identifying crop diseases and pests, especially in wheat, cotton, and rice. Finally, the key issues to be addressed and the future development direction of UAV-LARS monitoring of crop diseases and pests are proposed.

UAV-Based Low Altitude Remote Sensing for Concrete Bridge Multi-Category Damage Automatic Detection System

Detecting damage in bridges can be an arduous task, fraught with challenges stemming from the limitations of the inspection environment and the considerable time and resources required for manual acquisition. Moreover, prevalent damage detection methods rely heavily on pixel-level segmentation, rendering it infeasible to classify and locate different damage types accurately. To address these issues, the present study proposes a novel fully automated concrete bridge damage detection system that harnesses the power of unmanned aerial vehicle (UAV) remote sensing technology. The proposed system employs a Swin Transformer-based backbone network, coupled with a multi-scale attention pyramid network featuring a lightweight residual global attention network (LRGA-Net), culminating in unprecedented breakthroughs in terms of speed and accuracy. Comparative analyses reveal that the proposed system outperforms commonly used target detection models, including the YOLOv5-L and YOLOX-L models. The proposed system’s robustness in visual inspection results in the real world reinforces its efficacy, ushering in a new paradigm for bridge inspection and maintenance. The study findings underscore the potential of UAV-based inspection as a means of bolstering the efficiency and accuracy of bridge damage detection, highlighting its pivotal role in ensuring the safety and longevity of vital infrastructure.

Construction Site Multi-Category Target Detection System Based on UAV Low-Altitude Remote Sensing

On-site management of construction sites has always been a significant problem faced by the construction industry. With the development of UAVs, their use to monitor construction safety and progress will make construction more intelligent. This paper proposes a multi-category target detection system based on UAV low-altitude remote sensing, aiming to solve the problems of relying on fixed-position cameras and a single category of established detection targets when mainstream target detection algorithms are applied to construction supervision. The experimental results show that the proposed method can accurately and efficiently detect 15 types of construction site targets. In terms of performance, the proposed method achieves the highest accuracy in each category compared to other networks, with a mean average precision (mAP) of 82.48%. Additionally, by applying it to the actual construction site, the proposed system is confirmed to have comprehensive detection capability and robustness.

Application of Low Altitude Remote Sensing by UAV in the Extraction of Ground Fracture Features in Coal Mining Collapse Area

Application of Low Altitude Remote Sensing by UAV in the Extraction of Ground Fracture Features in Coal Mining Collapse Area

Decoupled Feature Pyramid Learning for Multi-Scale Object Detection in Low-Altitude Remote Sensing Images

Decoupled Feature Pyramid Learning for Multi-Scale Object Detection in Low-Altitude Remote Sensing Images

Multi-SUAV Collaboration and Low-Altitude Remote Sensing Technology-Based Image Registration and Change Detection Network of Garbage Scattered Areas in Nature Reserves

Change detection is an important task in remote sensing image processing and analysis. However, due to position errors and wind interference, bi-temporal low-altitude remote sensing images collected by SUAVs often suffer from different viewing angles. The existing methods need to use an independent registration network for registration before change detection, which greatly reduces the integrity and speed of the task. In this work, we propose an end-to-end network architecture RegCD-Net to address change detection problems in the bi-temporal SUAVs’ low-altitude remote sensing images. We utilize global and local correlations to generate an optical flow pyramid and realize image registration through layer-by-layer optical flow fields. Then we use a nested connection to combine the rich semantic information in deep layers of the network and the precise location information in the shallow layers and perform deep supervision through the combined attention module to finally achieve change detection in bi-temporal images. We apply this network to the task of change detection in the garbage-scattered areas of nature reserves and establish a related dataset. Experimental results show that our RegCD-Net outperforms several state-of-the-art CD methods with more precise change edge representation, relatively few parameters, fast speed, and better integration without additional registration networks.

Detecting Wheat Heads from UAV Low-Altitude Remote Sensing Images Using Deep Learning Based on Transformer

The object detection method based on deep learning convolutional neural network (CNN) significantly improves the detection performance of wheat head on wheat images obtained from the near ground. Nevertheless, for wheat head images of different stages, high density, and overlaps captured by the aerial-scale unmanned aerial vehicle (UAV), the existing deep learning-based object detection methods often have poor detection effects. Since the receptive field of CNN is usually small, it is not conducive to capture global features. The visual Transformer can capture the global information of an image; hence we introduce Transformer to improve the detection effect and reduce the computation of the network. Three object detection networks based on Transformer are designed and developed, including the two-stage method FR-Transformer and the one-stage methods R-Transformer and Y-Transformer. Compared with various other prevalent object detection CNN methods, our FR-Transformer method outperforms them by 88.3% for AP50 and 38.5% for AP75. The experiments represent that the FR-Transformer method can gratify requirements of rapid and precise detection of wheat heads by the UAV in the field to a certain extent. These more relevant and direct information provide a reliable reference for further estimation of wheat yield.

A Matching Optimization Algorithm About Low-Altitude Remote Sensing Images Based on Geometrical Constraint and Convolutional Neural Network

This article presents a novel matching optimization algorithm for low-altitude remote sensing images based on a geometrical constraint and a convolutional neural network (CNN). The proposed method was designed to be effective in enhancing the integrity and accuracy of point clouds generated by stereo matching. To overcome the limitations of stereo matching, we trained a CNN to predict how well image patches match and used it in patch optimization. The main advantage of this approach is that the proposed algorithm can decrease the mismatching and errors caused by noise, deep discontinuity, and weak texture in low-altitude remote sensing images and can reconstruct an integrated and accurate point cloud. Comparative studies and experimental results validate the accuracy of the proposed algorithm when used for dense point generation from low-altitude remote sensing images.

Ice Velocity in Upstream of Heilongjiang Based on UAV Low-Altitude Remote Sensing and the SIFT Algorithm

In river management, it is important to obtain ice velocity quickly and accurately during ice flood periods. However, traditional ice velocity monitoring methods require buoys, which are costly and inefficient to distribute. It was found that UAV remote sensing images combined with machine vision technology yielded obvious practical advantages in ice velocity monitoring. Current research has mainly monitored sea ice velocity through GPS or satellite remote sensing technology, with few reports available on river ice velocity monitoring. Moreover, traditional river ice velocity monitoring methods are subjective. To solve the problems of existing time-consuming and inaccurate ice velocity monitoring methods, a new ice velocity extraction method based on UAV remote sensing technology is proposed in this article. In this study, the Mohe River section in Heilongjiang Province was chosen as the research area. High-resolution orthoimages were obtained with a UAV during the ice flood period, and feature points in drift ice images were then extracted with the scale-invariant feature transform (SIFT) algorithm. Moreover, the extracted feature points were matched with the brute force (BF) algorithm. According to optimization results obtained with the random sample consensus (RANSAC) algorithm, the motion trajectories of these feature points were tracked, and an ice displacement rate field was finally established. The results indicated that the average ice velocities in the research area reached 2.00 and 0.74 m/s, and the maximum ice velocities on the right side of the river center were 2.65 and 1.04 m/s at 16:00 on 25 April 2021 and 8:00 on 26 April 2021, respectively. The ice velocity decreased from the river center toward the river banks. The proposed ice velocity monitoring technique and reported data in this study could provide an effective reference for the prediction of ice flood disasters.

Real-Time Identification of Rice Weeds by UAV Low-Altitude Remote Sensing Based on Improved Semantic Segmentation Model

Real-time analysis of UAV low-altitude remote sensing images at airborne terminals facilitates the timely monitoring of weeds in the farmland. Aiming at the real-time identification of rice weeds by UAV low-altitude remote sensing, two improved identification models, MobileNetV2-UNet and FFB-BiSeNetV2, were proposed based on the semantic segmentation models U-Net and BiSeNetV2, respectively. The MobileNetV2-UNet model focuses on reducing the amount of calculation of the original model parameters, and the FFB-BiSeNetV2 model focuses on improving the segmentation accuracy of the original model. In this study, we first tested and compared the segmentation accuracy and operating efficiency of the models before and after the improvement on the computer platform, and then transplanted the improved models to the embedded hardware platform Jetson AGX Xavier, and used TensorRT to optimize the model structure to improve the inference speed. Finally, the real-time segmentation effect of the two improved models on rice weeds was further verified through the collected low-altitude remote sensing video data. The results show that on the computer platform, the MobileNetV2-UNet model reduced the amount of network parameters, model size, and floating point calculations by 89.12%, 86.16%, and 92.6%, and the inference speed also increased by 2.77 times, when compared with the U-Net model. The FFB-BiSeNetV2 model improved the segmentation accuracy compared with the BiSeNetV2 model and achieved the highest pixel accuracy and mean Intersection over Union ratio of 93.09% and 80.28%. On the embedded hardware platform, the optimized MobileNetV2-UNet model and FFB-BiSeNetV2 model inferred 45.05 FPS and 40.16 FPS for a single image under the weight accuracy of FP16, respectively, both meeting the performance requirements of real-time identification. The two methods proposed in this study realize the real-time identification of rice weeds under low-altitude remote sensing by UAV, which provide a reference for the subsequent integrated operation of plant protection drones in real-time rice weed identification and precision spraying.

Assessment of Nutrient Status of Lowland Irrigated Rice (Oryza sativa L.) using Low Altitude Remote Sensing

Low altitude remote sensing provides near real-time information on crop nutrient status with higher spatial resolution compared to airborne and spaceborne systems. In this study, an Unmanned Aerial Vehicle System (UAVS) with on-board multispectral camera was deployed to determine the normalized difference vegetation index (NDVI) of lowland irrigated rice under different fertilizer treatments. Relationship between UAV-based NDVI and ground measurements from Soil Plant Analysis Development (SPAD) chlorophyll meter at different rice growth stages were also established. The results showed that experimental plots with high nitrogen fertilization had significantly higher SPAD and NDVI values compared with plots without nitrogen fertilization. Strong correlation between SPAD readings and NDVI values (p<0.001) was also observed as early as panicle initiation stage (r=0.7372). Hence, low altitude remote sensing can be a rapid and non-destructive tool for site-specific rice nutrient management. The use of low altitude remote sensing technique as evaluated in this study offers a practical approach in spot- or field-level application of nitrogen fertilizer during panicle initiation to enhance uniform spikelet development, and during flowering (few days before or after) to promote grain filling using foliar fertilizers.

Scientific Research on Agricultural Low-Altitude Remote Sensing Platform Based On Dual CPU Structure

With the support of the Jilin Province Education Department “13th Five-Year” Science and Technology Research Project Fund (JJKH20190974KJ), this paper, relying on the Jilin Province Digital Agriculture Key Discipline, carries out the research and practice of micro low-altitude remote sensing platform and its application in agricultural situation detection. This paper describes a kind of agricultural low-altitude remote sensing platform, which adopts dual CPU structure about central control layer and FMU flight control layer. This paper describes in detail the development status of low-altitude remote sensing, key technologies, experimental implementation schemes, core system design and so on. After field test, the operation effect is good.

A Review of Unmanned Aerial Vehicle Low-Altitude Remote Sensing (UAV-LARS) Use in Agricultural Monitoring in China

Precision agriculture relies on the rapid acquisition and analysis of agricultural information. An emerging method of agricultural monitoring is unmanned aerial vehicle low-altitude remote sensing (UAV-LARS), which possesses significant advantages of simple construction, strong mobility, and high spatial-temporal resolution with synchronously obtained image and spatial information. UAV-LARS could provide a high degree of overlap between X and Y during key crop growth periods that is currently lacking in satellite and remote sensing data. Simultaneously, UAV-LARS overcomes the limitations such as small scope of ground platform monitoring. Overall, UAV-LARS has demonstrated great potential as a tool for monitoring agriculture at fine- and regional-scales. Here, we systematically summarize the history and current application of UAV-LARS in Chinese agriculture. Specifically, we outline the technical characteristics and sensor payload of the available types of unmanned aerial vehicles and discuss their advantages and limitations. Finally, we provide suggestions for overcoming current limitations of UAV-LARS and directions for future work.

Automatic Detection of Scattered Garbage Regions Using Small Unmanned Aerial Vehicle Low-Altitude Remote Sensing Images for High-Altitude Natural Reserve Environmental Protection.

Recently, some famous high-altitude nature reserves have been shut down due to tourist garbage pollution. In order to clean up such garbage more conveniently and quickly, a novel detection framework is proposed to automatically detect scattered garbage regions using low-altitude remote sensing of small unmanned aerial vehicles (SUAVs), and it contains the following steps. First, high-resolution, low-altitude, multitemporal remote sensing images containing scattered garbage regions are collected by SUAVs, and two data augmentation methods are proposed to expand the training samples. Second, low-altitude remote sensing image registration and target-level image change detection are used to extract the candidate regions of garbage. Finally, a deep learning detection network is adopted to classify the scattered garbage regions. Experimental results show that the proposed detection framework achieves a mean accuracy of 96.94% and provides better performances on the real dataset compared with state-of-the-art methods.

Utilization of Low Altitude Remote Sensing to Estimate Canopy Growth and Determine Proper Amount of Top-dressed Nitrogen in a Rice Cultivar “Yamadanishiki” for Sake Brewing

Utilization of Low Altitude Remote Sensing to Estimate Canopy Growth and Determine Proper Amount of Top-dressed Nitrogen in a Rice Cultivar “Yamadanishiki” for Sake Brewing

Assessing the activity of deer and their influence on vegetation in a wetland using automatic cameras and low altitude remote sensing (LARS)

It was thought by the author of the present study that if both automatic cameras and LARS were used, instead of one or the other, then it would be possible to efficiently gain knowledge that could be applied in the strategic management of deer in wetlands. In the Mizorogaike wetland, deer (Cervus nippon) graze plants throughout its entire area. Aerial images taken in the winter were used in the deer trail survey, as the vegetation would be withered, and the deer trails become easier to identify. The rare species selected to evaluate the effect of deer on vegetation was Menyanthes trifoliata, which was reported to be on the decline in the Mizorogaike wetland. The deer trails were identified visually. M. trifoliata was detected in the images using deep learning. I investigated the relationship between increases and decreases in areas covered by M. trifoliata during the period from 2016 to 2019 and the distance of these areas from deer trails. Three routes were selected where the deer trails indicated that the deer might be crossing the water. Four camera traps were set up on the land side facing the water. In the Mizorogaike wetland, during the period from 2015 to 2019, the area where deer trails could be observed spread to every corner of the wetland. Deer trails became conspicuous at the southwest edge of the Mizorogaike wetland. Although in 2015 the deer trails were limited to the northern half of the floating mat, by 2019, they had spread down into the southern half. Although camera traps were only used to investigate the wetland in 2019, it is thought that deer began to regularly swim across the water during the period from 2015 to 2019 and that this led to trails forming in the south of the floating mat. The area covered by M. trifoliata decreased by 20% from 2016 to 2019. The areas where M. trifoliata cover decreased tended to be nearer to deer trails than the areas where it increased.

Monitoring Hybrid Rice Phenology at Initial Heading Stage Based on Low-Altitude Remote Sensing Data

Accurate monitoring of hybrid rice phenology (RP) is crucial for breeding rice cultivars and controlling fertilizing amount. The aim of this study is to monitor the exact date of hybrid rice initial heading stage (IHSDAS) based on low-altitude remote sensing data and analyze the influence factors of RP. In this study, six field experiments were conducted in Ezhou city and Lingshui city from 2016 to 2019, which involved different rice cultivars and nitrogen rates. Three low-altitude remote sensing platforms were used to collect rice canopy reflectance. Firstly, we compared the performance of normalized difference vegetation index (NDVI) and red edge chlorophyll index (CIred edge) for monitoring RP. Secondly, double logistic function (DLF), asymmetric gauss function (AGF), and symmetric gauss function (SGF) were used to fit time-series CIred edge for acquiring phenological curves (PC), the feature: maximum curvature (MC) of PC was extracted to monitor IHSDAS. Finally, we analyzed the influence of rice cultivars, N rates, and air temperature on RP. The results indicated that CIred edge was more appropriate than NDVI for monitoring RP without saturation problem. Compared with DLF and AGF, SGF could fit CIred edge without over fitting problem. MC of SGF_CIred edge from all three platforms showed good performance in monitoring IHSDAS with good robustness, R2 varied between 0.82 and 0.95, RMSE ranged from 2.31 to 3.81. In addition, the results demonstrated that high air temperature might cause a decrease of IHSDAS, and the growth process of rice was delayed when more nitrogen fertilizer was applied before IHSDAS. This study illustrated that low-altitude remote sensing technology could be used for monitoring field-scale hybrid rice IHSDAS accurately.

Automatic Garbage Scattered Area Detection with Data Augmentation and Transfer Learning in SUAV Low-Altitude Remote Sensing Images

Cleaning up the garbage timely plays an important role in protecting the ecological environment of nature reserves. The traditional approach adopts manual patrol and centralized cleaning to clean up garbage, which is inefficient. In order to protect the ecological environment of nature reserves, this paper proposes an automatic garbage scattered area detection (GSAD) model based on the state-of-the-art deep learning EfficientDet method, transfer learning, data augmentation, and image blocking. The main contributions of this paper are (1) we build a garbage sample dataset based on small unmanned aerial vehicle (SUAV) low-altitude remote sensing and (2) we propose a novel data augmentation approach based on garbage scattered area detection and (3) this paper establishes a model (GSAD) for garbage scattered area detection based on data augmentation, transfer learning, and image blocking and gives future research directions. Experimental results show that the GSAD model can achieve the F1-score of 95.11% and average detection time of 1.096 s.

A Multi-Modal Approach for Crop Health Mapping Using Low Altitude Remote Sensing, Internet of Things (IoT) and Machine Learning

The agriculture sector holds paramount importance in Pakistan due to the intrinsic agrarian nature of the economy. Pakistan has its GDP based on agriculture, however it relies on manual monitoring of crops, which is a labour intensive and ineffective method. In contrast to this, several cutting edge technology-based solutions are being employed in the developed countries to enhance the crop yield with the optimal use of resources. To this end, we have proposed an integrated approach for monitoring crop health using IoT, machine learning and drone technology. The integration of these sensing modalities generate heterogeneous data which not only varies in nature (i.e. observed parameter) but also has different temporal fidelity. The spatial resolution of these methods is also different, hence, the optimal integration of these sensing modalities and their implementation in practice are addressed in the proposed system. In our proposed solution, the IoT sensors provide the real-time status of environmental parameters impacting the crop, and the drone platform provide the multispectral data used for generating Vegetation Indices (VIs) such as Normalized Difference vegetation Index (NDVI) for analyzing the crop health. The NDVI provides information about the crop based on the chlorophyll content, which offers limited information regarding the crop health. In order to obtain a rich and detailed knowledge about crop health, the variable length time series data of IoT sensors and multispectral images were converted to a fixed-sized representation to generate crop health maps. A number of machine and deep learning algorithms were applied on the collected data wherein deep neural network with two hidden layers was found to be the most optimal model among all the selected models, providing an accuracy of (98.4%). Further, the health maps were validated through ground surveys and by agriculture experts due to the absence of reference data. The proposed research is basically an indigenous, technology based agriculture solution capable of providing important insights into the crop health by extracting complementary features from multi-modal data set, and minimizing the crop ground survey effort, particularly useful when the agriculture land is large in size.