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Active Learning Methods for Remote Sensing Image Classification

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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 (1 December 2023) | Viewed by 29196

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Special Issue Editors

Dr. Xiuyuan Zhang

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Guest Editor

College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
Interests: urban modeling; GeoAI; urban monitoring
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Shihong Du

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Guest Editor

School of Earth and Space Sciences, Peking University, Beijing 100871, China
Interests: image classification; spatial analysis; deep learning; sample learning; urban landscape
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Gong Cheng

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Guest Editor

School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
Interests: computer vision; pattern recognition; image processing; machine learning; deep learning; object detection and tracking; video analysis; remote sensing applications
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Special Issue Information

Dear Colleagues,

Remote sensing image classification (RSIC) plays a fundamental role in large-scale land resource surveys, ecological environment assessment, and human group behavior monitoring. Although past decades have witnessed great improvement in RSIC methods, especially for using deep learning neural networks, RSIC has encountered a bottleneck in that it requires huge samples and has poor accuracy in scenes where samples are scarce. Active learning serves as a possible solution for the issue, as it needs limited samples and can be adaptive to variant scenes. Accordingly, the progress of active learning methods will facilitate the development of RSIC.

We would like to invite you to contribute to this Special Issue on “Active Learning Methods for Remote Sensing Image Classification” which will gather insights and contributions to the field of active learning for RSIC. In the Special Issue, original research articles, reviews, and novel remote sensing data sets are welcome. Papers can be focused on but are not limited to:

Supervised/ unsupervised/ semi-supervised/ reinforcement/ transfer learning methods for RSIC;
Sample extraction, enhancement, and transformation for RSIC;
Deep learning frameworks and methods for RSIC;
Remote sensing data sets and benchmark for RSIC.

We look forward to receiving your contributions.

Dr. Xiuyuan Zhang
Prof. Dr. Shihong Du
Prof. Dr. Gong Cheng
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

Active learning
Transfer learning
Reinforcement learning
Deep learning
Image classification
Land cover/land use mapping
Samples

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Published Papers (6 papers)

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Research

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23 pages, 1781 KiB

Open AccessArticle

Attention-Enhanced Generative Adversarial Network for Hyperspectral Imagery Spatial Super-Resolution

by Baorui Wang, Yifan Zhang, Yan Feng, Bobo Xie and Shaohui Mei

Remote Sens. 2023, 15(14), 3644; https://doi.org/10.3390/rs15143644 - 21 Jul 2023

Cited by 2 | Viewed by 1804

Abstract

Hyperspectral imagery (HSI) with high spectral resolution contributes to better material discrimination, while the spatial resolution limited by the sensor technique prevents it from accurately distinguishing and analyzing targets. Though generative adversarial network-based HSI super-resolution methods have achieved remarkable progress, the problems of treating vital and unessential features equally in feature expression and training instability still exist. To address these issues, an attention-enhanced generative adversarial network (AEGAN) for HSI spatial super-resolution is proposed, which elaborately designs the enhanced spatial attention module (ESAM) and refined spectral attention module (RSAM) in the attention-enhanced generator. Specifically, the devised ESAM equipped with residual spatial attention blocks (RSABs) facilitates the generator that is more focused on the spatial parts of HSI that are difficult to produce and recover, and RSAM with spectral attention refines spectral interdependencies and guarantees the spectral consistency at the respective pixel positions. Additionally, an especial U-Net discriminator with spectral normalization is enclosed to pay more attention to the detailed informations of HSI and yield to stabilize the training. For producing more realistic and detailed super-resolved HSIs, an attention-enhanced generative loss is constructed to train and constrain the AEGAN model and investigate the high correlation of spatial context and spectral information in HSI. Moreover, to better simulate the complicated and authentic degradation, pseudo-real data are also generated with a high-order degradation model to train the overall network. Experiments on three benchmark HSI datasets illustrate the superior performance of the proposed AEGAN method in HSI spatial super-resolution over the compared methods. Full article

(This article belongs to the Special Issue Active Learning Methods for Remote Sensing Image Classification)

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17 pages, 16117 KiB

Open AccessArticle

Exploiting Superpixel-Based Contextual Information on Active Learning for High Spatial Resolution Remote Sensing Image Classification

by Jiechen Tang, Hengjian Tong, Fei Tong, Yun Zhang and Weitao Chen

Remote Sens. 2023, 15(3), 715; https://doi.org/10.3390/rs15030715 - 25 Jan 2023

Cited by 1 | Viewed by 2549

Abstract

Superpixel-based classification using Active Learning (AL) has shown great potential in high spatial resolution remote sensing image classification tasks. However, in existing superpixel-based classification models using AL, the expert labeling information is only used on the selected informative superpixel while its neighboring superpixels are ignored. Actually, as most superpixels are over-segmented, a ground object always contains multiple superpixels. Thus, the center superpixel tends to have the same label as its neighboring superpixels. In this paper, to make full use of the expert labeling information, a Similar Neighboring Superpixels Search and Labeling (SNSSL) method was proposed and used in the AL process. Firstly, we identify superpixels with certain categories and uncertain superpixels by supervised learning. Secondly, we use the active learning method to process those uncertain superpixels. In each round of AL, the expert labeling information is not only used to enrich the training set but also used to label the similar neighboring superpixels. Similar neighboring superpixels are determined by computing the similarity of two superpixels according to CIELAB Dominant Colors distance, Correlation distance, Angular Second Moment distance and Contrast distance. The final classification map is composed of the supervised learning classification map and the active learning with SNSSL classification map. To demonstrate the performance of the proposed SNSSL method, the experiments were conducted on images from two benchmark high spatial resolution remote sensing datasets. The experiment shows that overall accuracy, average accuracy and kappa coefficients of the classification using the SNSSL have been improved obviously compared with the classification without the SNSSL. Full article

(This article belongs to the Special Issue Active Learning Methods for Remote Sensing Image Classification)

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26 pages, 14141 KiB

Open AccessArticle

Consecutive Pre-Training: A Knowledge Transfer Learning Strategy with Relevant Unlabeled Data for Remote Sensing Domain

by Tong Zhang, Peng Gao, Hao Dong, Yin Zhuang, Guanqun Wang, Wei Zhang and He Chen

Remote Sens. 2022, 14(22), 5675; https://doi.org/10.3390/rs14225675 - 10 Nov 2022

Cited by 25 | Viewed by 3553

Abstract

Currently, under supervised learning, a model pre-trained by a large-scale nature scene dataset and then fine-tuned on a few specific task labeling data is the paradigm that has dominated knowledge transfer learning. Unfortunately, due to different categories of imaging data and stiff challenges of data annotation, there is not a large enough and uniform remote sensing dataset to support large-scale pre-training in the remote sensing domain (RSD). Moreover, pre-training models on large-scale nature scene datasets by supervised learning and then directly fine-tuning on diverse downstream tasks seems to be a crude method, which is easily affected by inevitable incorrect labeling, severe domain gaps and task-aware discrepancies. Thus, in this paper, considering the self-supervised pre-training and powerful vision transformer (ViT) architecture, a concise and effective knowledge transfer learning strategy called ConSecutive Pre-Training (CSPT) is proposed based on the idea of not stopping pre-training in natural language processing (NLP), which can gradually bridge the domain gap and transfer large-scale data knowledge to any specific domain (e.g., from nature scene domain to RSD) In addition, the proposed CSPT also can release the huge potential of unlabeled data for task-aware model training. Finally, extensive experiments were carried out on twelve remote sensing datasets involving three types of downstream tasks (e.g., scene classification, object detection and land cover classification) and two types of imaging data (e.g., optical and synthetic aperture radar (SAR)). The results show that by utilizing the proposed CSPT for task-aware model training, almost all downstream tasks in the RSD can outperform the previous knowledge transfer learning strategies based on model pre-training without any expensive manually labeling and even surpass the state-of-the-art (SOTA) performance without any careful network architecture designing. Full article

(This article belongs to the Special Issue Active Learning Methods for Remote Sensing Image Classification)

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18 pages, 6316 KiB

Open AccessArticle

A Framework Integrating DeeplabV3+, Transfer Learning, Active Learning, and Incremental Learning for Mapping Building Footprints

by Zhichao Li and Jinwei Dong

Remote Sens. 2022, 14(19), 4738; https://doi.org/10.3390/rs14194738 - 22 Sep 2022

Cited by 12 | Viewed by 3093

Abstract

Convolutional neural network (CNN)-based remote sensing (RS) image segmentation has become a widely used method for building footprint mapping. Recently, DeeplabV3+, an advanced CNN architecture, has shown satisfactory performance for building extraction in different urban landscapes. However, it faces challenges due to the large amount of labeled data required for model training and the extremely high costs associated with the annotation of unlabelled data. These challenges encouraged us to design a framework for building footprint mapping with fewer labeled data. In this context, the published studies on RS image segmentation are reviewed first, with a particular emphasis on the use of active learning (AL), incremental learning (IL), transfer learning (TL), and their integration for reducing the cost of data annotation. Based on the literature review, we defined three candidate frameworks by integrating AL strategies (i.e., margin sampling, entropy, and vote entropy), IL, TL, and DeeplabV3+. They examine the efficacy of AL, the efficacy of IL in accelerating AL performance, and the efficacy of both IL and TL in accelerating AL performance, respectively. Additionally, these frameworks enable the iterative selection of image tiles to be annotated, training and evaluation of DeeplabV3+, and quantification of the landscape features of selected image tiles. Then, all candidate frameworks were examined using WHU aerial building dataset as it has sufficient (i.e., 8188) labeled image tiles with representative buildings (i.e., various densities, areas, roof colors, and shapes of the building). The results support our theoretical analysis: (1) all three AL strategies reduced the number of image tiles by selecting the most informative image tiles, and no significant differences were observed in their performance; (2) image tiles with more buildings and larger building area were proven to be informative for the three AL strategies, which were prioritized during the data selection process; (3) IL can expedite model training by accumulating knowledge from chosen labeled tiles; (4) TL provides a better initial learner by incorporating knowledge from a pre-trained model; (5) DeeplabV3+ incorporated with IL, TL, and AL has the best performance in reducing the cost of data annotation. It achieved good performance (i.e., mIoU of 0.90) using only 10–15% of the sample dataset; DeeplabV3+ needs 50% of the sample dataset to realize the equivalent performance. The proposed frameworks concerning DeeplabV3+ and the results imply that integrating TL, AL, and IL in human-in-the-loop building extraction could be considered in real-world applications, especially for building footprint mapping. Full article

(This article belongs to the Special Issue Active Learning Methods for Remote Sensing Image Classification)

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17 pages, 9045 KiB

Open AccessArticle

Suggestive Data Annotation for CNN-Based Building Footprint Mapping Based on Deep Active Learning and Landscape Metrics

by Zhichao Li, Shuai Zhang and Jinwei Dong

Remote Sens. 2022, 14(13), 3147; https://doi.org/10.3390/rs14133147 - 30 Jun 2022

Cited by 9 | Viewed by 2644

Abstract

Convolutional neural network (CNN)-based very high-resolution (VHR) image segmentation has become a common way of extracting building footprints. Despite publicly available building datasets and pre-trained CNN models, it is still necessary to prepare sufficient labeled image tiles to train CNN models from scratch or update the parameters of pre-trained CNN models to extract buildings accurately in real-world applications, especially the large-scale building extraction, due to differences in landscapes and data sources. Deep active learning is an effective technique for resolving this issue. This study proposes a framework integrating two state-of-the-art (SOTA) models, U-Net and DeeplabV3+, three commonly used active learning strategies, (i.e., margin sampling, entropy, and vote entropy), and landscape characterization to illustrate the performance of active learning in reducing the effort of data annotation, and then understand what kind of image tiles are more advantageous for CNN-based building extraction. The framework enables iteratively selecting the most informative image tiles from the unlabeled dataset for data annotation, training the CNN models, and analyzing the changes in model performance. It also helps us to understand the landscape features of iteratively selected image tiles via active learning by considering building as the focal class and computing the percent, the number of patches, edge density, and landscape shape index of buildings based on labeled tiles in each selection. The proposed method was evaluated on two benchmark building datasets, WHU satellite dataset II and WHU aerial dataset. Models in each iteration were trained from scratch on all labeled tiles. Experimental results based on the two datasets indicate that, for both U-Net and DeeplabV3+, the three active learning strategies can reduce the number of image tiles to be annotated and achieve good model performance with fewer labeled image tiles. Moreover, image tiles with more building patches, larger areas of buildings, longer edges of buildings, and more dispersed building distribution patterns were more effective for model training. The study not only provides a framework to reduce the data annotation efforts in CNN-based building extraction but also summarizes the preliminary suggestions for data annotation, which could facilitate and guide data annotators in real-world applications. Full article

(This article belongs to the Special Issue Active Learning Methods for Remote Sensing Image Classification)

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Review

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27 pages, 1479 KiB

Open AccessReview

A Review of Deep Learning in Multiscale Agricultural Sensing

by Dashuai Wang, Wujing Cao, Fan Zhang, Zhuolin Li, Sheng Xu and Xinyu Wu

Remote Sens. 2022, 14(3), 559; https://doi.org/10.3390/rs14030559 - 25 Jan 2022

Cited by 116 | Viewed by 13508

Abstract

Population growth, climate change, and the worldwide COVID-19 pandemic are imposing increasing pressure on global agricultural production. The challenge of increasing crop yield while ensuring sustainable development of environmentally friendly agriculture is a common issue throughout the world. Autonomous systems, sensing technologies, and artificial intelligence offer great opportunities to tackle this issue. In precision agriculture (PA), non-destructive and non-invasive remote and proximal sensing methods have been widely used to observe crops in visible and invisible spectra. Nowadays, the integration of high-performance imagery sensors (e.g., RGB, multispectral, hyperspectral, thermal, and SAR) and unmanned mobile platforms (e.g., satellites, UAVs, and terrestrial agricultural robots) are yielding a huge number of high-resolution farmland images, in which rich crop information is compressed. However, this has been accompanied by challenges, i.e., ways to swiftly and efficiently making full use of these images, and then, to perform fine crop management based on information-supported decision making. In the past few years, deep learning (DL) has shown great potential to reshape many industries because of its powerful capabilities of feature learning from massive datasets, and the agriculture industry is no exception. More and more agricultural scientists are paying attention to applications of deep learning in image-based farmland observations, such as land mapping, crop classification, biotic/abiotic stress monitoring, and yield prediction. To provide an update on these studies, we conducted a comprehensive investigation with a special emphasis on deep learning in multiscale agricultural remote and proximal sensing. Specifically, the applications of convolutional neural network-based supervised learning (CNN-SL), transfer learning (TL), and few-shot learning (FSL) in crop sensing at land, field, canopy, and leaf scales are the focus of this review. We hope that this work can act as a reference for the global agricultural community regarding DL in PA and can inspire deeper and broader research to promote the evolution of modern agriculture. Full article

(This article belongs to the Special Issue Active Learning Methods for Remote Sensing Image Classification)

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