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Advances in Optical Remote Sensing Image Processing and Applications

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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 (31 March 2022) | Viewed by 30906

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Special Issue Editors

Prof. Dr. Nicolas H. Younan

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Guest Editor

Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS 39762, USA
Interests: signal processing and pattern recognition; automated target detection; image fusion; image information mining
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Qian Du

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Guest Editor

Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA
Interests: hyperspectral imagery; remote sensing; intelligent processing; machine learning; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Dr. Weiying Xie

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Guest Editor

State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, China
Interests: hyperspectral anomaly detection; network compression; efficient distributed learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Global observations from satellite remote sensing span multiple decades and also multiple spectral ranges (visible, near-infrared, thermal, etc.), spawning many potential remote sensing image processing technologies and applications. Particularly, in recent years, hyperspectral images and multisource data have provided new insight and capabilities for observing urban/natural environments, monitoring climate change, and investigating human behavior. In this Special Issue on “Advances in Optical Remote Sensing Image Processing and Applications”, we invite papers involving one or more of the following topical areas, focusing on imaging technology, data analysis, and practical application of optical remote sensing:

Remote sensing imaging spectroscopy and systems;
Data analysis (land-use classification, pan-sharpening, multitemporal data fusion, change detection, anomaly detection, etc.);
Optical remote sensing applications (road/building/river detection, wildland fire detection, forest environmental monitoring, climate observation, real-time processing, low-power computer vision, etc.);
Deep learning methods covering one or more of these topics;
Review articles covering one or more of these topics.

Prof. Dr. Nicolas H. Younan
Prof. Dr. Qian Du
Dr. Weiying Xie
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

Optical remote sensing
Hyperspectral image
Multisource remote sensing data
Very high spatial resolution
Mathematical morphology
Spatial/spectral information extraction
Deep learning
Image processing
Practical application

Published Papers (9 papers)

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Research

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18 pages, 13245 KiB

Open AccessArticle

Landsat-8 Sea Ice Classification Using Deep Neural Networks

by Alvaro Cáceres, Egbert Schwarz and Wiebke Aldenhoff

Remote Sens. 2022, 14(9), 1975; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14091975 - 20 Apr 2022

Cited by 2 | Viewed by 2843

Abstract

Knowing the location and type of sea ice is essential for safe navigation and route optimization in ice-covered areas. In this study, we developed a deep neural network (DNN) for pixel-based ice Stage of Development classification for the Baltic Sea using Landsat-8 optical satellite imagery to provide up-to-date ice information for Near-Real-Time maritime applications. In order to train the network, we labeled the ice regions shown in the Landsat-8 imagery with classes from the German Federal Maritime and Hydrographic Agency (BSH) ice charts. These charts are routinely produced and distributed by the BSH Sea Ice Department. The compiled data set for the Baltic Sea region consists of 164 ice charts from 2014 to 2021 and contains ice types classified by the Stage of Development. Landsat-8 level 1 (L1b) images that could be overlaid with the available BSH ice charts based on the time of acquisition were downloaded from the United States Geological Survey (USGS) global archive and indexed in a data cube for better handling. The input variables of the DNN are the individual spectral bands: aerosol coastal, blue, green, red and near-infrared (NIR) out of the Operational Land Imager (OLI) sensor. The bands were selected based on the reflectance and emission properties of sea ice. The output values are 4 ice classes of Stage of Development and Ice Free. The results obtained show significant improvements compared to the available BSH ice charts when moving from polygons to pixels, preserving the original classes. The classification model has an accuracy of 87.5% based on the test data set excluded from the training and validation process. Using optical imagery can therefore add value to maritime safety and navigation in ice- infested waters by high resolution and real-time availability. Furthermore, the obtained results can be extended to other optical satellite imagery such as Sentinel-2. Our approach is promising for automated Near-Real-Time (NRT) services, which can be deployed and integrated at a later stage at the German Aerospace Center (DLR) ground station in Neustrelitz. Full article

(This article belongs to the Special Issue Advances in Optical Remote Sensing Image Processing and Applications)

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25 pages, 26383 KiB

Open AccessArticle

Multispectral Image Enhancement Based on the Dark Channel Prior and Bilateral Fractional Differential Model

by Weijie Chen, Zhenhong Jia, Jie Yang and Nikola K. Kasabov

Remote Sens. 2022, 14(1), 233; https://doi.org/10.3390/rs14010233 - 05 Jan 2022

Cited by 6 | Viewed by 2589

Abstract

Compared with single-band remote sensing images, multispectral images can obtain information on the same target in different bands. By combining the characteristics of each band, we can obtain clearer enhanced images; therefore, we propose a multispectral image enhancement method based on the improved dark channel prior (IDCP) and bilateral fractional differential (BFD) model to make full use of the multiband information. First, the original multispectral image is inverted to meet the prior conditions of dark channel theory. Second, according to the characteristics of multiple bands, the dark channel algorithm is improved. The RGB channels are extended to multiple channels, and the spatial domain fractional differential mask is used to optimize the transmittance estimation to make it more consistent with the dark channel hypothesis. Then, we propose a bilateral fractional differentiation algorithm that enhances the edge details of an image through the fractional differential in the spatial domain and intensity domain. Finally, we implement the inversion operation to obtain the final enhanced image. We apply the proposed IDCP_BFD method to a multispectral dataset and conduct sufficient experiments. The experimental results show the superiority of the proposed method over relative comparison methods. Full article

(This article belongs to the Special Issue Advances in Optical Remote Sensing Image Processing and Applications)

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31 pages, 12180 KiB

Open AccessArticle

Exploring Fuzzy Local Spatial Information Algorithms for Remote Sensing Image Classification

by Anjali Madhu, Anil Kumar and Peng Jia

Remote Sens. 2021, 13(20), 4163; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13204163 - 18 Oct 2021

Cited by 5 | Viewed by 2450

Abstract

Fuzzy c-means (FCM) and possibilistic c-means (PCM) are two commonly used fuzzy clustering algorithms for extracting land use land cover (LULC) information from satellite images. However, these algorithms use only spectral or grey-level information of pixels for clustering and ignore their spatial correlation. Different variants of the FCM algorithm have emerged recently that utilize local spatial information in addition to spectral information for clustering. Such algorithms are seen to generate clustering outputs that are more enhanced than the classical spectral-based FCM algorithm. Nonetheless, the scope of integrating spatial contextual information with the conventional PCM algorithm, which has several advantages over the FCM algorithm for supervised classification, has not been explored much. This study proposed integrating local spatial information with the PCM algorithm using simpler but proven approaches from available FCM-based local spatial information algorithms. The three new PCM-based local spatial information algorithms: Possibilistic c-means with spatial constraints (PCM-S), possibilistic local information c-means (PLICM), and adaptive possibilistic local information c-means (ADPLICM) algorithms, were developed corresponding to the available fuzzy c-means with spatial constraints (FCM-S), fuzzy local information c-means (FLICM), and adaptive fuzzy local information c-means (ADFLICM) algorithms. Experiments were conducted to analyze and compare the FCM and PCM classifier variants for supervised LULC classifications in soft (fuzzy) mode. The quantitative assessment of the soft classification results from fuzzy error matrix (FERM) and root mean square error (RMSE) suggested that the new PCM-based local spatial information classifiers produced higher accuracies than the PCM, FCM, or its local spatial variants, in the presence of untrained classes and noise. The promising results from PCM-based local spatial information classifiers suggest that the PCM algorithm, which is known to be naturally robust to noise, when integrated with local spatial information, has the potential to result in more efficient classifiers capable of better handling ambiguities caused by spectral confusions in landscapes. Full article

(This article belongs to the Special Issue Advances in Optical Remote Sensing Image Processing and Applications)

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23 pages, 1366 KiB

Open AccessArticle

HA-Net: A Lake Water Body Extraction Network Based on Hybrid-Scale Attention and Transfer Learning

by Zhaobin Wang, Xiong Gao and Yaonan Zhang

Remote Sens. 2021, 13(20), 4121; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13204121 - 14 Oct 2021

Cited by 13 | Viewed by 2411

Abstract

Due to the large quantity of noise and complex spatial background of the remote sensing images, how to improve the accuracy of semantic segmentation has become a hot topic. Lake water body extraction is crucial for disaster detection, resource utilization, and carbon cycle, etc. The the area of lakes on the Tibetan Plateau has been constantly changing due to the movement of the Earth’s crust. Most of the convolutional neural networks used for remote sensing images are based on single-layer features for pixel classification while ignoring the correlation of such features in different layers. In this paper, the two-branch encoder is presented, which is a multiscale structure that combines the features of ResNet-34 with a feature pyramid network. Secondly, adaptive weights are distributed to global information using the hybrid-scale attention block. Finally, PixelShuffle is used to recover the feature maps’ resolution, and the densely connected block is used to refine the boundary of the lake water body. Likewise, we transfer the best weights which are saved on the Google dataset to the Landsat-8 dataset to ensure that our proposed method is robust. We validate the superiority of Hybrid-scale Attention Network (HA-Net) on two given datasets, which were created by us using Google and Landsat-8 remote sensing images. (1) On the Google dataset, HA-Net achieves the best performance of all five evaluation metrics with a Mean Intersection over Union (MIoU) of 97.38%, which improves by 1.04% compared with DeepLab V3+, and reduces the training time by about 100 s per epoch. Moreover, the overall accuracy (OA), Recall, True Water Rate (TWR), and False Water Rate (FWR) of HA-Net are 98.88%, 98.03%, 98.24%, and 1.76% respectively. (2) On the Landsat-8 dataset, HA-Net achieves the best overall accuracy and the True Water Rate (TWR) improvement of 2.93% compared to Pre_PSPNet, which proves to be more robust than other advanced models. Full article

(This article belongs to the Special Issue Advances in Optical Remote Sensing Image Processing and Applications)

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19 pages, 3742 KiB

Open AccessArticle

Multispectral Image Change Detection Based on Single-Band Slow Feature Analysis

by Youxi He, Zhenhong Jia, Jie Yang and Nikola K. Kasabov

Remote Sens. 2021, 13(15), 2969; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13152969 - 28 Jul 2021

Cited by 6 | Viewed by 2549

Abstract

Due to differences in external imaging conditions, multispectral images taken at different periods are subject to radiation differences, which severely affect the detection accuracy. To solve this problem, a modified algorithm based on slow feature analysis is proposed for multispectral image change detection. First, single-band slow feature analysis is performed to process bitemporal multispectral images band by band. In this way, the differences between unchanged pixels in each pair of single-band images can be sufficiently suppressed to obtain multiple feature-difference images containing real change information. Then, the feature-difference images of each band are fused into a grayscale distance image using the Euclidean distance. After Gaussian filtering of the grayscale distance image, false detection points can be further reduced. Finally, the k-means clustering method is performed on the filtered grayscale distance image to obtain the binary change map. Experiments reveal that our proposed algorithm is less affected by radiation differences and has obvious advantages in time complexity and detection accuracy. Full article

(This article belongs to the Special Issue Advances in Optical Remote Sensing Image Processing and Applications)

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19 pages, 8546 KiB

Open AccessArticle

An Improved Aggregated-Mosaic Method for the Sparse Object Detection of Remote Sensing Imagery

by Boya Zhao, Yuanfeng Wu, Xinran Guan, Lianru Gao and Bing Zhang

Remote Sens. 2021, 13(13), 2602; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132602 - 02 Jul 2021

Cited by 15 | Viewed by 3503

Abstract

Object detection based on remote sensing imagery has become increasingly popular over the past few years. Unlike natural images taken by humans or surveillance cameras, the scale of remote sensing images is large, which requires the training and inference procedure to be on a cutting image. However, objects appearing in remote sensing imagery are often sparsely distributed and the labels for each class are imbalanced. This results in unstable training and inference. In this paper, we analyze the training characteristics of the remote sensing images and propose the fusion of the aggregated-mosaic training method, with the assigned-stitch augmentation and auto-target-duplication. In particular, based on the ground truth and mosaic image size, the assigned-stitch augmentation enhances each training sample with an appropriate account of objects, facilitating the smooth training procedure. Hard to detect objects, or those in classes with rare samples, are randomly selected and duplicated by the auto-target-duplication, which solves the sample imbalance or classes with insufficient results. Thus, the training process is able to focus on weak classes. We employ VEDAI and NWPU VHR-10, remote sensing datasets with sparse objects, to verify the proposed method. The YOLOv5 adopts the Mosaic as the augmentation method and is one of state-of-the-art detectors, so we choose Mosaic (YOLOv5) as the baseline. Results demonstrate that our method outperforms Mosaic (YOLOv5) by 2.72% and 5.44% on 512 × 512 and 1024 × 1024 resolution imagery, respectively. Moreover, the proposed method outperforms Mosaic (YOLOv5) by 5.48% under the NWPU VHR-10 dataset. Full article

(This article belongs to the Special Issue Advances in Optical Remote Sensing Image Processing and Applications)

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23 pages, 7331 KiB

Open AccessArticle

An Image Stitching Method for Airborne Wide-Swath HyperSpectral Imaging System Equipped with Multiple Imagers

by Jingmei Li, Lingling Ma, Yongxiang Fan, Ning Wang, Keke Duan, Qijin Han, Xuyuan Zhang, Guozhong Su, Chuanrong Li and Lingli Tang

Remote Sens. 2021, 13(5), 1001; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13051001 - 06 Mar 2021

Cited by 6 | Viewed by 3117

Abstract

The field of view (FOV) of pushbroom hyperspectral imager is limited by the compromise of the detector scale and requirements of spatial resolution. Combining imagers along the sampling direction effectively expands its FOV and improves the imaging efficiency. Due to the small overlapping area between the adjacent imagers, stitching the images using traditional methods need a large amount of ground control points (GCPs) or additional strips, which reduce the efficiency of both image acquisition and processing. This paper proposed a new method to precisely stitch images acquired from multiple pushbroom imagers. First, the relative orientation model was built based on the homonymy points to calculate the relative relationship between the adjacent imagers. Then rigorous geometric imaging model was adopted to generate a seamless stitching image. Simulation data was used to verify the accuracy of the method and to quantitatively analyze the effect of different error sources. Results show that the stitching accuracy is better than two pixels. Overall, this method provides a novel solution for stitching airborne multiple pushbroom images, to generate the seamless stitching image with wide FOV. Full article

(This article belongs to the Special Issue Advances in Optical Remote Sensing Image Processing and Applications)

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24 pages, 9710 KiB

Open AccessArticle

A Lightweight Object Detection Framework for Remote Sensing Images

by Lang Huyan, Yunpeng Bai, Ying Li, Dongmei Jiang, Yanning Zhang, Quan Zhou, Jiayuan Wei, Juanni Liu, Yi Zhang and Tao Cui

Remote Sens. 2021, 13(4), 683; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13040683 - 13 Feb 2021

Cited by 20 | Viewed by 3561

Abstract

Onboard real-time object detection in remote sensing images is a crucial but challenging task in this computation-constrained scenario. This task not only requires the algorithm to yield excellent performance but also requests limited time and space complexity of the algorithm. However, previous convolutional neural networks (CNN) based object detectors for remote sensing images suffer from heavy computational cost, which hinders them from being deployed on satellites. Moreover, an onboard detector is desired to detect objects at vastly different scales. To address these issues, we proposed a lightweight one-stage multi-scale feature fusion detector called MSF-SNET for onboard real-time object detection of remote sensing images. Using lightweight SNET as the backbone network reduces the number of parameters and computational complexity. To strengthen the detection performance of small objects, three low-level features are extracted from the three stages of SNET respectively. In the detection part, another three convolutional layers are designed to further extract deep features with rich semantic information for large-scale object detection. To improve detection accuracy, the deep features and low-level features are fused to enhance the feature representation. Extensive experiments and comprehensive evaluations on the openly available NWPU VHR-10 dataset and DIOR dataset are conducted to evaluate the proposed method. Compared with other state-of-art detectors, the proposed detection framework has fewer parameters and calculations, while maintaining consistent accuracy. Full article

(This article belongs to the Special Issue Advances in Optical Remote Sensing Image Processing and Applications)

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Review

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40 pages, 540 KiB

Open AccessReview

Review of Remote Sensing Applications in Grassland Monitoring

by Zhaobin Wang, Yikun Ma, Yaonan Zhang and Jiali Shang

Remote Sens. 2022, 14(12), 2903; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14122903 - 17 Jun 2022

Cited by 29 | Viewed by 5506

Abstract

The application of remote sensing technology in grassland monitoring and management has been ongoing for decades. Compared with traditional ground measurements, remote sensing technology has the overall advantage of convenience, efficiency, and cost effectiveness, especially over large areas. This paper provides a comprehensive review of the latest remote sensing estimation methods for some critical grassland parameters, including above-ground biomass, primary productivity, fractional vegetation cover, and leaf area index. Then, the applications of remote sensing monitoring are also reviewed from the perspective of their use of these parameters and other remote sensing data. In detail, grassland degradation and grassland use monitoring are evaluated. In addition, disaster monitoring and carbon cycle monitoring are also included. Overall, most studies have used empirical models and statistical regression models, while the number of machine learning approaches has an increasing trend. In addition, some specialized methods, such as the light use efficiency approaches for primary productivity and the mixed pixel decomposition methods for vegetation coverage, have been widely used and improved. However, all the above methods have certain limitations. For future work, it is recommended that most applications should adopt the advanced estimation methods rather than simple statistical regression models. In particular, the potential of deep learning in processing high-dimensional data and fitting non-linear relationships should be further explored. Meanwhile, it is also important to explore the potential of some new vegetation indices based on the spectral characteristics of the specific grassland under study. Finally, the fusion of multi-source images should also be considered to address the deficiencies in information and resolution of remote sensing images acquired by a single sensor or satellite. Full article

(This article belongs to the Special Issue Advances in Optical Remote Sensing Image Processing and Applications)

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