Weakly Supervised Learning Based on Coupled Convolutional Neural Networks for Aircraft Detection

By Fan Zhang, Bo Du, Liangpei Zhang, Miaozhong Xu. 2016

248 citations recorded by the source corpus.

Aircraft detection from very high resolution (VHR) remote sensing images has been drawing increasing interest in recent years due to the successful civil and military applications. However, several challenges still exist: 1) extracting the high-level features and the hierarchical feature representations of the objects is difficult; 2) manual annotation of the objects in large image sets is generally expensive and sometimes unreliable; and 3) locating objects within such a large image is difficult and time consuming. In this paper, we propose a weakly supervised learning framework based on coupled convolutional neural networks (CNNs) for aircraft detection, which can simultaneously solve these problems. We first develop a CNN-based method to extract the high-level features and the hierarchical feature representations of the objects. We then employ an iterative weakly supervised learning framework to automatically mine and augment the training data set from the original image. We propose a coupled CNN method, which combines a candidate region proposal network and a localization network to extract the proposals and simultaneously locate the aircraft, which is more efficient and accurate, even in large-scale VHR images. In the experiments, the proposed method was applied to three challenging high-resolution data sets: the Sydney International Airport data set, the Tokyo Haneda Airport data set, and the Berlin Tegel Airport data set. The extensive experimental results confirm that the proposed method can achieve a higher detection accuracy than the other methods.

Subfields: Computer Vision and Pattern Recognition, Environmental Engineering, Ocean Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Junwei Han
2. Gong Cheng
3. Guisong Xia
4. Liangpei Zhang
5. Xiang Bai
6. Xiaoqiang Lu
7. Dacheng Tao
8. Jian Ding

In Environmental Engineering

1. Xiang Bai
2. Mihai Datcu
3. Friedrich Fraundorfer
4. Gong Cheng
5. Feng Xu
6. Devis Tuia
7. Jiebo Luo
8. Lichao Mou

In Ocean Engineering

1. Gong Cheng
2. Junwei Han
3. Xiaoqiang Lu
4. Gui-Song Xia
5. Liangpei Zhang
6. Lichao Mou
7. Mihai Datcu
8. Jian Ding

Citations in the corpus

• Remote Sensing Image Scene Classification: Benchmark and State of the Art (2017, background): “In recent years, there has been an extensive popularity of CNNs in various remote sensing applications, such as geospatial object detection [45, 50, 173] and land use scene classification”
• Weakly Supervised Object Localization and Detection: A Survey (2021, unknown): “In [203], Zhang et al. propose a coupled CNN method which combines a candidate region proposal network and a localization network to detect aircrafts in images.”
• A Survey of Deep Learning-Based Object Detection (2019, methodology): “Typical methods [155]–[165] all utilize deep neural networks to achieve detection task on remote sensing datasets.”
• Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network (2020, methodology): “ve 1. Introduction 1.1. Problem Description and Motivation Object detection on remote sensing imagery has numerous prospects in various fields, such as environmental regulation, surveillance, military [1,2], national security, traffic, forestry [3], oil and gas activity monitoring. There are many meth”
• Learning a Rotation Invariant Detector with Rotatable Bounding Box (2017, methodology): “A CNN based pio-neering study upon vehicle detection in satellite images is presented in[2], followed by many approaches that focus on different types of geospatial object detection problems [14, 20, 21, 5, 26, 17].”
• DOTA: A Large-Scale Dataset for Object Detection in Aerial Images (2017, background): “Extensive studies have been devoted to object detection in aerial images [24, 15, 18, 3, 20, 39, 19, 32, 31, 22], drawing upon recent advances in Computer Vision and accounting for the high demands of Earth Vision applications.”
• CAD-Net: A Context-Aware Detection Network for Objects in Remote Sensing Imagery (2019, methodology): “For example, [29] uses multi-scale CNN features for airport detection, [31] takes a weakly supervised learning framework for aircraft detection using CNNs, [30] and [32] integrate dense FPN (DFPN) and rotation regression for ship detection in remote sensing images, and [28] uses CNN features for mul”
• Accurate Object Localization in Remote Sensing Images Based on Convolutional Neural Networks (2017, methodology): “[46] constructed an iterative weakly supervised learning framework to automatically mine and augment the”
• Rotation-Insensitive and Context-Augmented Object Detection in Remote Sensing Images (2018, unknown): “Zhang et al. [32] construct an iterative weakly supervised-learning framework to automatically mine and augment the training data set from the original images and combine the candidate RPN and a localization network to extract the proposals and locate aircrafts in large-scale very high-resolution…”
• Deep learning in remote sensing: a review (2017, methodology): “[105] proposed a weakly supervised learning approach using coupled CNNs for aircraft detection.”