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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 18716

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Prof. Dr. Michalis Savelonas

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Department of Computer Science and Biomedical Informatics, School of Science, Campus of Lamia, University of Thessaly, GR-35131 Lamia, Greece
Interests: pattern recognition; computer vision; expert systems
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Special Issue Information

Dear Colleagues,

The progress of remote sensing imaging has been closely associated with computer vision and pattern recognition. Land cover mapping, target detection, change detection and boundary extraction, as well as pattern inference from time-series of imaging data, pose challenges for traditional image computer vision and pattern recognition tasks, such as image clustering, classification and segmentation. Engineered feature vectors have been applied for the analysis of optical, SAR, multispectral or hyperspectral images, as well as point clouds. Later, visual dictionaries, the so-called bag-of-visual-words models (BoVW), incorporated the statistics associated with each problem at hand. In the context of semantic segmentation, numerous approaches, including active contours, Markov random fields (MRF) and superpixels, have been combined with descriptors or BoVWs and applied on remote sensing data. The rapid evolution of powerful GPUs and the availability of large datasets aided extraordinary advances in deep-learning-based computer vision, starting from the performance breakthrough of AlexNet in ILSVRC 2012. This new paradigm reinvigorated the interest in computational tools for remote sensing. Numerous works are regularly published for the analysis of remote sensing data with deep neural networks. Convolutional neural networks (CNNs) and derivative architectures, such as VGG, ResNet and Inception, play a prominent role in this direction. Another deep learning branch in remote sensing consists of applications of recurrent neural networks (RNNs), including long short-term memory (LSTM) networks, on time-series of images.

This Special Issue aims to explore state-of-the-art computer vision and pattern recognition applications in remote sensing. Research contributions, including surveys, are welcome. In particular, novel contributions that cover, but are not limited to, the following application domains are welcome:

Land cover mapping;
Target detection;
Change detection;
Boundary extraction;
Pattern analysis on time-series of imaging data;
Works carried out at all scales and in all environments, including surveys and comparative studies, as well as the description of new methodologies, best practices, advantages and limitations for computational tools in remote sensing.

Prof. Dr. Michalis Savelonas
Guest Editor

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

land cover mapping
target detection
change detection
point-clouds
LiDAR
SAR imaging
multispectral imaging
hyperspectral imaging
computer vision
pattern recognition
deep learning
machine learning
image analysis
3D shape analysis
time-series analysis

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Published Papers (7 papers)

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Research

30 pages, 25238 KiB

Open AccessArticle

Unsupervised Color-Based Flood Segmentation in UAV Imagery

by Georgios Simantiris and Costas Panagiotakis

Remote Sens. 2024, 16(12), 2126; https://doi.org/10.3390/rs16122126 - 12 Jun 2024

Viewed by 707

Abstract

We propose a novel unsupervised semantic segmentation method for fast and accurate flood area detection utilizing color images acquired from unmanned aerial vehicles (UAVs). To the best of our knowledge, this is the first fully unsupervised method for flood area segmentation in color images captured by UAVs, without the need of pre-disaster images. The proposed framework addresses the problem of flood segmentation based on parameter-free calculated masks and unsupervised image analysis techniques. First, a fully unsupervised algorithm gradually excludes areas classified as non-flood, utilizing calculated masks over each component of the LAB colorspace, as well as using an RGB vegetation index and the detected edges of the original image. Unsupervised image analysis techniques, such as distance transform, are then applied, producing a probability map for the location of flooded areas. Finally, flood detection is obtained by applying hysteresis thresholding segmentation. The proposed method is tested and compared with variations and other supervised methods in two public datasets, consisting of 953 color images in total, yielding high-performance results, with 87.4% and 80.9% overall accuracy and F1-score, respectively. The results and computational efficiency of the proposed method show that it is suitable for onboard data execution and decision-making during UAV flights. Full article

(This article belongs to the Special Issue Computer Vision-Based Methods and Tools in Remote Sensing)

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20 pages, 6258 KiB

Open AccessArticle

Locating and Grading of Lidar-Observed Aircraft Wake Vortex Based on Convolutional Neural Networks

by Xinyu Zhang, Hongwei Zhang, Qichao Wang, Xiaoying Liu, Shouxin Liu, Rongchuan Zhang, Rongzhong Li and Songhua Wu

Remote Sens. 2024, 16(8), 1463; https://doi.org/10.3390/rs16081463 - 20 Apr 2024

Viewed by 825

Abstract

Aircraft wake vortices are serious threats to aviation safety. The Pulsed Coherent Doppler Lidar (PCDL) has been widely used in the observation of aircraft wake vortices due to its advantages of high spatial-temporal resolution and high precision. However, the post-processing algorithms require significant computing resources, which cannot achieve the real-time detection of a wake vortex (WV). This paper presents an improved Convolutional Neural Network (CNN) method for WV locating and grading based on PCDL data to avoid the influence of unstable ambient wind fields on the localization and classification results of WV. Typical WV cases are selected for analysis, and the WV locating and grading models are validated on different test sets. The consistency of the analytical algorithm and the CNN algorithm is verified. The results indicate that the improved CNN method achieves satisfactory recognition accuracy with higher efficiency and better robustness, especially in the case of strong turbulence, where the CNN method recognizes the wake vortex while the analytical method cannot. The improved CNN method is expected to be applied to optimize the current aircraft spacing criteria, which is promising in terms of aviation safety and economic benefit improvement. Full article

(This article belongs to the Special Issue Computer Vision-Based Methods and Tools in Remote Sensing)

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12 pages, 3758 KiB

Open AccessArticle

Fusion of Single and Integral Multispectral Aerial Images

by Mohamed Youssef and Oliver Bimber

Remote Sens. 2024, 16(4), 673; https://doi.org/10.3390/rs16040673 - 14 Feb 2024

Viewed by 1648

Abstract

An adequate fusion of the most significant salient information from multiple input channels is essential for many aerial imaging tasks. While multispectral recordings reveal features in various spectral ranges, synthetic aperture sensing makes occluded features visible. We present a first and hybrid (model- and learning-based) architecture for fusing the most significant features from conventional aerial images with the ones from integral aerial images that are the result of synthetic aperture sensing for removing occlusion. It combines the environment’s spatial references with features of unoccluded targets that would normally be hidden by dense vegetation. Our method outperforms state-of-the-art two-channel and multi-channel fusion approaches visually and quantitatively in common metrics, such as mutual information, visual information fidelity, and peak signal-to-noise ratio. The proposed model does not require manually tuned parameters, can be extended to an arbitrary number and arbitrary combinations of spectral channels, and is reconfigurable for addressing different use cases. We demonstrate examples for search and rescue, wildfire detection, and wildlife observation. Full article

(This article belongs to the Special Issue Computer Vision-Based Methods and Tools in Remote Sensing)

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18 pages, 9369 KiB

Open AccessArticle

Quantifying the Loss of Coral from a Bleaching Event Using Underwater Photogrammetry and AI-Assisted Image Segmentation

by Kai L. Kopecky, Gaia Pavoni, Erica Nocerino, Andrew J. Brooks, Massimiliano Corsini, Fabio Menna, Jordan P. Gallagher, Alessandro Capra, Cristina Castagnetti, Paolo Rossi, Armin Gruen, Fabian Neyer, Alessandro Muntoni, Federico Ponchio, Paolo Cignoni, Matthias Troyer, Sally J. Holbrook and Russell J. Schmitt

Remote Sens. 2023, 15(16), 4077; https://doi.org/10.3390/rs15164077 - 18 Aug 2023

Cited by 7 | Viewed by 7889

Abstract

Detecting the impacts of natural and anthropogenic disturbances that cause declines in organisms or changes in community composition has long been a focus of ecology. However, a tradeoff often exists between the spatial extent over which relevant data can be collected, and the resolution of those data. Recent advances in underwater photogrammetry, as well as computer vision and machine learning tools that employ artificial intelligence (AI), offer potential solutions with which to resolve this tradeoff. Here, we coupled a rigorous photogrammetric survey method with novel AI-assisted image segmentation software in order to quantify the impact of a coral bleaching event on a tropical reef, both at an ecologically meaningful spatial scale and with high spatial resolution. In addition to outlining our workflow, we highlight three key results: (1) dramatic changes in the three-dimensional surface areas of live and dead coral, as well as the ratio of live to dead colonies before and after bleaching; (2) a size-dependent pattern of mortality in bleached corals, where the largest corals were disproportionately affected, and (3) a significantly greater decline in the surface area of live coral, as revealed by our approximation of the 3D shape compared to the more standard planar area (2D) approach. The technique of photogrammetry allows us to turn 2D images into approximate 3D models in a flexible and efficient way. Increasing the resolution, accuracy, spatial extent, and efficiency with which we can quantify effects of disturbances will improve our ability to understand the ecological consequences that cascade from small to large scales, as well as allow more informed decisions to be made regarding the mitigation of undesired impacts. Full article

(This article belongs to the Special Issue Computer Vision-Based Methods and Tools in Remote Sensing)

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Figure 1
Site location. (a) satellite image of geographical location of French Polynesia in the South Pacific Ocean; (b) satellite image of Moorea, French Polynesia with a box indicating the location of the study site; (c) the fore reef of Moorea near our study site after the bleaching event that occurred in April of 2019 (PC: A. Thurber). Full article ">Figure 2
Schematic of workflow. (a) custom-designed mount for a photogrammetry target that can be screwed into an anchor mounted in the reef substrate (PC: K. Kopecky); (b) diver taking photographs of the reef with custom photogrammetry targets in place (PC: R. Honeycutt); (c) orthophotomosaic of a single reef plot before the bleaching event; (d) interactive AI-based segmentations using TagLab (bright pink shapes) inside of a designated working area (shaded square) on the orthophotomosaic; (e) zoomed-in view of fully automated annotations of live corals in TagLab; (f) an example live coral colony (left panel) that died after the bleaching event (right panel). Full article ">Figure 3
Example of 3D model layers for a single reef plot and epoch. (a) 3D mesh model with shaded rendering; (b) 3D mesh model with ambient occlusion rendering; (c) digital elevation model (DEM); and (d) orthorectified photomosaic. Full article ">Figure 4
The TagLab computer interface. A plot with automatic segmentations of live and dead coral before (left) and after (right) the bleaching event. The small images show a live coral colony (left, pink shading) that died as a result of a bleaching event (right, brown shading). White boxes and lines indicate where the example colony is located in each of the larger images. The panels on the far right display the various attributes of the plot and the annotated colonies. From top to bottom: total coverage of designated coral classes (i.e., live and dead coral); a data table showing all annotated colonies, co-registered through time; attributes of the selected colony (e.g., 2D planar area, approximated 3D surface area, and perimeter); and a map preview showing the portion of the entire orthophotomosaic that is currently displayed. Full article ">Figure 5
Changes in live and dead coral from before to after bleaching. (a) The proportions of the total number of colonies (summed across replicate plots) in each year that were either alive (gray) or dead (black). Total number of colonies before bleaching: N = 4306; total number of colonies after bleaching: N = 4015. (b) Total approximated 3D surface areas (m2) of live coral (gray) and dead coral (black). Triangles indicate means, error bars are ±1 SE, lines connect means through time, and small dots are plot (replicate) totals. Full article ">Figure 6
The mean number (±1 SE; N = 5 plots) of dead coral colonies per plot in three size classes (approximated 3D surface area in cm2), before (2018, dark blue) and after (2019, light blue) the bleaching event. Full article ">Figure 7
Comparison of estimates of live coral using approximated 3D surface area (m2, triangles) and 2D planar area (m2, squares) in 2018 (pre-bleaching, dark blue) and 2019 (post-bleaching, light blue) (plots as replicates). Triangles and squares represent means, error bars are ±1 SE, and lines connect observations through time. Full article ">

25 pages, 4426 KiB

Open AccessArticle

Converging Channel Attention Mechanisms with Multilayer Perceptron Parallel Networks for Land Cover Classification

by Xiangsuo Fan, Xuyang Li, Chuan Yan, Jinlong Fan, Lin Chen and Nayi Wang

Remote Sens. 2023, 15(16), 3924; https://doi.org/10.3390/rs15163924 - 8 Aug 2023

Cited by 2 | Viewed by 1198

Abstract

This paper proposes a network structure called CAMP-Net, which considers the problem that traditional deep learning algorithms are unable to manage the pixel information of different bands, resulting in poor differentiation of feature representations of different categories and causing classification overfitting. CAMP-Net is a parallel network that, firstly, enhances the interaction of local information of bands by grouping the spectral nesting of the band information and then proposes a parallel processing model. One branch is responsible for inputting the features, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) band information generated by grouped nesting into the ViT framework, and enhancing the interaction and information flow between different channels in the feature map by adding the channel attention mechanism to realize the expressive capability of the feature map. The other branch assists the network’s ability to enhance the extraction of different feature channels by designing a multi-layer perceptron network based on the utilization of the feature channels. Finally, the classification results are obtained by fusing the features obtained by the channel attention mechanism with those obtained by the MLP to achieve pixel-level multispectral image classification. In this study, the application of the algorithm was carried out in the feature distribution of South County, Yiyang City, Hunan Province, and the experiments were conducted based on 10 m Sentinel-2 multispectral RS images. The experimental results show that the overall accuracy of the algorithm proposed in this paper is 99.00% and the transformer (ViT) is 95.81%, while the performance of the algorithm in the Sentinel-2 dataset was greatly improved for the transformer. The transformer shows a huge improvement, which provides research value for developing a land cover classification algorithm for remote sensing images. Full article

(This article belongs to the Special Issue Computer Vision-Based Methods and Tools in Remote Sensing)

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23 pages, 28102 KiB

Open AccessArticle

Multi-SUAV Collaboration and Low-Altitude Remote Sensing Technology-Based Image Registration and Change Detection Network of Garbage Scattered Areas in Nature Reserves

by Kai Yan, Yaxin Dong, Yang Yang and Lin Xing

Remote Sens. 2022, 14(24), 6352; https://doi.org/10.3390/rs14246352 - 15 Dec 2022

Cited by 1 | Viewed by 1620

Abstract

Change detection is an important task in remote sensing image processing and analysis. However, due to position errors and wind interference, bi-temporal low-altitude remote sensing images collected by SUAVs often suffer from different viewing angles. The existing methods need to use an independent registration network for registration before change detection, which greatly reduces the integrity and speed of the task. In this work, we propose an end-to-end network architecture RegCD-Net to address change detection problems in the bi-temporal SUAVs’ low-altitude remote sensing images. We utilize global and local correlations to generate an optical flow pyramid and realize image registration through layer-by-layer optical flow fields. Then we use a nested connection to combine the rich semantic information in deep layers of the network and the precise location information in the shallow layers and perform deep supervision through the combined attention module to finally achieve change detection in bi-temporal images. We apply this network to the task of change detection in the garbage-scattered areas of nature reserves and establish a related dataset. Experimental results show that our RegCD-Net outperforms several state-of-the-art CD methods with more precise change edge representation, relatively few parameters, fast speed, and better integration without additional registration networks. Full article

(This article belongs to the Special Issue Computer Vision-Based Methods and Tools in Remote Sensing)

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19 pages, 15010 KiB

Open AccessArticle

DSM Generation from Multi-View High-Resolution Satellite Images Based on the Photometric Mesh Refinement Method

by Benchao Lv, Jianchen Liu, Ping Wang and Muhammad Yasir

Remote Sens. 2022, 14(24), 6259; https://doi.org/10.3390/rs14246259 - 10 Dec 2022

Cited by 2 | Viewed by 2492

Abstract

Automatic reconstruction of DSMs from satellite images is a hot issue in the field of photogrammetry. Nowadays, most state-of-the-art pipelines produce 2.5D products. In order to solve some shortcomings of traditional algorithms and expand the means of updating digital surface models, a DSM generation method based on variational mesh refinement of satellite stereo image pairs to recover 3D surfaces from coarse input is proposed. Specifically, the initial coarse mesh is constructed first and the geometric features of the generated 3D mesh model are then optimized by using the information of the original images, while the 3D mesh subdivision is constrained by combining the image’s texture information and projection information, with subdivision optimization of the mesh model finally achieved. The results of this method are compared qualitatively and quantitatively with those of the commercial software PCI and the SGM method. The experimental results show that the generated 3D digital surface has clearer edge contours, more refined planar textures, and sufficient model accuracy to match well with the actual conditions of the ground surface, proving the effectiveness of the method. The method is advantageous for conducting research on true 3D products in complex urban areas and can generate complete DSM products with the input of rough meshes, thus indicating it has some development prospects. Full article

(This article belongs to the Special Issue Computer Vision-Based Methods and Tools in Remote Sensing)

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