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Crowd-Sourced Data and Deep Learning in Remote Sensing: Methods and Applications

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: closed (27 April 2024) | Viewed by 12893

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Dr. Zelang Miao

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Guest Editor

School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Interests: pattern recognition; land cover mapping; global/regional urbanization

Prof. Dr. Hao Wu

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Guest Editor

College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China
Interests: land cover and land use; change detection; cellular automata; GIS applications

Dr. Dongyang Hou

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Guest Editor

School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Interests: web service discovery and WebGIS development; spatio-temporal crowd-sourced data mining; remote sensing image retrieval; land cover mapping
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Special Issue Information

Dear Colleagues,

Although we are living in a global Big Data era, the challenges to intelligent satellite image interpretation still remain. The advances in deep learning have significantly improved image processing capacity. The number and variety of training sample data, however, is insufficient for processing the large volume of multi-source satellite images. From a different research perspective, the evolution and exponential growth of modern information technology (e.g., smart mobile devices) has expedited the availability of large amounts of data, the so-called crowd-sourced data. The crowd-sourced data produced by people worldwide, either accidentally or intentionally, is proven to be an essential and cost-effective tool in a wide range of practical applications, such as training sample collection. To date, only a few studies have examined the integrated applications of crowd-sourced data and deep learning in the community of remote sensing, and thus further studies are necessary in order to address this topic.

Dr. Zelang Miao
Prof. Dr. Hao Wu
Dr. Dongyang Hou
Guest Editors

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Keywords

crowd-sourced data
deep learning
remote sensing

Published Papers (9 papers)

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Research

21 pages, 3199 KiB

Open AccessArticle

A Method for Measuring Spatial Information of Area Maps Considering the Diversity of Node–Edge and Gestalt Principles

by Qiankun Kang, Xiaoguang Zhou and Dongyang Hou

Appl. Sci. 2024, 14(9), 3764; https://doi.org/10.3390/app14093764 - 28 Apr 2024

Viewed by 141

Abstract

Existing methods for measuring the spatial information of area maps fail to take into account the diversity of adjacency relations and the heterogeneity of adjacency distances among area objects, resulting in insufficient measurement information. This article proposes a method for measuring area map information that considers the diversity of the node–edge and Gestalt principles. Firstly, this method utilizes the adjacency relations between the Voronoi diagram of area objects to construct an adjacency graph that characterizes the spatial distribution of area objects in area maps. This adjacency graph serves as the information representation of area maps. Secondly, the method selects four characteristic indicators, namely geometric information, node degree, adjacency distance, and adjacency strength, to represent the diversity of nodes and edges in the graph that affect spatial information. Finally, nodes in the adjacency graph are taken as the basic units, and the spatial information of area maps is comprehensively calculated by integrating the four characteristics that represent spatial information. To verify the validity and rationality of the proposed method, a dataset of continuously simplified area maps and a dataset of artificially simulated degrees of randomness were designed to evaluate the performance of the existing method and the method proposed in this paper. The results indicate that the correlation between the measurement results obtained by the method proposed in this paper and the degree of disorder is as high as 0.94, outperforming the existing representative methods. Additionally, the correlation between the measurement results of this method and the degree of simplification reaches 1, indicating that the variation range of the measured values is more consistent with the cognitive assumptions based on artificial simulations compared to the existing methods. The experimental results show that the method proposed in this paper is an effective metric approach for representing spatial information in area maps. Full article

(This article belongs to the Special Issue Crowd-Sourced Data and Deep Learning in Remote Sensing: Methods and Applications)

24 pages, 11724 KiB

Open AccessArticle

YOLO-SAD: An Efficient SAR Aircraft Detection Network

by Junyi Chen, Yanyun Shen, Yinyu Liang, Zhipan Wang and Qingling Zhang

Appl. Sci. 2024, 14(7), 3025; https://doi.org/10.3390/app14073025 - 03 Apr 2024

Viewed by 468

Abstract

Aircraft detection in SAR images of airports remains crucial for continuous ground observation and aviation transportation scheduling in all weather conditions, but low resolution and complex scenes pose unique challenges. Existing methods struggle with accuracy, overlapping detections, and missed targets. We propose You Only Look Once-SAR Aircraft Detector (YOLO-SAD), a novel detector that tackles these issues. YOLO-SAD leverages the Attention-Efficient Layer Aggregation Network-Head (A-ELAN-H) module to prioritize essential features for improved accuracy. Additionally, the SAR Aircraft Detection-Feature Pyramid Network (SAD-FPN) optimizes multi-scale feature fusion, boosting detection speed. Finally, Enhanced Non-Maximum Suppression (EH-NMS) eliminates overlapping detections. On the SAR Aircraft Detection Dataset (SADD), YOLO-SAD achieved 91.9% AP(0.5) and 57.1% AP(0.5:0.95), surpassing the baseline by 2.1% and 1.9%, respectively. Extensive comparisons on SADD further demonstrate YOLO-SAD’s superiority over five state-of-the-art methods in both AP(0.5) and AP(0.5:0.95). The outcomes of further comparative experiments on the SAR-AIRcraft-1.0 dataset confirm the robust generalization capability of YOLO-SAD, demonstrating its potential use in aircraft detection with SAR. Full article

(This article belongs to the Special Issue Crowd-Sourced Data and Deep Learning in Remote Sensing: Methods and Applications)

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20 pages, 4820 KiB

Open AccessArticle

Building Polygon Extraction from High-Resolution Remote Sensing Imagery Using Knowledge Distillation

by Haiyan Xu, Gang Xu, Geng Sun, Jie Chen and Jun Hao

Appl. Sci. 2023, 13(16), 9239; https://doi.org/10.3390/app13169239 - 14 Aug 2023

Viewed by 1068

Abstract

Building polygons plays an important role in urban management. Although leveraging deep learning techniques for building polygon extraction offers advantages, the models heavily rely on a large number of training samples to achieve good generalization performance. In scenarios with small training samples, the models struggle to effectively represent diverse building structures and handle the complexity introduced by the background. A common approach to enhance feature representation is fine-tuning a pre-trained model on a large dataset specific to the task. However, the fine-tuning process tends to overfit the model to the task area samples, leading to the loss of generalization knowledge from the large dataset. To address this challenge and enable the model to inherit the generalization knowledge from the large dataset while learning the characteristics of the task area samples, this paper proposes a knowledge distillation-based framework called Building Polygon Distillation Network (BPDNet). The teacher network of BPDNet is trained on a large building polygon dataset containing diverse building samples. The student network was trained on a small number of available samples from the target area to learn the characteristics of the task area samples. The teacher network provides guidance during the training of the student network, enabling it to learn under the supervision of generalization knowledge. Moreover, to improve the extraction of buildings against the backdrop of a complex urban context, characterized by fuzziness, irregularity, and connectivity issues, BPDNet employs the Dice Loss, which focuses attention on building boundaries. The experimental results demonstrated that BPDNet effectively addresses the problem of limited generalization by integrating the generalization knowledge from the large dataset with the characteristics of the task area samples. It accurately identifies building polygons with diverse structures and alleviates boundary fuzziness and connectivity issues. Full article

(This article belongs to the Special Issue Crowd-Sourced Data and Deep Learning in Remote Sensing: Methods and Applications)

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15 pages, 3847 KiB

Open AccessArticle

An Anomaly Detection-Based Domain Adaptation Framework for Cross-Domain Building Extraction from Remote Sensing Images

by Shaoxuan Zhao, Xiaoguang Zhou and Dongyang Hou

Appl. Sci. 2023, 13(3), 1674; https://doi.org/10.3390/app13031674 - 28 Jan 2023

Cited by 1 | Viewed by 1638

Abstract

Deep learning-based building extraction methods have achieved a high accuracy in closed remote sensing datasets. In fact, the distribution bias between the source and target domains can lead to a dramatic decrease in their building extraction effect in the target domain. However, the mainstream domain adaptation methods that specifically address this domain bias problem require the reselection of many unlabeled samples and retraining in other target domains. This is time-consuming and laborious and even impossible at small regions. To address this problem, a novel domain adaptation framework for cross-domain building extraction is proposed from a perspective of anomaly detection. First, the initial extraction results of images in the target domain are obtained by a source domain-based pre-trained model, and then these results are classified into building mixed and non-building layers according to the predicted probability. Second, anomalous objects in the building layer are detected using the isolation forest method. Subsequently, the remaining objects in the building layer and the objects in the non-building layer are used as positive and negative samples, respectively, to reclassify the mixed layer using the random forest classifier. The newly extracted objects are fused with the remaining objects in the building layer as the final result. Four different experiments are performed on different semantic segmentation models and target domains. Some experimental results indicate that our framework can improve cross-domain building extraction compared to the pre-trained model, with an 8.7% improvement in the F1 metric when migrating from the Inria Aerial Image Labeling dataset to the Wuhan University dataset. Furthermore, experimental results show that our framework can be applied to multiple target domains without retraining and can achieve similar results to domain adaptation models based on adversarial learning. Full article

(This article belongs to the Special Issue Crowd-Sourced Data and Deep Learning in Remote Sensing: Methods and Applications)

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14 pages, 1571 KiB

Open AccessArticle

Contrasting Dual Transformer Architectures for Multi-Modal Remote Sensing Image Retrieval

by Mohamad M. Al Rahhal, Mohamed Abdelkader Bencherif, Yakoub Bazi, Abdullah Alharbi and Mohamed Lamine Mekhalfi

Appl. Sci. 2023, 13(1), 282; https://doi.org/10.3390/app13010282 - 26 Dec 2022

Cited by 3 | Viewed by 1835

Abstract

Remote sensing technology has advanced rapidly in recent years. Because of the deployment of quantitative and qualitative sensors, as well as the evolution of powerful hardware and software platforms, it powers a wide range of civilian and military applications. This in turn leads to the availability of large data volumes suitable for a broad range of applications such as monitoring climate change. Yet, processing, retrieving, and mining large data are challenging. Usually, content-based remote sensing image (RS) retrieval approaches rely on a query image to retrieve relevant images from the dataset. To increase the flexibility of the retrieval experience, cross-modal representations based on text–image pairs are gaining popularity. Indeed, combining text and image domains is regarded as one of the next frontiers in RS image retrieval. Yet, aligning text to the content of RS images is particularly challenging due to the visual-sematic discrepancy between language and vision worlds. In this work, we propose different architectures based on vision and language transformers for text-to-image and image-to-text retrieval. Extensive experimental results on four different datasets, namely TextRS, Merced, Sydney, and RSICD datasets are reported and discussed. Full article

(This article belongs to the Special Issue Crowd-Sourced Data and Deep Learning in Remote Sensing: Methods and Applications)

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13 pages, 3046 KiB

Open AccessArticle

A Kalman Filter-Based Method for Reconstructing GMS-5 Land Surface Temperature Time Series

by Rui Qin, Genliang Chen, Haibo Zhang, Luo Liu and Shaoqiu Long

Appl. Sci. 2022, 12(15), 7414; https://doi.org/10.3390/app12157414 - 23 Jul 2022

Viewed by 1165

Abstract

Satellite-derived environmental parameters play important roles in environmental research on global changes and regional resources. Atmosphere effects and sensor limitations often lead to data products that vary in quality. The main goal of time series data reconstruction is to use various statistical and numerical analysis methods and to stimulate changing seasonal or annual parameters, providing more complete data sets for correlational research. This paper aims to develop a time series reconstruction algorithm for LST based on data assimilation according to the current problems of unstable precision and unsatisfactory results, and the simplistic effects of evaluation methods while using remote sensing-derived LST data as the basic parameters and the daily LST data derived from the static meteorological satellite GMS-5 as the input data. The data assimilation system used the Kalman filter as the assimilation algorithm. A complete set of global refined LST time series data sets were obtained by constantly correcting the LST values according to the regional ground-based observations. This method was implemented using MATLAB software (version R2017a), and was applied and validated through partitioning using the principal elevation in the Beijing, Tianjin, and Hebei regions. The results show that the accuracy of the reconstructed LST data series improved significantly in terms of the mean and standard deviation. Better consistency was achieved between the variables obtained over a year from the reconstructed LST data and the ground observations from the LST data set. Full article

(This article belongs to the Special Issue Crowd-Sourced Data and Deep Learning in Remote Sensing: Methods and Applications)

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17 pages, 5597 KiB

Open AccessArticle

A Multiscale Attention-Guided UNet++ with Edge Constraint for Building Extraction from High Spatial Resolution Imagery

by Hua Zhao, Hua Zhang and Xiangcheng Zheng

Appl. Sci. 2022, 12(12), 5960; https://doi.org/10.3390/app12125960 - 11 Jun 2022

Cited by 6 | Viewed by 1643

Abstract

Building extraction from high spatial resolution imagery (HSRI) plays an important role in the remotely sensed imagery application fields. However, automatically extracting buildings from HSRI is still a challenging task due to such factors as large size variations of buildings, background complexity, variations in appearance, etc. Especially, it is difficult to extract both crowded small buildings and large buildings with accurate boundaries. To address these challenges, this paper presents an end-to-end encoder–decoder model to automatically extract buildings from HSRI. The designed network, called AEUNet++, is based on UNet++, attention mechanism and multi-task learning. Specifically, the AEUNet++ introduces the UNet++ as the backbone to extract multiscale features. Then, the attention block is used to effectively fuse different-layer feature maps instead of direct concatenation in the output of traditional UNet++, which can assign adaptive weights to different-layer feature maps as their relative importance to enhance the sensitivity of the mode and suppress the background influence of irrelevant features. To further improve the boundary accuracy of the extracted buildings, the boundary geometric information of buildings is integrated into the proposed model by a multi-task loss using a proposed distance class map during training of the network, which simultaneously learns the extraction of buildings and boundaries and only outputs extracted buildings while testing. Two different data sets are utilized for evaluating the performance of AEUNet++. The experimental results indicate that AEUNet++ produces greater accuracy than U-Net and the original UNet++ architectures and, hence, provides an effective method for building extraction from HSRI. Full article

(This article belongs to the Special Issue Crowd-Sourced Data and Deep Learning in Remote Sensing: Methods and Applications)

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21 pages, 5609 KiB

Open AccessArticle

An End-to-End Atrous Spatial Pyramid Pooling and Skip-Connections Generative Adversarial Segmentation Network for Building Extraction from High-Resolution Aerial Images

by Mingyang Yu, Wenzhuo Zhang, Xiaoxian Chen, Yaohui Liu and Jingge Niu

Appl. Sci. 2022, 12(10), 5151; https://doi.org/10.3390/app12105151 - 20 May 2022

Cited by 7 | Viewed by 1735

Abstract

Automatic building extraction based on high-resolution aerial imagery is an important challenge with a wide range of practical applications. One of the mainstream methods for extracting buildings from high-resolution images is deep learning because of its excellent deep feature extraction capability. However, existing models suffer from the problems of hollow interiors of some buildings and blurred boundaries. Furthermore, the increase in remote sensing image resolution has also led to rough segmentation results. To address these issues, we propose a generative adversarial segmentation network (ASGASN) for pixel-level extraction of buildings. The segmentation network of this framework adopts an asymmetric encoder–decoder structure. It captures and aggregates multiscale contextual information using the ASPP module and improves the classification and localization accuracy of the network using the global convolutional block. The discriminator network is an adversarial network that correctly discriminates the output of the generator and ground truth maps and computes multiscale

L_{1}

loss by fusing multiscale feature mappings. The segmentation network and the discriminator network are trained alternately on the WHU building dataset and the China typical cities building dataset. Experimental results show that the proposed ASGASN can accurately identify different types of buildings and achieve pixel-level high accuracy extraction of buildings. Additionally, compared to available deep learning models, ASGASN also achieved the highest accuracy performance (89.4% and 83.6% IoU on these two datasets, respectively). Full article

(This article belongs to the Special Issue Crowd-Sourced Data and Deep Learning in Remote Sensing: Methods and Applications)

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21 pages, 48622 KiB

Open AccessArticle

Integrating Data Modality and Statistical Learning Methods for Earthquake-Induced Landslide Susceptibility Mapping

by Zelang Miao, Renfeng Peng, Wei Wang, Qirong Li, Shuai Chen, Anshu Zhang, Minghui Pu, Ke Li, Qinqin Liu and Changhao Hu

Appl. Sci. 2022, 12(3), 1760; https://doi.org/10.3390/app12031760 - 08 Feb 2022

Cited by 5 | Viewed by 2054

Abstract

Earthquakes induce landslides worldwide every year that may cause massive fatalities and financial losses. Precise and timely landslide susceptibility mapping (LSM) is significant for landslide hazard assessment and mitigation in earthquake-affected areas. State-of-the-art LSM approaches connect causative factors from various sources without considering the fusion of different information at the data modal level. To exploit the complementary information of different modalities and boost LSM accuracy, this study presents a new LSM model that integrates data modality and machine learning methods. The presented method first groups causative factors into different modal types based on their intrinsic characteristics, followed by the calculation of the pairwise similarity of modal data. The similarities of different modalities are fused using nonlinear graph fusion to generate a unified graph, which is subsequently classified using different machine learning methods to produce final LSM. Experimental results suggest that the presented method achieves higher performance than existing LSM methods. This study provides a new solution for producing precise LSM from a fusion perspective that can be applied to minimize the potential landslide risk and for sustainable use of erosion-prone slopes. Full article

(This article belongs to the Special Issue Crowd-Sourced Data and Deep Learning in Remote Sensing: Methods and Applications)

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