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Novel Approaches for Remote Sensing Image Processing
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Novel Approaches for Remote Sensing Image Processing

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

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 10215

Special Issue Editors

Prof. Dr. Ke Wu

E-Mail Website
Guest Editor
School of Geophysics and Spatial Information, China University of Geosciences, Wuhan 430074, China
Interests: remote sensing technology; image processing; remote sensing applications; geosciences
Special Issues, Collections and Topics in MDPI journals
Dr. Yuxiang Zhang

E-Mail Website
Guest Editor
School of Geographies and Geomatics, China University of Geosciences, Wuhan, China
Interests: remote sensing classification; change detection and applications
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yi Wang

E-Mail Website1 Website2
Guest Editor
The School of Geophysics and Geomatics, China University of Geosciences, 388 Luomo Road, Wuhan 430074, China
Interests: hyperspectral image processing; artificial intelligence and machine learning in remote sensing; geological applications with remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is focused on hyperspectral image processing, image classification, unmixing, sub-pixel mapping, change detection on multi-remote sensing images, artificial intelligence and machine learning in remote sensing, and the geological applications of remote sensing. 

This Special Issue aims to collect original research and review articles related to approaches for remote sensing image processing. Themes of particular interest for this collection include, but are not limited to:

  • hyperspectral image processing
  • classification
  • sub-pixel mapping
  • change detection
  • artificial neural networks
  • geological remote sensing

Prof. Dr. Ke Wu
Dr. Yuxiang Zhang
Prof. Dr. Yi Wang
Guest Editors

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Published Papers (6 papers)

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Research

17 pages, 11189 KiB  
Article
Old Landslide Detection Using Optical Remote Sensing Images Based on Improved YOLOv8
by Yunlong Li, Mingtao Ding, Qian Zhang, Zhihui Luo, Wubiao Huang, Cancan Zhang and Hui Jiang
Appl. Sci. 2024, 14(3), 1100; https://doi.org/10.3390/app14031100 - 28 Jan 2024
Cited by 4 | Viewed by 1824
Abstract
The reactivation of old landslides can be triggered by heavy destructive earthquakes, heavy rainfall, and ongoing human activities, thereby resulting in the occurrence of secondary landslides. However, most existing models are designed for detecting nascent landslides and there are few algorithms for old [...] Read more.
The reactivation of old landslides can be triggered by heavy destructive earthquakes, heavy rainfall, and ongoing human activities, thereby resulting in the occurrence of secondary landslides. However, most existing models are designed for detecting nascent landslides and there are few algorithms for old landslide detection. In this paper, we introduce a novel landslide detection model known as YOLOv8-CW, built upon the YOLOv8 (You Only Look Once) architecture, to tackle the formidable challenge of identifying old landslides. We replace the Complete-IoU loss function in the original model with the Wise-IoU loss function to mitigate the impact of low-quality samples on model training and improve detection recall rate. We integrate a CBAM (Convolutional Block Attention Module) attention mechanism into our model to enhance detection accuracy. By focusing on the southwest river basin of the Sichuan–Tibet area, we collect 558 optical remote sensing images of old landslides in three channels from Google Earth and establish a dataset specifically for old landslide detection. Compared to the original model, our proposed YOLOv8-CW model achieves an increase in detection accuracy of 10.9%, recall rate of 6%, and F1 score from 0.66 to 0.74, respectively. These results demonstrate that our improved model exhibits excellent performance in detecting old landslides within the Sichuan–Tibet area. Full article
(This article belongs to the Special Issue Novel Approaches for Remote Sensing Image Processing)
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14 pages, 4054 KiB  
Article
Unsupervised Wavelet-Feature Correlation Ratio Markov Clustering Algorithm for Remotely Sensed Images
by Zhaohui Wang
Appl. Sci. 2024, 14(2), 767; https://doi.org/10.3390/app14020767 - 16 Jan 2024
Viewed by 958
Abstract
The spectrums of one type of object under different conditions have the same features (up, down, protruding, concave) at the same spectral positions, which can be used as primary parameters to evaluate the difference among remotely sensed pixels. The wavelet-feature correlation ratio Markov [...] Read more.
The spectrums of one type of object under different conditions have the same features (up, down, protruding, concave) at the same spectral positions, which can be used as primary parameters to evaluate the difference among remotely sensed pixels. The wavelet-feature correlation ratio Markov clustering algorithm (WFCRMCA) for remotely sensed data is proposed based on an accurate description of abrupt spectral features and an optimized Markov clustering in the wavelet feather space. The peak points can be captured and identified by applying a wavelet transform to spectral data. The correlation ratio between two samples is a statistical calculation of the matched peak point positions on the wavelet feature within an adjustable spectrum domain or a range of wavelet scales. The evenly sampled data can be used to create class centers, depending on the correlation ratio threshold at each Markov step, accelerating the clustering speed by avoiding the computation of Euclidean distance for traditional clustering algorithms, such as K-means and ISODATA. Markov clustering applies several strategies, such as a simulated annealing method and gradually shrinking the clustering size, to control the clustering convergence. It can quickly obtain the best class centers at each clustering temperature. The experimental results of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Thermal Mapping (TM) data have verified its acceptable clustering accuracy and high convergence velocity. Full article
(This article belongs to the Special Issue Novel Approaches for Remote Sensing Image Processing)
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19 pages, 6351 KiB  
Article
Point Cloud Deep Learning Network Based on Local Domain Multi-Level Feature
by Xianquan Han, Xijiang Chen, Hui Deng, Peng Wan and Jianzhou Li
Appl. Sci. 2023, 13(19), 10804; https://doi.org/10.3390/app131910804 - 28 Sep 2023
Cited by 1 | Viewed by 1749
Abstract
Point cloud deep learning networks have been widely applied in point cloud classification, part segmentation and semantic segmentation. However, current point cloud deep learning networks are insufficient in the local feature extraction of the point cloud, which affects the accuracy of point cloud [...] Read more.
Point cloud deep learning networks have been widely applied in point cloud classification, part segmentation and semantic segmentation. However, current point cloud deep learning networks are insufficient in the local feature extraction of the point cloud, which affects the accuracy of point cloud classification and segmentation. To address this issue, this paper proposes a local domain multi-level feature fusion point cloud deep learning network. First, dynamic graph convolutional operation is utilized to obtain the local neighborhood feature of the point cloud. Then, relation-shape convolution is used to extract a deeper-level edge feature of the point cloud, and max pooling is adopted to aggregate the edge features. Finally, point cloud classification and segmentation are realized based on global features and local features. We use the ModelNet40 and ShapeNet datasets to conduct the comparison experiment, which is a large-scale 3D CAD model dataset and a richly annotated, large-scale dataset of 3D shapes. For ModelNet40, the overall accuracy (OA) of the proposed method is similar to DGCNN, RS-CNN, PointConv and GAPNet, all exceeding 92%. Compared to PointNet, PointNet++, SO-Net and MSHANet, the OA of the proposed method is improved by 5%, 2%, 3% and 2.6%, respectively. For the ShapeNet dataset, the mean Intersection over Union (mIoU) of the part segmentation achieved by the proposed method is 86.3%, which is 2.9%, 1.4%, 1.7%, 1.7%, 1.2%, 0.1% and 1.0% higher than PointNet, RS-Net, SCN, SPLATNet, DGCNN, RS-CNN and LRC-NET, respectively. Full article
(This article belongs to the Special Issue Novel Approaches for Remote Sensing Image Processing)
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21 pages, 34356 KiB  
Article
Hyperspectral Point Cloud Projection for the Semantic Segmentation of Multimodal Hyperspectral and Lidar Data with Point Convolution-Based Deep Fusion Neural Networks
by Kevin T. Decker and Brett J. Borghetti
Appl. Sci. 2023, 13(14), 8210; https://doi.org/10.3390/app13148210 - 14 Jul 2023
Cited by 4 | Viewed by 1656
Abstract
The fusion of dissimilar data modalities in neural networks presents a significant challenge, particularly in the case of multimodal hyperspectral and lidar data. Hyperspectral data, typically represented as images with potentially hundreds of bands, provide a wealth of spectral information, while lidar data, [...] Read more.
The fusion of dissimilar data modalities in neural networks presents a significant challenge, particularly in the case of multimodal hyperspectral and lidar data. Hyperspectral data, typically represented as images with potentially hundreds of bands, provide a wealth of spectral information, while lidar data, commonly represented as point clouds with millions of unordered points in 3D space, offer structural information. The complementary nature of these data types presents a unique challenge due to their fundamentally different representations requiring distinct processing methods. In this work, we introduce an alternative hyperspectral data representation in the form of a hyperspectral point cloud (HSPC), which enables ingestion and exploitation with point cloud processing neural network methods. Additionally, we present a composite fusion-style, point convolution-based neural network architecture for the semantic segmentation of HSPC and lidar point cloud data. We investigate the effects of the proposed HSPC representation for both unimodal and multimodal networks ingesting a variety of hyperspectral and lidar data representations. Finally, we compare the performance of these networks against each other and previous approaches. This study paves the way for innovative approaches to multimodal remote sensing data fusion, unlocking new possibilities for enhanced data analysis and interpretation. Full article
(This article belongs to the Special Issue Novel Approaches for Remote Sensing Image Processing)
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22 pages, 10478 KiB  
Article
Object-Oriented Remote Sensing Image Change Detection Based on Color Co-Occurrence Matrix
by Zhu Zhu, Tinggang Zhou, Jinsong Chen, Xiaoli Li, Shanxin Guo, Longlong Zhao and Luyi Sun
Appl. Sci. 2023, 13(11), 6748; https://doi.org/10.3390/app13116748 - 1 Jun 2023
Cited by 2 | Viewed by 1557
Abstract
Aiming at the problem of misdetection caused by the traditional texture characteristic extraction model, which does not describe the correlation among multiple bands, an object-oriented remote sensing image change detection method based on a color co-occurrence matrix is proposed. First, the image is [...] Read more.
Aiming at the problem of misdetection caused by the traditional texture characteristic extraction model, which does not describe the correlation among multiple bands, an object-oriented remote sensing image change detection method based on a color co-occurrence matrix is proposed. First, the image is divided into multi-scale objects by graph-based superpixel segmentation, and the optimal scale is determined by the overall goodness F-measure (OGF). Then, except for the extraction of the spectral features, the multi-channel texture features based on the color co-occurrence matrix (CCM) are extracted to consider the correlation among multiple bands. To accurately find the representative features to overcome the impact of feature redundancy, a cumulative backward search strategy (CBSS) is further designed. Finally, the change detection is completed by inputting the difference image of dual time points to the trained random forest model. Taking Shenzhen and Dapeng as the study areas, with Sentinel-2 and Skysat images under different spatial resolutions, and the forest–bareland change type as an example, the effectiveness of the proposed algorithm is verified by qualitative and quantitative analyses. They show that the proposed algorithm can obtain higher detection accuracy than the texture features without band correlation. Full article
(This article belongs to the Special Issue Novel Approaches for Remote Sensing Image Processing)
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11 pages, 3270 KiB  
Communication
HGF Spatial–Spectral Fusion Method for Hyperspectral Images
by Pingjie Fu, Yuxuan Zhang, Fei Meng, Wei Zhang and Banghua Zhang
Appl. Sci. 2023, 13(1), 34; https://doi.org/10.3390/app13010034 - 20 Dec 2022
Cited by 1 | Viewed by 1477
Abstract
Quantitative studies on surface elements require satellite hyperspectral images with high spatial resolution. The identification of different surface elements requires different characteristic bands and their corresponding optimal spatial–spectral fusion methods. To address these problems, the harmonic analysis (HA), guided filtering, and Gram–Schmidt (GS) [...] Read more.
Quantitative studies on surface elements require satellite hyperspectral images with high spatial resolution. The identification of different surface elements requires different characteristic bands and their corresponding optimal spatial–spectral fusion methods. To address these problems, the harmonic analysis (HA), guided filtering, and Gram–Schmidt (GS) algorithms were integrated to propose a spatial–spectral fusion method called HGF. The fusion experiment and validation of the hyperspectral images of GaoFen-5 (GF-5) and ZY1-02D were conducted separately using the HGF method, and the fusion effect was evaluated in three band intervals according to the spectral response of the ground class. First, HGF was used to fuse the GF-5 and GaoFen-1 (GF-1) images, and the fusion effect was evaluated both qualitatively and quantitatively. Second, the optimal fusion method was selected for the corresponding characteristic bands of the different surface elements. Finally, the hyperspectral image obtained by ZY1-02D and multispectral image of Sentinel-2B were used for validation to improve the accuracy and efficiency of satellite hyperspectral images in quantitative studies. The results show that for further studies on soil, vegetation, and water bodies, the best fusion methods in the 390–730, 730–1400, and 1400–2260 nm intervals are the GS, HGF, and HGF algorithms, respectively. Further analysis showed that the HGF or GS methods can be selected for quantitative studies on vegetation and water bodies and that the HGF method exhibits outstanding advantages for quantitative analysis of each soil element. Full article
(This article belongs to the Special Issue Novel Approaches for Remote Sensing Image Processing)
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