scholarly journals A Novel Method of Aircraft Detection Based on High-Resolution Panchromatic Optical Remote Sensing Images

Sensors ◽  
2017 ◽  
Vol 17 (5) ◽  
pp. 1047 ◽  
Author(s):  
Wensheng Wang ◽  
Ting Nie ◽  
Tianjiao Fu ◽  
Jianyue Ren ◽  
Longxu Jin
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Novel Method ◽  
Aircraft Detection

scholarly journals EU-Net: An Efficient Fully Convolutional Network for Building Extraction from Optical Remote Sensing Images

2019 ◽  
Vol 11 (23) ◽  
pp. 2813 ◽  
Author(s):  
Wenchao Kang ◽  
Yuming Xiang ◽  
Feng Wang ◽  
Hongjian You
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Ground Truth ◽  
Aerial Image ◽  
Optical Remote Sensing ◽  
Building Extraction ◽  
Remote Sensing Images ◽  
Convolutional Network ◽  
Practical Applications ◽  
The Impact

Automatic building extraction from high-resolution remote sensing images has many practical applications, such as urban planning and supervision. However, fine details and various scales of building structures in high-resolution images bring new challenges to building extraction. An increasing number of neural network-based models have been proposed to handle these issues, while they are not efficient enough, and still suffer from the error ground truth labels. To this end, we propose an efficient end-to-end model, EU-Net, in this paper. We first design the dense spatial pyramid pooling (DSPP) to extract dense and multi-scale features simultaneously, which facilitate the extraction of buildings at all scales. Then, the focal loss is used in reverse to suppress the impact of the error labels in ground truth, making the training stage more stable. To assess the universality of the proposed model, we tested it on three public aerial remote sensing datasets: WHU aerial imagery dataset, Massachusetts buildings dataset, and Inria aerial image labeling dataset. Experimental results show that the proposed EU-Net is superior to the state-of-the-art models of all three datasets and increases the prediction efficiency by two to four times.

Fast and accurate denoising method applied to very high resolution optical remote sensing images

2017 ◽  
Author(s):  
Antoine Masse ◽  
Sebastien Lefevre ◽  
Renaud Binet ◽  
Gwendoline Blanchet ◽  
Stéphanie Artigues ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Denoising Method ◽  
Very High

scholarly journals An Aircraft Object Detection Algorithm Based on Small Samples in Optical Remote Sensing Image

2020 ◽  
Vol 10 (17) ◽  
pp. 5778
Author(s):  
Ting Wang ◽  
Changqing Cao ◽  
Xiaodong Zeng ◽  
Zhejun Feng ◽  
Jingshi Shen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Detection Algorithm ◽  
Small Samples ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Sensing Technology ◽  
Optical Remote Sensing Image ◽  
Aircraft Detection ◽  
Small Aircraft

In recent years, remote sensing technology has developed rapidly, and the ground resolution of spaceborne optical remote sensing images has reached the sub-meter range, providing a new technical means for aircraft object detection. Research on aircraft object detection based on optical remote sensing images is of great significance for military object detection and recognition. However, spaceborne optical remote sensing images are difficult to obtain and costly. Therefore, this paper proposes the aircraft detection algorithm, itcan detect aircraft objects with small samples. Firstly, this paper establishes an aircraft object dataset containing weak and small aircraft objects. Secondly, the detection algorithm has been proposed to detect weak and small aircraft objects. Thirdly, the aircraft detection algorithm has been proposed to detect multiple aircraft objects of varying sizes. There are 13,324 aircraft in the test set. According to the method proposed in this paper, the f1 score can achieve 90.44%. Therefore, the aircraft objects can be detected simply and efficiently by using the method proposed. It can effectively detect aircraft objects and improve early warning capabilities.

scholarly journals Deep Fusion Feature Based Object Detection Method for High Resolution Optical Remote Sensing Images

2019 ◽  
Vol 9 (6) ◽  
pp. 1130 ◽  
Author(s):  
Eric Wang ◽  
Yueping Li ◽  
Zhe Nie ◽  
Juntao Yu ◽  
Zuodong Liang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Object Detection ◽  
Fine Tuning ◽  
Detection Accuracy ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Feature Extraction Method ◽  
Feature Based ◽  
Fusion Feature

With the rapid growth of high-resolution remote sensing image-based applications, one of the fundamental problems in managing the increasing number of remote sensing images is automatic object detection. In this paper, we present a fusion feature-based deep learning approach to detect objects in high-resolution remote sensing images. It employs fine-tuning from ImageNet as a pre-training model to address the challenge of it lacking a large amount of training datasets in remote sensing. Besides, we improve the binarized normed gradients algorithm by multiple weak feature scoring models for candidate window selection and design a deep fusion feature extraction method with the context feature and object feature. Experiments are performed on different sizes of high-resolution optical remote sensing images. The results show that our model is better than regular models, and the average detection accuracy is 8.86% higher than objNet.

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