School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban 4041, South Africa.
School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg 3209, South Africa.
Sensors (Basel). 2023 Jun 23;23(13):5849. doi: 10.3390/s23135849.
Object detection in remotely sensed satellite images is critical to socio-economic, bio-physical, and environmental monitoring, necessary for the prevention of natural disasters such as flooding and fires, socio-economic service delivery, and general urban and rural planning and management. Whereas deep learning approaches have recently gained popularity in remotely sensed image analysis, they have been unable to efficiently detect image objects due to complex landscape heterogeneity, high inter-class similarity and intra-class diversity, and difficulty in acquiring suitable training data that represents the complexities, among others. To address these challenges, this study employed multi-object detection deep learning algorithms with a transfer learning approach on remotely sensed satellite imagery captured on a heterogeneous landscape. In the study, a new dataset of diverse features with five object classes collected from Google Earth Engine in various locations in southern KwaZulu-Natal province in South Africa was used to evaluate the models. The dataset images were characterized with objects that have varying sizes and resolutions. Five (5) object detection methods based on R-CNN and YOLO architectures were investigated via experiments on our newly created dataset. This paper provides a comprehensive performance evaluation and analysis of the recent deep learning-based object detection methods for detecting objects in high-resolution remote sensing satellite images. The models were also evaluated on two publicly available datasets: Visdron and PASCAL VOC2007. Results showed that the highest detection accuracy of the vegetation and swimming pool instances was more than 90%, and the fastest detection speed 0.2 ms was observed in YOLOv8.
遥感卫星图像中的目标检测对于社会经济、生物物理和环境监测至关重要，对于预防洪水和火灾等自然灾害、提供社会经济服务以及进行一般的城乡规划和管理是必要的。尽管深度学习方法最近在遥感图像分析中得到了广泛应用，但由于复杂的景观异质性、高类间相似性和类内多样性以及难以获取适合代表复杂性等特点的训练数据，它们无法有效地检测图像中的目标。为了解决这些挑战，本研究在遥感卫星图像上采用了多目标检测深度学习算法和迁移学习方法，该图像是在异质景观上捕获的。在研究中，使用了来自南非夸祖鲁-纳塔尔省南部不同地点的谷歌地球引擎收集的具有五个目标类别的不同特征的新数据集来评估模型。该数据集图像的特点是具有不同大小和分辨率的目标。通过在我们新创建的数据集上进行实验，研究了五种基于 R-CNN 和 YOLO 架构的目标检测方法。本文对用于检测高分辨率遥感卫星图像中目标的最新基于深度学习的目标检测方法进行了全面的性能评估和分析。这些模型还在两个公开可用的数据集 Visdron 和 PASCAL VOC2007 上进行了评估。结果表明，植被和游泳池实例的最高检测准确率超过 90%，而 YOLOv8 的最快检测速度为 0.2ms。
