A Hierarchical Airport Detection Method Using Spatial Analysis and Deep Learning

Zeng, Fanhao; Cheng, Liang; Li, Ning; Xia, Nan; Ma, Lei; Zhou, Xiao; Li, Manchun

doi:10.3390/rs11192204

Cited by 27 publications

(31 citation statements)

References 60 publications

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“…The supervised DL model commonly requires a greater number of training data and it has more layers and depth than ML [ 75 ]. DL can be used in monitoring the growth conditions of maize and yield predictions as it can obtain higher precision [ 76 ]. Thus, integrated ML, DL, and crop models for data assimilation would attract more attention as they will combine the advantages of mechanistic crop models and the advantages of non-mechanical relationships.…”

Section: Discussionmentioning

confidence: 99%

Modified Red Blue Vegetation Index for Chlorophyll Estimation and Yield Prediction of Maize from Visible Images Captured by UAV

Guo

Wang

et al. 2020

Sensors

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The vegetation index (VI) has been successfully used to monitor the growth and to predict the yield of agricultural crops. In this paper, a long-term observation was conducted for the yield prediction of maize using an unmanned aerial vehicle (UAV) and estimations of chlorophyll contents using SPAD-502. A new vegetation index termed as modified red blue VI (MRBVI) was developed to monitor the growth and to predict the yields of maize by establishing relationships between MRBVI- and SPAD-502-based chlorophyll contents. The coefficients of determination (R2s) were 0.462 and 0.570 in chlorophyll contents’ estimations and yield predictions using MRBVI, and the results were relatively better than the results from the seven other commonly used VI approaches. All VIs during the different growth stages of maize were calculated and compared with the measured values of chlorophyll contents directly, and the relative error (RE) of MRBVI is the lowest at 0.355. Further, machine learning (ML) methods such as the backpropagation neural network model (BP), support vector machine (SVM), random forest (RF), and extreme learning machine (ELM) were adopted for predicting the yields of maize. All VIs calculated for each image captured during important phenological stages of maize were set as independent variables and the corresponding yields of each plot were defined as dependent variables. The ML models used the leave one out method (LOO), where the root mean square errors (RMSEs) were 2.157, 1.099, 1.146, and 1.698 (g/hundred grain weight) for BP, SVM, RF, and ELM. The mean absolute errors (MAEs) were 1.739, 0.886, 0.925, and 1.356 (g/hundred grain weight) for BP, SVM, RF, and ELM, respectively. Thus, the SVM method performed better in predicting the yields of maize than the other ML methods. Therefore, it is strongly suggested that the MRBVI calculated from images acquired at different growth stages integrated with advanced ML methods should be used for agricultural- and ecological-related chlorophyll estimation and yield predictions.

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Section: Discussionmentioning

confidence: 99%

Modified Red Blue Vegetation Index for Chlorophyll Estimation and Yield Prediction of Maize from Visible Images Captured by UAV

Guo

Wang

et al. 2020

Sensors

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“…It accomplished airport detection from remote sensing images with complex background information, but the model was often overfit due to insufficient samples. Fan et al [26] proposed a layered airport detection algorithm based on spatial analysis and Faster R-CNN to achieve large-scale airport detection from optical remote sensing images. Li et al [27] built an end-to-end airport detection model from remote sensing images based on a deep transferable convolutional neural network, which overcame the shortcomings of traditional CNN models for airport detection under complex backgrounds.…”

Section: State-of-the-artmentioning

confidence: 99%

A New Framework for Automatic Airports Extraction from SAR Images Using Multi-Level Dual Attention Mechanism

et al. 2020

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The detection of airports from Synthetic Aperture Radar (SAR) images is of great significance in various research fields. However, it is challenging to distinguish the airport from surrounding objects in SAR images. In this paper, a new framework, multi-level and densely dual attention (MDDA) network is proposed to extract airport runway areas (runways, taxiways, and parking lots) in SAR images to achieve automatic airport detection. The framework consists of three parts: down-sampling of original SAR images, MDDA network for feature extraction and classification, and up-sampling of airports extraction results. First, down-sampling is employed to obtain a medium-resolution SAR image from the high-resolution SAR images to ensure the samples (500 × 500) can contain adequate information about airports. The dataset is then input to the MDDA network, which contains an encoder and a decoder. The encoder uses ResNet_101 to extract four-level features with different resolutions, and the decoder performs fusion and further feature extraction on these features. The decoder integrates the chained residual pooling network (CRP_Net) and the dual attention fusion and extraction (DAFE) module. The CRP_Net module mainly uses chained residual pooling and multi-feature fusion to extract advanced semantic features. In the DAFE module, position attention module (PAM) and channel attention mechanism (CAM) are combined with weighted filtering. The entire decoding network is constructed in a densely connected manner to enhance the gradient transmission among features and take full advantage of them. Finally, the airport results extracted by the decoding network were up-sampled by bilinear interpolation to accomplish airport extraction from high-resolution SAR images. To verify the proposed framework, experiments were performed using Gaofen-3 SAR images with 1 m resolution, and three different airports were selected for accuracy evaluation. The results showed that the mean pixels accuracy (MPA) and mean intersection over union (MIoU) of the MDDA network was 0.98 and 0.97, respectively, which is much higher than RefineNet and DeepLabV3. Therefore, MDDA can achieve automatic airport extraction from high-resolution SAR images with satisfying accuracy.

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“…The methods for aircraft target recognition mainly employ template matching [23,24] and model-based learning [25][26][27]. These methods have achieved some good results, but also have limitations of ow precision and long runtime [28]. There is a strong correlation among these three steps, as the results of each step will directly affect the next step.…”

Section: A Aircraft Detection From Rsismentioning

confidence: 99%

Convolutional Neural Network Based Weakly Supervised Learning for Aircraft Detection From Remote Sensing Image

Weise

Wang

et al. 2020

IEEE Access

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Object detection methods based on Convolutional Neural Networks (CNNs) require a large number of images with annotation information to train. In aircraft detection from remote sensing images (RSIs), aircraft targets are usually small and the cost of manual annotation is very high. In this paper, we tackle the problem of weakly supervised aircraft detection from RSIs, which aims to learn detectors with only image-level annotations, i.e., without bounding-box labeled data during the training stage. Based on the fact that the feature maps learned from the CNN network are localizable, we propose a simple yet efficient aircraft detection algorithm called Weakly Supervised Learning in AlexNet (AlexNet-WSL). In AlexNet-WSL, we utilize the AlexNet CNN as backbone network, but replace the last two fully connected layers with a Global Average Pooling (GAP) and two convolutional layers. Based on the class activation maps, we generate heat maps via reverse weighting for locating the target object. Unlike object detection methods that require object location data for training, our proposal only needs image-level labelled data. We furthermore build a set of remote sensing aircraft images, the Weakly Supervised Aircraft Detection Dataset (WSADD) for algorithm benchmarking. The experimental results on the WSADD show that AlexNet-WSL effectively detects the aircraft and achieves a detection effect equivalent to the Faster R-CNN method and the YOLOv3 method, which both require bounding-box labelled data for training, with a lower false alarm rate and a shorter training time.

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A Hierarchical Airport Detection Method Using Spatial Analysis and Deep Learning

Cited by 27 publications

References 60 publications

Modified Red Blue Vegetation Index for Chlorophyll Estimation and Yield Prediction of Maize from Visible Images Captured by UAV

Modified Red Blue Vegetation Index for Chlorophyll Estimation and Yield Prediction of Maize from Visible Images Captured by UAV

A New Framework for Automatic Airports Extraction from SAR Images Using Multi-Level Dual Attention Mechanism

Convolutional Neural Network Based Weakly Supervised Learning for Aircraft Detection From Remote Sensing Image

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