scholarly journals Multi-Component Fusion Network for Small Object Detection in Remote Sensing Images

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 128339-128352 ◽  
Author(s):  
Jing Liu ◽  
Shuojin Yang ◽  
Liang Tian ◽  
Wei Guo ◽  
Bingyin Zhou ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Small Object ◽  
Remote Sensing Images ◽  
Small Object Detection

scholarly journals Small Object Detection in Remote Sensing Images Based on Super-Resolution with Auxiliary Generative Adversarial Networks

2020 ◽  
Vol 12 (19) ◽  
pp. 3152
Author(s):  
Luc Courtrai ◽  
Minh-Tan Pham ◽  
Sébastien Lefèvre
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Network Architecture ◽  
Super Resolution ◽  
Remote Sensing Data ◽  
Generative Adversarial Networks ◽  
Small Object ◽  
Remote Sensing Images ◽  
Generative Adversarial Network ◽  
Small Object Detection

This article tackles the problem of detecting small objects in satellite or aerial remote sensing images by relying on super-resolution to increase image spatial resolution, thus the size and details of objects to be detected. We show how to improve the super-resolution framework starting from the learning of a generative adversarial network (GAN) based on residual blocks and then its integration into a cycle model. Furthermore, by adding to the framework an auxiliary network tailored for object detection, we considerably improve the learning and the quality of our final super-resolution architecture, and more importantly increase the object detection performance. Besides the improvement dedicated to the network architecture, we also focus on the training of super-resolution on target objects, leading to an object-focused approach. Furthermore, the proposed strategies do not depend on the choice of a baseline super-resolution framework, hence could be adopted for current and future state-of-the-art models. Our experimental study on small vehicle detection in remote sensing data conducted on both aerial and satellite images (i.e., ISPRS Potsdam and xView datasets) confirms the effectiveness of the improved super-resolution methods to assist with the small object detection tasks.

scholarly journals Object Detection in Remote Sensing Images Based on a Scene-Contextual Feature Pyramid Network

2019 ◽  
Vol 11 (3) ◽  
pp. 339 ◽  
Author(s):  
Chaoyue Chen ◽  
Weiguo Gong ◽  
Yongliang Chen ◽  
Weihong Li
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Contextual Information ◽  
Model Performance ◽  
Small Object ◽  
Remote Sensing Images ◽  
Art Object ◽  
Contextual Feature ◽  
Feature Pyramid ◽  
Small Object Detection

Object detection has attracted increasing attention in the field of remote sensing image analysis. Complex backgrounds, vertical views, and variations in target kind and size in remote sensing images make object detection a challenging task. In this work, considering that the types of objects are often closely related to the scene in which they are located, we propose a convolutional neural network (CNN) by combining scene-contextual information for object detection. Specifically, we put forward the scene-contextual feature pyramid network (SCFPN), which aims to strengthen the relationship between the target and the scene and solve problems resulting from variations in target size. Additionally, to improve the capability of feature extraction, the network is constructed by repeating a building aggregated residual block. This block increases the receptive field, which can extract richer information for targets and achieve excellent performance with respect to small object detection. Moreover, to improve the proposed model performance, we use group normalization, which divides the channels into groups and computes the mean and variance for normalization within each group, to solve the limitation of the batch normalization. The proposed method is validated on a public and challenging dataset. The experimental results demonstrate that our proposed method outperforms other state-of-the-art object detection models.

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