Spectral – spatial urban target detection for hyperspectral remote sensing data using artificial neural network

Gakhar, Shalini; Tiwari, K. C.

doi:10.1016/j.ejrs.2021.01.002

Cited by 13 publications

(4 citation statements)

References 16 publications

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“…Hu Gensheng et al [8] used a dual-spectral camera installed on a UAV platform to obtain images of pine forests and then used a weighted support vector data description algorithm to identify diseased trees. Aimed at the problem that the traditional aerial photo identification method cannot quickly locate the pest outbreak center and track the spread of the disaster, Sun Yu et al [9] proposed a realtime monitoring method based on deep learning. The depthwise separable convolutional network as a feature extractor achieves an average precision of 97.22% during testing.…”

Section: Introductionmentioning

confidence: 99%

Detection of Pine Wilt Nematode from Drone Images Using UAV

Sun

Ibrayim

Hamdulla

2022

Sensors

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Pine wilt nematode disease is a devastating forest disease that spreads rapidly. Using drone remote sensing to monitor pine wilt nematode trees promptly is an effective way to control the spread of pine wilt nematode disease. In this study, the YOLOv4 algorithm was used to automatically identify abnormally discolored wilt from pine wilt nematode disease on UAV remote sensing images. Because the network structure of YOLOv4 is too complex, although the detection accuracy is high, the detection speed is relatively low. To solve this problem, the lightweight deep learning network MobileNetv2 is used to optimize the backbone feature extraction network. Furthermore, the YOLOv4 algorithm was improved by improving the backbone network part, adding CBAM attention, and adding the Inceptionv2 structure to reduce the number of model parameters and improve the accuracy and efficiency of identification. The speed and accuracy of the Faster R-CNN, YOLOv4, SSD, YOLOv5, and the improved MobileNetv2-YOLOv4 algorithm were compared, and the detection effects of the Faster R-CNN, YOLOv4, SSD, YOLOv5 and the improved MobileNetv2-YOLOv4 algorithm on trees with pine wilt nematode were analyzed. The experimental results show that the average precision of the improved MobileNetv2-YOLOv4 algorithm is 86.85%, the training time of each iteration cycle is 156 s, the parameter size is 39.23 MB, and the test time of a single image is 15 ms, which is better than Faster R-CNN, YOLOv4, and SSD, but comparable to YOLOv5. Compared with the advantages and disadvantages, comprehensively comparing these four indicators, the improved algorithm has a more balanced performance in the detection speed, the parameter size, and the average precision. The F1 score of the improved algorithm (95.60%) was higher than that of Faster R-CNN (90.80%), YOLOv4 (94.56%), and SSD (92.14%), which met the monitoring requirements of pine wilt nematode trees. Faster R-CNN and SSD pine-wilt-nematode tree detection models are not ideal in practical applications. Compared with the YOLOv4 pine-wilt-nematode tree detection model, the improved MobileNetv2-YOLOv4 algorithm satisfies the condition of maintaining a lower model parameter quantity to obtain higher detection accuracy; therefore, it is more suitable for practical application scenarios of embedded devices. It can be used for the rapid detection of pine wilt nematode diseased trees.

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Section: Introductionmentioning

confidence: 99%

Detection of Pine Wilt Nematode from Drone Images Using UAV

Sun

Ibrayim

Hamdulla

2022

Sensors

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“…Target detection in aerial remote-sensing images aims to identify and determine the position and type of specific objects contained in the remote-sensing images. With the rapid development of drone and satellite technologies, it has been widely applied in both military and civilian sectors, playing a crucial role in various aspects, such as environmental monitoring [1], urban planning [2], agricultural management [3], and disaster response [4]. As depicted in Figure 1, aerial remote-sensing images have many features, such as overhead imaging, significant changes in object size, and many small targets.…”

Section: Introductionmentioning

confidence: 99%

RN-YOLO: A Small Target Detection Model for Aerial Remote-Sensing Images

Wang,

Zhou,

et al. 2024

Electronics

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Accurately detecting targets in remote-sensing images is crucial for the military, urban planning, and resource exploration. There are some challenges in extracting detailed features from remote-sensing images, such as complex backgrounds, large-scale variations, and numerous small targets. This paper proposes a remote-sensing target detection model called RN-YOLO (YOLO with RepGhost and NAM), which integrates RepGhost and a normalization-based attention module (NAM) based on YOLOv8. Firstly, NAM is added to the feature extraction network to enhance the capture capabilities for small targets by recalibrating receptive fields and strengthening information flow. Secondly, an efficient RepGhost_C2f structure is employed in the feature fusion network to replace the C2f module, effectively reducing the parameters. Lastly, the WIoU (Wise Intersection over Union) loss function is adopted to mitigate issues such as significant variations in target sizes and difficulty locating small targets, effectively improving the localization accuracy of small targets. The experimental results demonstrate that compared to the YOLOv8s model, the RN-YOLO model reduces the parameter count by 13.9%. Moreover, on the DOTAv1.5, TGRS-HRRSD, and RSOD datasets, the detection accuracy (mAP@.5:.95) of the RN-YOLO model improves by 3.6%, 1.2%, and 2%, respectively, compared to the YOLOv8s model, showcasing its outstanding performance and enhanced capability in detecting small targets.

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“…The use of machine learning applications in vegetation mapping has been on the rise in recent years [24][25][26], driven by the availability of large hyperspectral datasets and the development of advanced algorithms and computational tools. Machine learning techniques, such as artificial neural networks [27][28][29], decision trees [30,31], support vector machines [32], and random forests [33,34], have shown promising results in identifying and classifying vegetation cover from hyperspectral images, offering a more robust, reliable, and cost-effective solution for remote sensing applications.…”

Section: Introductionmentioning

confidence: 99%

Vegetation Identification in Hyperspectral Images Using Distance/Correlation Metrics

2023

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Distance/correlation metrics have emerged as a robust and simplified tool for assessing the spectral characteristics of hyperspectral image pixels and effectively categorizing vegetation within a specific study area. Correlation methods provide a readily deployable and computationally efficient approach, rendering them particularly advantageous for applications in developing nations or regions with limited resources. This article presents a comparative investigation of correlation/distance metrics for the identification of vegetation pixels in hyperspectral imagery. The study facilitates a comprehensive evaluation of five distance and/or correlation metrics, namely, direct correlation, cosine similarity, normalized Euclidean distance, Bray–Curtis distance, and Pearson correlation. Direct correlation and Pearson correlation emerged as the two metrics that demonstrated the highest accuracy in vegetation pixel identification. Using the selected methodologies, a vegetation detection algorithm was implemented and validated using a hyperspectral image of the Manga neighborhood in Cartagena de Indias, Colombia. The spectral library facilitated image processing, while the mathematical calculation of correlations was performed using the numpy and scipy libraries in the Python programming language. Both the approach adopted in this study and the implemented algorithm aim to serve as a point of reference for conducting detection studies on diverse material types in hyperspectral imagery using open-access programming platforms.

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Spectral – spatial urban target detection for hyperspectral remote sensing data using artificial neural network

Cited by 13 publications

References 16 publications

Detection of Pine Wilt Nematode from Drone Images Using UAV

Detection of Pine Wilt Nematode from Drone Images Using UAV

RN-YOLO: A Small Target Detection Model for Aerial Remote-Sensing Images

Vegetation Identification in Hyperspectral Images Using Distance/Correlation Metrics

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