scholarly journals A Method for Vehicle Detection in High-Resolution Satellite Images that Uses a Region-Based Object Detector and Unsupervised Domain Adaptation

2020 ◽  
Vol 12 (3) ◽  
pp. 575 ◽  
Author(s):  
Yohei Koga ◽  
Hiroyuki Miyazaki ◽  
Ryosuke Shibasaki
Keyword(s):  
Deep Learning ◽  
Domain Adaptation ◽  
Vehicle Detection ◽  
Detection Performance ◽  
Training Data ◽  
Detection Accuracy ◽  
Target Area ◽  
Target Domain ◽  
Unsupervised Domain Adaptation ◽  
High Resolution Satellite Images

Recently, object detectors based on deep learning have become widely used for vehicle detection and contributed to drastic improvement in performance measures. However, deep learning requires much training data, and detection performance notably degrades when the target area of vehicle detection (the target domain) is different from the training data (the source domain). To address this problem, we propose an unsupervised domain adaptation (DA) method that does not require labeled training data, and thus can maintain detection performance in the target domain at a low cost. We applied Correlation alignment (CORAL) DA and adversarial DA to our region-based vehicle detector and improved the detection accuracy by over 10% in the target domain. We further improved adversarial DA by utilizing the reconstruction loss to facilitate learning semantic features. Our proposed method achieved slightly better performance than the accuracy achieved with the labeled training data of the target domain. We demonstrated that our improved DA method could achieve almost the same level of accuracy at a lower cost than non-DA methods with a sufficient amount of labeled training data of the target domain.

scholarly journals Comparative Research on Deep Learning Approaches for Airplane Detection from Very High-Resolution Satellite Images

2020 ◽  
Vol 12 (3) ◽  
pp. 458 ◽  
Author(s):  
Ugur Alganci ◽  
Mehmet Soydas ◽  
Elif Sertel
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Object Detection ◽  
Satellite Images ◽  
Training Data ◽  
Object Localization ◽  
Detection Accuracy ◽  
Single Shot ◽  
High Resolution Satellite Images ◽  
Very High

Object detection from satellite images has been a challenging problem for many years. With the development of effective deep learning algorithms and advancement in hardware systems, higher accuracies have been achieved in the detection of various objects from very high-resolution (VHR) satellite images. This article provides a comparative evaluation of the state-of-the-art convolutional neural network (CNN)-based object detection models, which are Faster R-CNN, Single Shot Multi-box Detector (SSD), and You Look Only Once-v3 (YOLO-v3), to cope with the limited number of labeled data and to automatically detect airplanes in VHR satellite images. Data augmentation with rotation, rescaling, and cropping was applied on the test images to artificially increase the number of training data from satellite images. Moreover, a non-maximum suppression algorithm (NMS) was introduced at the end of the SSD and YOLO-v3 flows to get rid of the multiple detection occurrences near each detected object in the overlapping areas. The trained networks were applied to five independent VHR test images that cover airports and their surroundings to evaluate their performance objectively. Accuracy assessment results of the test regions proved that Faster R-CNN architecture provided the highest accuracy according to the F1 scores, average precision (AP) metrics, and visual inspection of the results. The YOLO-v3 ranked as second, with a slightly lower performance but providing a balanced trade-off between accuracy and speed. The SSD provided the lowest detection performance, but it was better in object localization. The results were also evaluated in terms of the object size and detection accuracy manner, which proved that large- and medium-sized airplanes were detected with higher accuracy.

scholarly journals Harmonized Segmentation of Neonatal Brain MRI

2021 ◽  
Vol 15 ◽  
Author(s):  
Irina Grigorescu ◽  
Lucy Vanes ◽  
Alena Uus ◽  
Dafnis Batalle ◽  
Lucilio Cordero-Grande ◽  
...  
Keyword(s):  
Cortical Thickness ◽  
Domain Adaptation ◽  
Brain Mri ◽  
Medical Image Segmentation ◽  
Training Data ◽  
Imaging Data ◽  
Target Domain ◽  
Tissue Segmentation ◽  
Unsupervised Domain Adaptation ◽  
Preterm Born

Deep learning based medical image segmentation has shown great potential in becoming a key part of the clinical analysis pipeline. However, many of these models rely on the assumption that the train and test data come from the same distribution. This means that such methods cannot guarantee high quality predictions when the source and target domains are dissimilar due to different acquisition protocols, or biases in patient cohorts. Recently, unsupervised domain adaptation techniques have shown great potential in alleviating this problem by minimizing the shift between the source and target distributions, without requiring the use of labeled data in the target domain. In this work, we aim to predict tissue segmentation maps on T2-weighted magnetic resonance imaging data of an unseen preterm-born neonatal population, which has both different acquisition parameters and population bias when compared to our training data. We achieve this by investigating two unsupervised domain adaptation techniques with the objective of finding the best solution for our problem. We compare the two methods with a baseline fully-supervised segmentation network and report our results in terms of Dice scores obtained on our source test dataset. Moreover, we analyse tissue volumes and cortical thickness measures of the harmonized data on a subset of the population matched for gestational age at birth and postmenstrual age at scan. Finally, we demonstrate the applicability of the harmonized cortical gray matter maps with an analysis comparing term and preterm-born neonates and a proof-of-principle investigation of the association between cortical thickness and a language outcome measure.

scholarly journals Unsupervised Domain Adaptation via Discriminative Manifold Embedding and Alignment

2020 ◽  
Vol 34 (04) ◽  
pp. 5029-5036
Author(s):  
You-Wei Luo ◽  
Chuan-Xian Ren ◽  
Pengfei Ge ◽  
Ke-Kun Huang ◽  
Yu-Feng Yu
Keyword(s):  
Deep Learning ◽  
Manifold Learning ◽  
Domain Adaptation ◽  
Global Approximation ◽  
Target Domain ◽  
Learning Framework ◽  
Unsupervised Domain Adaptation ◽  
Manifold Embedding ◽  
Rich Information ◽  
The Rich

Unsupervised domain adaptation is effective in leveraging the rich information from the source domain to the unsupervised target domain. Though deep learning and adversarial strategy make an important breakthrough in the adaptability of features, there are two issues to be further explored. First, the hard-assigned pseudo labels on the target domain are risky to the intrinsic data structure. Second, the batch-wise training manner in deep learning limits the description of the global structure. In this paper, a Riemannian manifold learning framework is proposed to achieve transferability and discriminability consistently. As to the first problem, this method establishes a probabilistic discriminant criterion on the target domain via soft labels. Further, this criterion is extended to a global approximation scheme for the second issue; such approximation is also memory-saving. The manifold metric alignment is exploited to be compatible with the embedding space. A theoretical error bound is derived to facilitate the alignment. Extensive experiments have been conducted to investigate the proposal and results of the comparison study manifest the superiority of consistent manifold learning framework.

scholarly journals Unsupervised Domain Adaptation with Dual-Scheme Fusion Network for Medical Image Segmentation

2020 ◽  
Author(s):  
Danbing Zou ◽  
Qikui Zhu ◽  
Pingkun Yan
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Network Performance ◽  
Domain Adaptation ◽  
Medical Image Segmentation ◽  
Training Data ◽  
Tumor Segmentation ◽  
Target Domain ◽  
Brain Tumor Segmentation ◽  
Unsupervised Domain Adaptation

Domain adaptation aims to alleviate the problem of retraining a pre-trained model when applying it to a different domain, which requires large amount of additional training data of the target domain. Such an objective is usually achieved by establishing connections between the source domain labels and target domain data. However, this imbalanced source-to-target one way pass may not eliminate the domain gap, which limits the performance of the pre-trained model. In this paper, we propose an innovative Dual-Scheme Fusion Network (DSFN) for unsupervised domain adaptation. By building both source-to-target and target-to-source connections, this balanced joint information flow helps reduce the domain gap to further improve the network performance. The mechanism is further applied to the inference stage, where both the original input target image and the generated source images are segmented with the proposed joint network. The results are fused to obtain more robust segmentation. Extensive experiments of unsupervised cross-modality medical image segmentation are conducted on two tasks -- brain tumor segmentation and cardiac structures segmentation. The experimental results show that our method achieved significant performance improvement over other state-of-the-art domain adaptation methods.

scholarly journals Adversarial domain adaptation for cross data source macromolecule in situ structural classification in cellular electron cryo-tomograms

2019 ◽  
Vol 35 (14) ◽  
pp. i260-i268 ◽  
Author(s):  
Ruogu Lin ◽  
Xiangrui Zeng ◽  
Kris Kitani ◽  
Min Xu
Keyword(s):  
Deep Learning ◽  
Domain Adaptation ◽  
Training Data ◽  
Supplementary Information ◽  
Target Domain ◽  
Structural Classification ◽  
Feature Extractor ◽  
Data Source ◽  
The Cross

Abstract Motivation Since 2017, an increasing amount of attention has been paid to the supervised deep learning-based macromolecule in situ structural classification (i.e. subtomogram classification) in cellular electron cryo-tomography (CECT) due to the substantially higher scalability of deep learning. However, the success of such supervised approach relies heavily on the availability of large amounts of labeled training data. For CECT, creating valid training data from the same data source as prediction data is usually laborious and computationally intensive. It would be beneficial to have training data from a separate data source where the annotation is readily available or can be performed in a high-throughput fashion. However, the cross data source prediction is often biased due to the different image intensity distributions (a.k.a. domain shift). Results We adapt a deep learning-based adversarial domain adaptation (3D-ADA) method to timely address the domain shift problem in CECT data analysis. 3D-ADA first uses a source domain feature extractor to extract discriminative features from the training data as the input to a classifier. Then it adversarially trains a target domain feature extractor to reduce the distribution differences of the extracted features between training and prediction data. As a result, the same classifier can be directly applied to the prediction data. We tested 3D-ADA on both experimental and realistically simulated subtomogram datasets under different imaging conditions. 3D-ADA stably improved the cross data source prediction, as well as outperformed two popular domain adaptation methods. Furthermore, we demonstrate that 3D-ADA can improve cross data source recovery of novel macromolecular structures. Availability and implementation https://github.com/xulabs/projects Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals UNSUPERVISED DOMAIN ADAPTATION USING A TEACHER-STUDENT NETWORK FOR CROSS-CITY CLASSIFICATION OF SENTINEL-2 IMAGES

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 1569-1574
Author(s):  
J. Hu ◽  
L. Mou ◽  
X. X. Zhu
Keyword(s):  
Remote Sensing ◽  
Domain Adaptation ◽  
Training Data ◽  
Target Domain ◽  
Source Domain ◽  
Unsupervised Domain Adaptation ◽  
Teacher Student ◽  
The Mean ◽  
The City ◽  
Teacher Model

Abstract. A machine learning algorithm in remote sensing often fails in the inference of a data set which has a different geographic location than the training data. This is because data of different locations have different underlying distributions caused by complicated reasons, such as the climate and the culture. For a large scale or a global scale task, this issue becomes relevant since it is extremely expensive to collect training data over all regions of interest. Unsupervised domain adaptation is a potential solution for this issue. Its goal is to train an algorithm in a source domain and generalize it to a target domain without using any label from the target domain. Those domains can be associated to geographic locations in remote sensing. In this paper, we attempt to adapt the unsupervised domain adaptation strategy by using a teacher-student network, mean teacher model, to investigate a cross-city classification problem in remote sensing. The mean teacher model consists of two identical networks, a teacher network and a student network. The objective function is a combination of a classification loss and a consistent loss. The classification loss works within the source domain (a city) and aims at accomplishing the goal of classification. The consistent loss works within the target domain (another city) and aims at transferring the knowledge learned from the source to the target. In this paper, two cross-city scenarios are set up. First, we train the model with the data of the city Munich, Germany, and test it on the data of the city Moscow, Russia. The second one is carried out by switching the training and testing data. For comparison, the baseline algorithm is a ResNet-18 which is also chosen as the backbone for the teacher and student networks in the mean teacher model. With 10 independent runs, in the first scenario, the mean teacher model has a mean overall accuracy of 53.38% which is slightly higher than the mean overall accuracy of the baseline, 52.21%. However, in the second scenario, the mean teacher model has a mean overall accuracy of 62.71% which is 5% higher than the mean overall accuracy of the baseline, 57.76%. This work demonstrates that it is worthy to explore the potential of the mean teacher model to solve the domain adaptation issues in remote sensing.

scholarly journals Benchmarking Domain Adaptation Methods on Aerial Datasets

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8070
Author(s):  
Navya Nagananda ◽  
Abu Md Niamul Taufique ◽  
Raaga Madappa ◽  
Chowdhury Sadman Jahan ◽  
Breton Minnehan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Supervised Classification ◽  
Domain Adaptation ◽  
State Of The Art ◽  
Classification Performance ◽  
Target Domain ◽  
Source Domain ◽  
Unsupervised Domain Adaptation ◽  
Testing Data ◽  
Classification Tasks

Deep learning grew in importance in recent years due to its versatility and excellent performance on supervised classification tasks. A core assumption for such supervised approaches is that the training and testing data are drawn from the same underlying data distribution. This may not always be the case, and in such cases, the performance of the model is degraded. Domain adaptation aims to overcome the domain shift between the source domain used for training and the target domain data used for testing. Unsupervised domain adaptation deals with situations where the network is trained on labeled data from the source domain and unlabeled data from the target domain with the goal of performing well on the target domain data at the time of deployment. In this study, we overview seven state-of-the-art unsupervised domain adaptation models based on deep learning and benchmark their performance on three new domain adaptation datasets created from publicly available aerial datasets. We believe this is the first study on benchmarking domain adaptation methods for aerial data. In addition to reporting classification performance for the different domain adaptation models, we present t-SNE visualizations that illustrate the benefits of the adaptation process.

scholarly journals Augmenting Crop Detection for Precision Agriculture with Deep Visual Transfer Learning—A Case Study of Bale Detection

2020 ◽  
Vol 13 (1) ◽  
pp. 23
Author(s):  
Wei Zhao ◽  
William Yamada ◽  
Tianxin Li ◽  
Matthew Digman ◽  
Troy Runge
Keyword(s):  
Object Detection ◽  
Transfer Learning ◽  
Precision Agriculture ◽  
Crop Production ◽  
Domain Adaptation ◽  
Training Data ◽  
Detection Accuracy ◽  
Detection Model ◽  
Agriculture Products

In recent years, precision agriculture has been researched to increase crop production with less inputs, as a promising means to meet the growing demand of agriculture products. Computer vision-based crop detection with unmanned aerial vehicle (UAV)-acquired images is a critical tool for precision agriculture. However, object detection using deep learning algorithms rely on a significant amount of manually prelabeled training datasets as ground truths. Field object detection, such as bales, is especially difficult because of (1) long-period image acquisitions under different illumination conditions and seasons; (2) limited existing prelabeled data; and (3) few pretrained models and research as references. This work increases the bale detection accuracy based on limited data collection and labeling, by building an innovative algorithms pipeline. First, an object detection model is trained using 243 images captured with good illimitation conditions in fall from the crop lands. In addition, domain adaptation (DA), a kind of transfer learning, is applied for synthesizing the training data under diverse environmental conditions with automatic labels. Finally, the object detection model is optimized with the synthesized datasets. The case study shows the proposed method improves the bale detecting performance, including the recall, mean average precision (mAP), and F measure (F1 score), from averages of 0.59, 0.7, and 0.7 (the object detection) to averages of 0.93, 0.94, and 0.89 (the object detection + DA), respectively. This approach could be easily scaled to many other crop field objects and will significantly contribute to precision agriculture.

Correlation alignment with attention mechanism for unsupervised domain adaptation

2021 ◽  
pp. 1-7
Author(s):  
Rong Chen ◽  
Chongguang Ren
Keyword(s):  
Natural Language Processing ◽  
Language Processing ◽  
Transfer Process ◽  
Negative Transfer ◽  
Domain Adaptation ◽  
Attention Mechanism ◽  
Target Domain ◽  
Source Domain ◽  
Second Order Statistics ◽  
Unsupervised Domain Adaptation

Domain adaptation aims to solve the problems of lacking labels. Most existing works of domain adaptation mainly focus on aligning the feature distributions between the source and target domain. However, in the field of Natural Language Processing, some of the words in different domains convey different sentiment. Thus not all features of the source domain should be transferred, and it would cause negative transfer when aligning the untransferable features. To address this issue, we propose a Correlation Alignment with Attention mechanism for unsupervised Domain Adaptation (CAADA) model. In the model, an attention mechanism is introduced into the transfer process for domain adaptation, which can capture the positively transferable features in source and target domain. Moreover, the CORrelation ALignment (CORAL) loss is utilized to minimize the domain discrepancy by aligning the second-order statistics of the positively transferable features extracted by the attention mechanism. Extensive experiments on the Amazon review dataset demonstrate the effectiveness of CAADA method.

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