A critical problem associated with the southern part of Nigeria is the rapid alteration of the landscape as a result of logging, agricultural practices, human migration and expansion, oil exploration, exploitation and...A critical problem associated with the southern part of Nigeria is the rapid alteration of the landscape as a result of logging, agricultural practices, human migration and expansion, oil exploration, exploitation and production activities. These processes have had both positive and negative effects on the economic and socio-political development of the country in general. The negative impacts have led not only to the degradation of the ecosystem but also posing hazards to human health and polluting surface and ground water resources. This has created the need for the development of a rapid, cost effective and efficient land use/land cover (LULC) classification technique to monitor the biophysical dynamics in the region. Due to the complex land cover patterns existing in the study area and the occasionally indistinguishable relationship between land cover and spectral signals, this paper introduces a combined use of unsupervised and supervised image classification for detecting land use/land cover (LULC) classes. With the continuous conflict over the impact of oil activities in the area, this work provides a procedure for detecting LULC change, which is an important factor to consider in the design of an environmental decision-making framework. Results from the use of this technique on Landsat TM and ETM+ of 1987 and 2002 are discussed. The results reveal the pros and cons of the two methods and the effects of their overall accuracy on post-classification change detection.展开更多
This paper proposed a new approach of sample part classification and design, a so called Or-dered-object-oriented method (O-O-O method). Based on the theory of neural networks, fuzzy clustering algorithm and adaptive ...This paper proposed a new approach of sample part classification and design, a so called Or-dered-object-oriented method (O-O-O method). Based on the theory of neural networks, fuzzy clustering algorithm and adaptive pattern recognition, O-O-O method can be used to classify and design the sample parts automatically. The basic theory, the main step as well as the characteristics of the method are analysed. The construction of the ordered object in application is also presented in this paper.展开更多
机场飞行区现使用的场面监视方法存在着定位偏差较大、不稳定、易跳变、皆为点源定位等问题。针对这些问题,设计了基于视觉图像的飞行区监视方法,实现快速准确的目标检测和轮廓定位,使飞行区监视更加稳定精确。提出了一种基于MobileNetV...机场飞行区现使用的场面监视方法存在着定位偏差较大、不稳定、易跳变、皆为点源定位等问题。针对这些问题,设计了基于视觉图像的飞行区监视方法,实现快速准确的目标检测和轮廓定位,使飞行区监视更加稳定精确。提出了一种基于MobileNetV3和YOLOv5的网络模型(以下称为MobileNetV3-YOLOv5),即在YOLOv5的主干中使用MobileNetV3,来提高对目标的检测速度和准确度;提出了一种基于优化特征点提取的改进定向快速旋转简报(Oriented FAST and Rotated BRIEF,ORB)算法,将图像分割成多个区域,分别提取每个区域的特征点,从而提高目标识别框内区域的特征点识别数量,再进行特征点聚类筛选,最后根据识别目标类型采用最小包围盒进行轮廓划分,得到目标的轮廓定位。试验结果表明:MobileNetV3-YOLOv5方法对比原始YOLOv5模型,在识别目标准确率方面提升5百分点,在效率方面提升14张/s;同时在0~60 m的范围内,轮廓估计误差仅为2.9%;体现了所提出的监视方法的有效性,可以提升飞行区监视定位准确性和运行安全性。展开更多
文摘A critical problem associated with the southern part of Nigeria is the rapid alteration of the landscape as a result of logging, agricultural practices, human migration and expansion, oil exploration, exploitation and production activities. These processes have had both positive and negative effects on the economic and socio-political development of the country in general. The negative impacts have led not only to the degradation of the ecosystem but also posing hazards to human health and polluting surface and ground water resources. This has created the need for the development of a rapid, cost effective and efficient land use/land cover (LULC) classification technique to monitor the biophysical dynamics in the region. Due to the complex land cover patterns existing in the study area and the occasionally indistinguishable relationship between land cover and spectral signals, this paper introduces a combined use of unsupervised and supervised image classification for detecting land use/land cover (LULC) classes. With the continuous conflict over the impact of oil activities in the area, this work provides a procedure for detecting LULC change, which is an important factor to consider in the design of an environmental decision-making framework. Results from the use of this technique on Landsat TM and ETM+ of 1987 and 2002 are discussed. The results reveal the pros and cons of the two methods and the effects of their overall accuracy on post-classification change detection.
文摘This paper proposed a new approach of sample part classification and design, a so called Or-dered-object-oriented method (O-O-O method). Based on the theory of neural networks, fuzzy clustering algorithm and adaptive pattern recognition, O-O-O method can be used to classify and design the sample parts automatically. The basic theory, the main step as well as the characteristics of the method are analysed. The construction of the ordered object in application is also presented in this paper.
文摘机场飞行区现使用的场面监视方法存在着定位偏差较大、不稳定、易跳变、皆为点源定位等问题。针对这些问题,设计了基于视觉图像的飞行区监视方法,实现快速准确的目标检测和轮廓定位,使飞行区监视更加稳定精确。提出了一种基于MobileNetV3和YOLOv5的网络模型(以下称为MobileNetV3-YOLOv5),即在YOLOv5的主干中使用MobileNetV3,来提高对目标的检测速度和准确度;提出了一种基于优化特征点提取的改进定向快速旋转简报(Oriented FAST and Rotated BRIEF,ORB)算法,将图像分割成多个区域,分别提取每个区域的特征点,从而提高目标识别框内区域的特征点识别数量,再进行特征点聚类筛选,最后根据识别目标类型采用最小包围盒进行轮廓划分,得到目标的轮廓定位。试验结果表明:MobileNetV3-YOLOv5方法对比原始YOLOv5模型,在识别目标准确率方面提升5百分点,在效率方面提升14张/s;同时在0~60 m的范围内,轮廓估计误差仅为2.9%;体现了所提出的监视方法的有效性,可以提升飞行区监视定位准确性和运行安全性。
