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Explainable Artificial Intelligence (XAI) in Remote Sensing Big Data

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

Deadline for manuscript submissions: closed (15 August 2023) | Viewed by 37417

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Prof. Dr. Lizhe Wang

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Guest Editor

School of Computer Science, China University of Geosciences, Wuhan 430074, China
Interests: remote sensing image processing

Prof. Dr. Jun Li

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Guest Editor

School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China
Interests: remote sensing image processing; data fusion; hyperspectral image classification
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Dr. Peng Liu

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Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: remote sensing image fusion; information extraction on remote sensing image; remote sensing big data; applications of artificial intelligence in remote sensing field
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the advent of the era of remote sensing big data, artificial intelligence (AI) has spread to almost all corners of the various remote sensing applications. In many cases, characteristics of remote sensing big data, such as multi-source, multi-scale, high-dimensional, dynamic state, isomer, and non-linear characteristics etc., are well learned by advanced AI algorithms. Data-driven methods, especially deep learning models, have achieved state-of-the-art results for most remote sensing image processing tasks (object detection, segmentation etc.) and even some remote sensing inverse tasks (atmosphere, vegetarian etc.). By using large labeled datasets, we can often make highly accurate predictions on remote sensing data.

However, current data-driven AI did not provide us clear physical or cognitive meaning of the internal features and representations of remote sensing big data. Most deep learning techniques do not disclose how the data features take effect and why the predictions are taken. Remote sensing big data exacerbated the problem of in-transparency and in-explainability of current AI. It is becoming a barrier between the latest AI techniques and some remote sensing applications. Many scientists in hydrology remote sensing, atmospheric remote sensing, and ocean remote sensing etc. even do not believe the prediction results from deep learning, since these communities are more inclined to believe models with a clear physical meaning. Explainable artificial intelligence (XAI) is widely acknowledged as a crucial step to the practical deployment of AI models in remote sensing communities.

This Special Issue seeks contributions on theory or applications of XAI in remote sensing big data. In particular, we seek research articles on the applications whose physical or cognitive models are represented by XAI, or articles addressing how the remote sensing big data drive the model based on XAI.

Topics of interest include, but are not limited to:

Theoretical and philosophical foundations of XAI
XAI for remote sensing image visual tasks such object detection, segmentation, change detection, fusion etc.
XAI for terrestrial remote sensing, atmospheric remote sensing, and ocean remote sensing etc.
XAI for unmanned aerial vehicle (UAV) remote sensing big data
XAI for simultaneous localization and mapping (SLAM) with remote sensing big data
XAI for global scale inversion problems, such as biomass, thermal emission, vegetarian etc.
XAI for high performance computation on large-scale remote sensing applications

Prof. Dr. Lizhe Wang
Prof. Dr. Jun Li
Dr. Peng Liu
Guest Editors

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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

Explainable Artificial Intelligence (XAI)
Remote Sensing Big Data
Semantic Interpretation
Deep Feature Understanding
Data-driven
Large Scale Inversion Problems
Global (or Local) Surrogate Models
Feature Importance
Influential Instances
Accumulated Local Effects

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Explainable Artificial Intelligence (XAI) in Remote Sensing Big Data (Second Edition) in Remote Sensing (2 articles)

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Editorial

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4 pages, 180 KiB

Open AccessEditorial

Unlocking the Potential of Explainable Artificial Intelligence in Remote Sensing Big Data

by Peng Liu, Lizhe Wang and Jun Li

Remote Sens. 2023, 15(23), 5448; https://doi.org/10.3390/rs15235448 - 22 Nov 2023

Viewed by 1705

Abstract

In the ever-evolving landscape of artificial intelligence and big data, the concept of explainable artificial intelligence (XAI) [...] Full article

(This article belongs to the Special Issue Explainable Artificial Intelligence (XAI) in Remote Sensing Big Data)

Research

Jump to: Editorial

27 pages, 10679 KiB

Open AccessArticle

Agricultural Land Cover Mapping through Two Deep Learning Models in the Framework of EU’s CAP Activities Using Sentinel-2 Multitemporal Imagery

by Eleni Papadopoulou, Giorgos Mallinis, Sofia Siachalou, Nikos Koutsias, Athanasios C. Thanopoulos and Georgios Tsaklidis

Remote Sens. 2023, 15(19), 4657; https://doi.org/10.3390/rs15194657 - 22 Sep 2023

Cited by 1 | Viewed by 2055

Abstract

The images of the Sentinel-2 constellation can help the verification process of farmers’ declarations, providing, among other things, accurate spatial explicit maps of the agricultural land cover. The aim of the study is to design, develop, and evaluate two deep learning (DL) architectures tailored for agricultural land cover and crop type mapping. The focus is on a detailed class scheme encompassing fifteen distinct classes, utilizing Sentinel-2 imagery acquired on a monthly basis throughout the year. The study’s geographical scope covers a diverse rural area in North Greece, situated within southeast Europe. These architectures are a Temporal Convolutional Neural Network (CNN) and a combination of a Recurrent and a 2D Convolutional Neural Network (R-CNN), and their accuracy is compared to the well-established Random Forest (RF) machine learning algorithm. The comparative approach is not restricted to simply presenting the results given by classification metrics, but it also assesses the uncertainty of the classification results using an entropy measure and the spatial distribution of the classification errors. Furthermore, the issue of sampling strategy for the extraction of the training set is highlighted, targeting the efficient handling of both the imbalance of the dataset and the spectral variability of instances among classes. The two developed deep learning architectures performed equally well, presenting an overall accuracy of 90.13% (Temporal CNN) and 90.18% (R-CNN), higher than the 86.31% overall accuracy of the RF approach. Finally, the Temporal CNN method presented a lower entropy value (6.63%), compared both to R-CNN (7.76%) and RF (28.94%) methods, indicating that both DL approaches should be considered for developing operational EO processing workflows. Full article

(This article belongs to the Special Issue Explainable Artificial Intelligence (XAI) in Remote Sensing Big Data)

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17 pages, 4859 KiB

Open AccessArticle

Enhancing Land Cover Mapping and Monitoring: An Interactive and Explainable Machine Learning Approach Using Google Earth Engine

by Haifei Chen, Liping Yang and Qiusheng Wu

Remote Sens. 2023, 15(18), 4585; https://doi.org/10.3390/rs15184585 - 18 Sep 2023

Cited by 9 | Viewed by 5866

Abstract

Artificial intelligence (AI) and machine learning (ML) have been applied to solve various remote sensing problems. To fully leverage the power of AI and ML to tackle impactful remote sensing problems, it is essential to enable researchers and practitioners to understand how AI and ML models actually work and thus to improve the model performance strategically. Accurate and timely land cover maps are essential components for informed land management decision making. To address the ever-increasing need for high spatial and temporal resolution maps, this paper developed an interactive and open-source online tool, in Python, to help interpret and improve the ML models used for land cover mapping with Google Earth Engine (GEE). The tool integrates the workflow of both land cover classification and land cover change dynamics, which requires the generation of a time series of land cover maps. Three feature importance metrics are reported, including impurity-based, permutation-based, and SHAP (Shapley additive explanations) value-based feature importance. Two case studies are presented to showcase the tool’s capability and ease of use, enabling a globally accessible and free convergent application of remote sensing technologies. This tool may inspire researchers to facilitate explainable AI (XAI)-empowered remote sensing applications with GEE. Full article

(This article belongs to the Special Issue Explainable Artificial Intelligence (XAI) in Remote Sensing Big Data)

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27 pages, 8544 KiB

Open AccessArticle

An Unsupervised Feature Extraction Using Endmember Extraction and Clustering Algorithms for Dimension Reduction of Hyperspectral Images

by Sayyed Hamed Alizadeh Moghaddam, Saeed Gazor, Fahime Karami, Meisam Amani and Shuanggen Jin

Remote Sens. 2023, 15(15), 3855; https://doi.org/10.3390/rs15153855 - 3 Aug 2023

Cited by 2 | Viewed by 2233

Abstract

Hyperspectral images (HSIs) provide rich spectral information, facilitating many applications, including landcover classification. However, due to the high dimensionality of HSIs, landcover mapping applications usually suffer from the curse of dimensionality, which degrades the efficiency of supervised classifiers due to insufficient training samples. Feature extraction (FE) is a popular dimension reduction strategy for this issue. This paper proposes an unsupervised FE algorithm that involves extracting endmembers and clustering spectral bands. The proposed method first extracts existing endmembers from the HSI data via a vertex component analysis method. Using these endmembers, it subsequently constructs a prototype space (PS) in which each spectral band is represented by a point. Similar/correlated bands in the PS remain near one another, forming several clusters. Therefore, our method, in the next step, clusters spectral bands into multiple clusters via K-means and fuzzy C-means algorithms. Finally, it combines all the spectral bands in the same cluster using a weighted average operator to decrease the high dimensionality. The extracted features were evaluated by applying an SVM classifier. The experimental results confirmed the superior performance of the proposed method compared with five state-of-the-art dimension reduction algorithms. It outperformed these algorithms in terms of classification accuracy on three widely used hyperspectral images (Indian Pines, KSC, and Pavia Centre). The suggested technique also showed comparable or even stronger performance (up to 9% improvement) compared with its supervised competitor. Notably, the proposed method exhibited higher accuracy even when only a limited number of training samples were available for supervised classification. Using only five training samples per class for the KSC and Pavia Centre datasets, our method’s classification accuracy was higher than that of its best-performing unsupervised competitors by about 7% and 1%, respectively, in our experiments. Full article

(This article belongs to the Special Issue Explainable Artificial Intelligence (XAI) in Remote Sensing Big Data)

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11 pages, 1803 KiB

Open AccessArticle

A Marine Small-Targets Classification Algorithm Based on Improved Convolutional Neural Networks

by Huinan Guo and Long Ren

Remote Sens. 2023, 15(11), 2917; https://doi.org/10.3390/rs15112917 - 3 Jun 2023

Cited by 1 | Viewed by 1640

Abstract

Deep learning, especially convolutional neural network (CNN) techniques, has been shown to have superior performance in ship classification, as have small-target recognition studies in safety inspections of hydraulic structures such as ports and dams. High-resolution synthetic aperture radar (SAR)-based maritime ship classification plays an increasingly important role in marine surveillance, marine rescue, and maritime ship management. To improve ship classification accuracy and training efficiency, we proposed a CNN-based ship classification method. Firstly, the image characteristics of different ship structures and the materials of ship SAR images were analyzed. We then constructed a ship SAR image dataset and performed preprocessing operations such as averaging. Combined with a classic neural network structure, we created a new convolutional module, namely, the Inception-Residual Controller (IRC) module. A convolutional neural network was built based on the IRC module to extract image features and establish a ship classification model. Finally, we conducted simulation experiments for ship classification and analyzed the experimental results for comparison. The experimental results showed that the average accuracy of ship classification of the model in this paper reached 98.71%, which was approximately 3% more accurate than the traditional network model and approximately 1% more accurate compared with other recently improved models. The new module also performed well in evaluation metrics, such as the recall rate, with accurate classifications. The model could satisfactorily describe different ship types. Therefore, it could be applied to marine ship classification management with the possibility of being extended to hydraulic building target recognition tasks. Full article

(This article belongs to the Special Issue Explainable Artificial Intelligence (XAI) in Remote Sensing Big Data)

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24 pages, 3447 KiB

Open AccessArticle

UAV Aerial Image Generation of Crucial Components of High-Voltage Transmission Lines Based on Multi-Level Generative Adversarial Network

by Jinyu Wang, Yingna Li and Wenxiang Chen

Remote Sens. 2023, 15(5), 1412; https://doi.org/10.3390/rs15051412 - 2 Mar 2023

Cited by 9 | Viewed by 2132

Abstract

With the aim of improving the image quality of the crucial components of transmission lines taken by unmanned aerial vehicles (UAV), a priori work on the defective fault location of high-voltage transmission lines has attracted great attention from researchers in the UAV field. In recent years, generative adversarial nets (GAN) have achieved good results in image generation tasks. However, the generation of high-resolution images with rich semantic details from complex backgrounds is still challenging. Therefore, we propose a novel GANs-based image generation model to be used for the critical components of power lines. However, to solve the problems related to image backgrounds in public data sets, considering that the image background of the common data set CPLID (Chinese Power Line Insulator Dataset) is simple. However, it cannot fully reflect the complex environments of transmission line images; therefore, we established an image data set named “KCIGD” (The Key Component Image Generation Dataset), which can be used for model training. CFM-GAN (GAN networks based on coarse–fine-grained generators and multiscale discriminators) can generate the images of the critical components of transmission lines with rich semantic details and high resolutions. CFM-GAN can provide high-quality image inputs for transmission line fault detection and line inspection models to guarantee the safe operation of power systems. Additionally, we can use these high-quality images to expand the data set. In addition, CFM-GAN consists of two generators and multiple discriminators, which can be flexibly applied to image generation tasks in other scenarios. We introduce a penalty mechanism-related Monte Carlo search (MCS) approach in the CFM-GAN model to introduce more semantic details in the generated images. Moreover, we presented a multiscale discriminator structure according to the multitask learning mechanisms to effectively enhance the quality of the generated images. Eventually, the experiments using the CFM-GAN model on the KCIGD dataset and the publicly available CPLID indicated that the model used in this work outperformed existing mainstream models in improving image resolution and quality. Full article

(This article belongs to the Special Issue Explainable Artificial Intelligence (XAI) in Remote Sensing Big Data)

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26 pages, 17562 KiB

Open AccessArticle

Unboxing the Black Box of Attention Mechanisms in Remote Sensing Big Data Using XAI

by Erfan Hasanpour Zaryabi, Loghman Moradi, Bahareh Kalantar, Naonori Ueda and Alfian Abdul Halin

Remote Sens. 2022, 14(24), 6254; https://doi.org/10.3390/rs14246254 - 9 Dec 2022

Cited by 12 | Viewed by 3122

Abstract

This paper presents exploratory work looking into the effectiveness of attention mechanisms (AMs) in improving the task of building segmentation based on convolutional neural network (CNN) backbones. Firstly, we evaluate the effectiveness of CNN-based architectures with and without AMs. Secondly, we attempt to interpret the results produced by the CNNs using explainable artificial intelligence (XAI) methods. We compare CNNs with and without (vanilla) AMs for buildings detection. Five metrics are calculated, namely F1-score, precision, recall, intersection over union (IoU) and overall accuracy (OA). For the XAI portion of this work, the methods of Layer Gradient X activation and Layer DeepLIFT are used to explore the internal AMs and their overall effects on the network. Qualitative evaluation is based on color-coded value attribution to assess how the AMs facilitate the CNNs in performing buildings classification. We look at the effects of employing five AM algorithms, namely (i) squeeze and excitation (SE), (ii) convolution attention block module (CBAM), (iii) triplet attention, (iv) shuffle attention (SA), and (v) efficient channel attention (ECA). Experimental results indicate that AMs generally and markedly improve the quantitative metrics, with the attribution visualization results of XAI methods agreeing with the quantitative metrics. Full article

(This article belongs to the Special Issue Explainable Artificial Intelligence (XAI) in Remote Sensing Big Data)

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25 pages, 5836 KiB

Open AccessArticle

Bidirectional Flow Decision Tree for Reliable Remote Sensing Image Scene Classification

by Jiangfan Feng, Dini Wang and Zhujun Gu

Remote Sens. 2022, 14(16), 3943; https://doi.org/10.3390/rs14163943 - 14 Aug 2022

Cited by 5 | Viewed by 2816

Abstract

Remote sensing image scene classification (RSISC), which aims to classify scene categories for remote sensing imagery, has broad applications in various fields. Recent deep learning (DL) successes have led to a new wave of RSISC applications; however, they lack explainability and trustworthiness. Here, we propose a bidirectional flow decision tree (BFDT) module to create a reliable RS scene classification framework. Our algorithm combines BFDT and Convolutional Neural Networks (CNNs) to make the decision process easily interpretable. First, we extract multilevel feature information from the pretrained CNN model, which provides the basis for constructing the subsequent hierarchical structure. Then the model uses the discriminative nature of scene features at different levels to gradually refine similar subsets and learn the interclass hierarchy. Meanwhile, the last fully connected layer embeds decision rules for the decision tree from the bottom up. Finally, the cascading softmax loss is used to train and learn the depth features based on the hierarchical structure formed by the tree structure that contains rich remote sensing information. We also discovered that superclass results can be obtained well for unseen classes due to its unique tree structure hierarchical property, which results in our model having a good generalization effect. The experimental results align with theoretical predictions using three popular datasets. Our proposed framework provides explainable results, leading to correctable and trustworthy approaches. Full article

(This article belongs to the Special Issue Explainable Artificial Intelligence (XAI) in Remote Sensing Big Data)

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20 pages, 1865 KiB

Open AccessArticle

Novel Insights in Spatial Epidemiology Utilizing Explainable AI (XAI) and Remote Sensing

by Anastasios Temenos, Ioannis N. Tzortzis, Maria Kaselimi, Ioannis Rallis, Anastasios Doulamis and Nikolaos Doulamis

Remote Sens. 2022, 14(13), 3074; https://doi.org/10.3390/rs14133074 - 26 Jun 2022

Cited by 26 | Viewed by 4488

Abstract

The COVID-19 pandemic has affected many aspects of human life around the world, due to its tremendous outcomes on public health and socio-economic activities. Policy makers have tried to develop efficient responses based on technologies and advanced pandemic control methodologies, to limit the wide spreading of the virus in urban areas. However, techniques such as social isolation and lockdown are short-term solutions that minimize the spread of the pandemic in cities and do not invert long-term issues that derive from climate change, air pollution and urban planning challenges that enhance the spreading ability. Thus, it seems crucial to understand what kind of factors assist or prevent the wide spreading of the virus. Although AI frameworks have a very efficient predictive ability as data-driven procedures, they often struggle to identify strong correlations among multidimensional data and provide robust explanations. In this paper, we propose the fusion of a heterogeneous, spatio-temporal dataset that combine data from eight European cities spanning from 1 January 2020 to 31 December 2021 and describe atmospheric, socio-economic, health, mobility and environmental factors all related to potential links with COVID-19. Remote sensing data are the key solution to monitor the availability on public green spaces between cities in the study period. So, we evaluate the benefits of NIR and RED bands of satellite images to calculate the NDVI and locate the percentage in vegetation cover on each city for each week of our 2-year study. This novel dataset is evaluated by a tree-based machine learning algorithm that utilizes ensemble learning and is trained to make robust predictions on daily cases and deaths. Comparisons with other machine learning techniques justify its robustness on the regression metrics RMSE and MAE. Furthermore, the explainable frameworks SHAP and LIME are utilized to locate potential positive or negative influence of the factors on global and local level, with respect to our model’s predictive ability. A variation of SHAP, namely treeSHAP, is utilized for our tree-based algorithm to make fast and accurate explanations. Full article

(This article belongs to the Special Issue Explainable Artificial Intelligence (XAI) in Remote Sensing Big Data)

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23 pages, 10527 KiB

Open AccessArticle

Landslide Extraction from High-Resolution Remote Sensing Imagery Using Fully Convolutional Spectral–Topographic Fusion Network

by Wei Xia, Jun Chen, Jianbo Liu, Caihong Ma and Wei Liu

Remote Sens. 2021, 13(24), 5116; https://doi.org/10.3390/rs13245116 - 16 Dec 2021

Cited by 18 | Viewed by 3641

Abstract

Considering the complexity of landslide hazards, their manual investigation lacks efficiency and is time-consuming, especially in high-altitude plateau areas. Therefore, extracting landslide information using remote sensing technology has great advantages. In this study, comprehensive research was carried out on the landslide features of high-resolution remote sensing images on the Mangkam dataset. Based on the idea of feature-driven classification, the landslide extraction model of a fully convolutional spectral–topographic fusion network (FSTF-Net) based on a deep convolutional neural network of multi-source data fusion is proposed, which takes into account the topographic factor (slope and aspect) and the normalized difference vegetation index (NDVI) as multi-source data input by which to train the model. In this paper, a high-resolution remote sensing image classification method based on a fully convolutional network was used to extract the landslide information, thereby realizing the accurate extraction of the landslide and surrounding ground-object information. With Mangkam County in the southeast of the Qinghai–Tibet Plateau China as the study area, the proposed method was evaluated based on the high-precision digital elevation model (DEM) generated from stereoscopic images of Resources Satellite-3 and multi-source high-resolution remote sensing image data (Beijing-2, Worldview-3, and SuperView-1). Results show that our method had a landslide detection precision of 0.85 and an overall classification accuracy of 0.89. Compared with the latest DeepLab_v3+, our model increases the landslide detection precision by 5%. Thus, the proposed FSTF-Net model has high reliability and robustness. Full article

(This article belongs to the Special Issue Explainable Artificial Intelligence (XAI) in Remote Sensing Big Data)

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23 pages, 8342 KiB

Open AccessArticle

CCT: Conditional Co-Training for Truly Unsupervised Remote Sensing Image Segmentation in Coastal Areas

by Bo Fang, Gang Chen, Jifa Chen, Guichong Ouyang, Rong Kou and Lizhe Wang

Remote Sens. 2021, 13(17), 3521; https://doi.org/10.3390/rs13173521 - 5 Sep 2021

Cited by 7 | Viewed by 3181

Abstract

As the fastest growing trend in big data analysis, deep learning technology has proven to be both an unprecedented breakthrough and a powerful tool in many fields, particularly for image segmentation tasks. Nevertheless, most achievements depend on high-quality pre-labeled training samples, which are labor-intensive and time-consuming. Furthermore, different from conventional natural images, coastal remote sensing ones generally carry far more complicated and considerable land cover information, making it difficult to produce pre-labeled references for supervised image segmentation. In our research, motivated by this observation, we take an in-depth investigation on the utilization of neural networks for unsupervised learning and propose a novel method, namely conditional co-training (CCT), specifically for truly unsupervised remote sensing image segmentation in coastal areas. In our idea, a multi-model framework consisting of two parallel data streams, which are superpixel-based over-segmentation and pixel-level semantic segmentation, is proposed to simultaneously perform the pixel-level classification. The former processes the input image into multiple over-segments, providing self-constrained guidance for model training. Meanwhile, with this guidance, the latter continuously processes the input image into multi-channel response maps until the model converges. Incentivized by multiple conditional constraints, our framework learns to extract high-level semantic knowledge and produce full-resolution segmentation maps without pre-labeled ground truths. Compared to the black-box solutions in conventional supervised learning manners, this method is of stronger explainability and transparency for its specific architecture and mechanism. The experimental results on two representative real-world coastal remote sensing datasets of image segmentation and the comparison with other state-of-the-art truly unsupervised methods validate the plausible performance and excellent efficiency of our proposed CCT. Full article

(This article belongs to the Special Issue Explainable Artificial Intelligence (XAI) in Remote Sensing Big Data)

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