Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Browser

Deep Learning for Remote Sensing in Data Scarce Regimes

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (15 December 2022) | Viewed by 22248

Share This Special Issue

Special Issue Editors

Dr. Mohammad Rostami

E-Mail Website
Guest Editor

Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Suite 1001, Marina del Rey, CA 90292, USA
Interests: machine learning in data scarce learning regimes

Dr. Senthilnath Jayavelu

E-Mail Website
Guest Editor

Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
Interests: deep learning; reinforcement learning; optimizations; multiagent systems; materials informatics; remote sensing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Yongshuo Fu

E-Mail Website
Guest Editor

College of Water Sciences, Beijing Normal University, Beijing 100875, China
Interests: vegetation phenology; climate change; ecohydrology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in deep learning have led to high-performance supervised learning algorithms for the domain of electrooptical (EO) images. This success, however, is conditioned on generating huge annotated datasets using modern crowdsourcing data annotation platforms such as Amazon Mechanical Turk that recruit ordinary people for data annotation. Unlike the EO domain, data annotation in remote sensing domains is substantially more challenging, and for various reasons, using crowdsourcing platforms is not feasible. As a result, we frequently encounter data scarcity in solving supervised deep learning in remote sensing applications. This Special Issue serves as an outlet for articles covering but not limited to:

- Cross-domain transfer learning for remote sensing applications;
- Domain adaptation using synthetic data in remote sensing applications;
- Zero-shot and few-shot learning in remote sensing applications;
- Efficient approaches for remote sensing data annotation.

Dr. Mohammad Rostami
Dr. Senthilnath Jayavelu
Prof. Dr. Yongshuo Fu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

data-scarce learning regime
zero-shot learning
domain adaptation
transfer learning

Published Papers (5 papers)

Download All Papers

Research

22 pages, 5329 KiB

Open AccessArticle

AFFPN: Attention Fusion Feature Pyramid Network for Small Infrared Target Detection

by Zhen Zuo, Xiaozhong Tong, Junyu Wei, Shaojing Su, Peng Wu, Runze Guo and Bei Sun

Remote Sens. 2022, 14(14), 3412; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14143412 - 15 Jul 2022

Cited by 27 | Viewed by 3204

Abstract

The detection of small infrared targets lacking texture and shape information in the presence of complex background clutter is a challenge that has attracted considerable research attention in recent years. Typical deep learning-based target detection methods are designed with deeper network structures, which may lose targets in the deeper layers and cannot directly be used for small infrared target detection. Therefore, we designed the attention fusion feature pyramid network (AFFPN) specifically for small infrared target detection. Specifically, it consists of feature extraction and feature fusion modules. In the feature extraction stage, the global contextual prior information of small targets is first considered in the deep layer of the network using the atrous spatial pyramid pooling module. Subsequently, the spatial location and semantic information features of small infrared targets in the shallow and deep layers are adaptively enhanced by the designed attention fusion module to improve the feature representation capability of the network for targets. Finally, high-performance detection is achieved through the multilayer feature fusion mechanism. Moreover, we performed a comprehensive ablation study to evaluate the effectiveness of each component. The results demonstrate that the proposed method performs better than state-of-the-art methods on a publicly available dataset. Furthermore, AFFPN was deployed on an NVIDIA Jetson AGX Xavier development board and achieved real-time target detection, further advancing practical research and applications in the field of unmanned aerial vehicle infrared search and tracking. Full article

(This article belongs to the Special Issue Deep Learning for Remote Sensing in Data Scarce Regimes)

► Show Figures

Figure 1

20 pages, 8709 KiB

Open AccessArticle

UAV Multispectral Image-Based Urban River Water Quality Monitoring Using Stacked Ensemble Machine Learning Algorithms—A Case Study of the Zhanghe River, China

by Yi Xiao, Yahui Guo, Guodong Yin, Xuan Zhang, Yu Shi, Fanghua Hao and Yongshuo Fu

Remote Sens. 2022, 14(14), 3272; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14143272 - 07 Jul 2022

Cited by 23 | Viewed by 3867

Abstract

Timely monitoring of inland water quality using unmanned aerial vehicle (UAV) remote sensing is critical for water environmental conservation and management. In this study, two UAV flights were conducted (one in February and the other in December 2021) to acquire images of the Zhanghe River (China), and a total of 45 water samples were collected concurrently with the image acquisition. Machine learning (ML) methods comprising Multiple Linear Regression, the Least Absolute Shrinkage and Selection Operator, a Backpropagation Neural Network (BP), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) were applied to retrieve four water quality parameters: chlorophyll-a (Chl-a), total nitrogen (TN), total phosphors (TP), and permanganate index (COD_Mn). Then, ML models based on the stacking approach were developed. Results show that stacked ML models could achieve higher accuracy than a single ML model; the optimal methods for Chl-a, TN, TP, and COD_Mn were RF-XGB, BP-RF, RF, and BP-RF, respectively. For the testing dataset, the R² values of the best inversion models for Chl-a, TN, TP, and COD_Mn were 0.504, 0.839, 0.432, and 0.272, the root mean square errors were 1.770 μg L⁻¹, 0.189 mg L⁻¹, 0.053 mg L⁻¹, and 0.767 mg L⁻¹, and the mean absolute errors were 1.272 μg L⁻¹, 0.632 mg L⁻¹, 0.045 mg L⁻¹, and 0.674 mg L⁻¹, respectively. This study demonstrated the great potential of combined UAV remote sensing and stacked ML algorithms for water quality monitoring. Full article

(This article belongs to the Special Issue Deep Learning for Remote Sensing in Data Scarce Regimes)

► Show Figures

Graphical abstract

18 pages, 3882 KiB

Open AccessArticle

RAANet: A Residual ASPP with Attention Framework for Semantic Segmentation of High-Resolution Remote Sensing Images

by Runrui Liu, Fei Tao, Xintao Liu, Jiaming Na, Hongjun Leng, Junjie Wu and Tong Zhou

Remote Sens. 2022, 14(13), 3109; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14133109 - 28 Jun 2022

Cited by 50 | Viewed by 6551

Abstract

Classification of land use and land cover from remote sensing images has been widely used in natural resources and urban information management. The variability and complex background of land use in high-resolution imagery poses greater challenges for remote sensing semantic segmentation. To obtain multi-scale semantic information and improve the classification accuracy of land-use types in remote sensing images, the deep learning models have been wildly focused on. Inspired by the idea of the atrous-spatial pyramid pooling (ASPP) framework, an improved deep learning model named RAANet (Residual ASPP with Attention Net) is constructed in this paper, which constructed a new residual ASPP by embedding the attention module and residual structure into the ASPP. There are 5 dilated attention convolution units and a residual unit in its encoder. The former is used to obtain important semantic information at more scales, and residual units are used to reduce the complexity of the network to prevent the disappearance of gradients. In practical applications, according to the characteristics of the data set, the attention unit can select different attention modules such as the convolutional block attention model (CBAM). The experimental results obtained from the land-cover domain adaptive semantic segmentation (LoveDA) and ISPRS Vaihingen datasets showed that this model can enhance the classification accuracy of semantic segmentation compared to the current deep learning models. Full article

(This article belongs to the Special Issue Deep Learning for Remote Sensing in Data Scarce Regimes)

► Show Figures

Figure 1

18 pages, 5360 KiB

Open AccessArticle

Forest Resistance and Resilience to 2002 Drought in Northern China

by Xiran Li, Yitong Yao, Guodong Yin, Feifei Peng and Muxing Liu

Remote Sens. 2021, 13(15), 2919; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13152919 - 25 Jul 2021

Cited by 5 | Viewed by 2628

Abstract

Drought can weaken forest activity and even lead to forest mortality, and the response of different forest types to drought can be diverse. Deciduous broadleaf forest (DBF) and deciduous needleleaf forest (DNF) are two of the majority forest types in northern China. In this region, a severe drought event happened in 2002. However, due to the lack of data, the spatio-temporal characteristics of the ecosystem stability of different forest types here remain unclear. In this study, we used a machine learning approach (model tree ensemble, MTE) to drive fluxsite gross primary productivity (GPP), combined with remote sensing-based GPP and a vegetation index data (EVI), to analyze the spatial and temporal characteristics of resistance and resilience of DNF and DBF in northern China during and after the 2002 drought. The results showed that the DBFs were more acclimatized to the drought, while the resistance and resilience of DNF and DBF were diverse under different consecutive drought events. These results could be suggestive for forest conservation and vegetation modeling. Full article

(This article belongs to the Special Issue Deep Learning for Remote Sensing in Data Scarce Regimes)

► Show Figures

Figure 1

20 pages, 17098 KiB

Open AccessArticle

SAFFNet: Self-Attention-Based Feature Fusion Network for Remote Sensing Few-Shot Scene Classification

by Joseph Kim and Mingmin Chi

Remote Sens. 2021, 13(13), 2532; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132532 - 28 Jun 2021

Cited by 36 | Viewed by 3877

Abstract

In real applications, it is necessary to classify new unseen classes that cannot be acquired in training datasets. To solve this problem, few-shot learning methods are usually adopted to recognize new categories with only a few (out-of-bag) labeled samples together with the known classes available in the (large-scale) training dataset. Unlike common scene classification images obtained by CCD (Charge-Coupled Device) cameras, remote sensing scene classification datasets tend to have plentiful texture features rather than shape features. Therefore, it is important to extract more valuable texture semantic features from a limited number of labeled input images. In this paper, a multi-scale feature fusion network for few-shot remote sensing scene classification is proposed by integrating a novel self-attention feature selection module, denoted as SAFFNet. Unlike a pyramidal feature hierarchy for object detection, the informative representations of the images with different receptive fields are automatically selected and re-weighted for feature fusion after refining network and global pooling operation for a few-shot remote sensing classification task. Here, the feature weighting value can be fine-tuned by the support set in the few-shot learning task. The proposed model is evaluated on three publicly available datasets for few shot remote sensing scene classification. Experimental results demonstrate the effectiveness of the proposed SAFFNet to improve the few-shot classification accuracy significantly compared to other few-shot methods and the typical multi-scale feature fusion network. Full article

(This article belongs to the Special Issue Deep Learning for Remote Sensing in Data Scarce Regimes)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Deep Learning for Remote Sensing in Data Scarce Regimes

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (5 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI