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Hyperspectral Imagery Intelligent Processing for Coastal Environmental Studies

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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 (30 November 2019) | Viewed by 33917

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Special Issue Editors

Prof. Dr. Weiwei Sun

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Guest Editor

Department of Geography and Spatial Information Techniques, Ningbo University Beijing, Ningbo 315211, China
Interests: remote sensing; hyperspectral image processing; artificial intelligence
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Prof. Dr. Ruiliang Pu

E-Mail Website
Guest Editor

School of Geosciences, University of South Florida, 4204 E Fowler Ave., NES 107, Tampa, FL 33620, USA
Interests: hyperspectral data analysis; remote sensing; invasive species mapping and monitoring; land cover change detection; image processing
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Prof. Dr. Dengsheng Lu

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Guest Editor

School of Geographical Sciences, Fujian Normal University, No. 18 Middle Wulongjiang Avenue, Fuzhou 350117, China
Interests: forest aboveground biomass mapping; classification; change detection
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Dr. Jiangtao Peng

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Guest Editor

Faculty of Mathematics and Statistics, 368 Youyi Avenue, Wuchang District, Wuhan 430062, China
Interests: hyperspectral imagery; deep learning; transfer learning; image classification; health monitoring
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Dr. Jia Wan

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Guest Editor

Department of Electrical & Computer Engineering, University of California San Diego, 9500 Gilman Drive, San Diego, CA, USA
Interests: computer vision; image analysis; machine learning; remote sensing; big data processing
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Special Issue Information

Dear Colleagues,

Coastal ocean environments are profoundly important to global ecology. The effective monitoring and sustainable management of these vital environments requires comprehensive multidisciplinary understanding. Hyperspectral remote sensing provides one of the most effective tools for understanding coastal ecosystems and their connections to the ocean and land interfaces. Currently, hyperspectral data are being used in many applications related to coastal areas, such as the characterization of phytoplankton, measurement of water quality, analysis of the incidence of algal blooms, and coastal fisheries. In this Special Issue, advanced techniques for the analysis of coastal ocean environments using hyperspectral remote sensing data will be presented.

This Special Issue aims to collect articles addressing new developments and methodologies, best practices and applications of hyperspectral remote sensing in coastal environments. We invite you to submit your most recent advancements on all relevant topics, including but not limited to the following:

Advanced data pre-processing methods for coastal hyperspectral images (e.g., dimensionality reduction, band selection, image denoising, etc.).
Fusion of hyperspectral and other remote sensing data (e.g., Lidar, multispectral imagery, SAR, and Pan images) for coastal analyses.
Diagnostic spectrum characteristic extraction and feature analysis of coastal ground objects (e.g., wetland, vegetation, and water).
Training sample selection and optimization techniques for hyperspectral data in coastal areas.
Fine identification of coastal ground objects using hyperspectral image processing (e.g., classification, spectral unmixing, and target detection).
Temporal change detection and analysis of coastal hyperspectral images.
Improved algorithms in big coastal hyperspectral data processing, such as deep learning and transfer learning.

Dr. Weiwei Sun
Dr. Ruiliang Pu
Dr. Dengsheng Lu
Dr. Jiangtao Peng
Dr. Jia Wan
Guest Editors

Manuscript Submission Information

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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.

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Keywords

hyperspectral imagery
remote sensing
machine learning
coastal wetland
image classification
invasive species
data fusion
change detection
coastal wetland mapping

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Published Papers (6 papers)

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Research

21 pages, 16064 KiB

Open AccessArticle

A Hierarchical Classification Framework of Satellite Multispectral/Hyperspectral Images for Mapping Coastal Wetlands

by Leilei Jiao, Weiwei Sun, Gang Yang, Guangbo Ren and Yinnian Liu

Remote Sens. 2019, 11(19), 2238; https://doi.org/10.3390/rs11192238 - 26 Sep 2019

Cited by 65 | Viewed by 5458

Abstract

Mapping different land cover types with satellite remote sensing data is significant for restoring and protecting natural resources and ecological services in coastal wetlands. In this paper, we propose a hierarchical classification framework (HCF) that implements two levels of classification scheme to identify different land cover types of coastal wetlands. The first level utilizes the designed decision tree to roughly group land covers into four rough classes and the second level combines multiple features (i.e., spectral feature, texture feature and geometric feature) of each class to distinguish different subtypes of land covers in each rough class. Two groups of classification experiments on Landsat and Sentinel multispectral data and China Gaofen (GF)-5 hyperspectral data are carried out in order to testify the classification behaviors of two famous coastal wetlands of China, that is, Yellow River Estuary and Yancheng coastal wetland. Experimental results on Landsat data show that the proposed HCF performs better than support vector machine and random forest in classifying land covers of coastal wetlands. Moreover, HCF is suitable for both multispectral data and hyperspectral data and the GF-5 data is superior to Landsat-8 and Sentinel-2 multispectral data in obtaining fine classification results of coastal wetlands. Full article

(This article belongs to the Special Issue Hyperspectral Imagery Intelligent Processing for Coastal Environmental Studies)

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20 pages, 1830 KiB

Open AccessArticle

ℓ_p-ICP Coastline Inflection Method for Geolocation Error Estimation in FY-3 MWRI Data

by Xinghui Zhao, Na Chen, Weifu Li, Jiangtao Peng and Lijun Shen

Remote Sens. 2019, 11(16), 1886; https://doi.org/10.3390/rs11161886 - 12 Aug 2019

Cited by 2 | Viewed by 2589

Abstract

Known as input in the Numerical Weather Prediction (NWP) models, Microwave Radiation Imager (MWRI) data have been widely distributed to the user community. With the development of remote sensing technology, improving the geolocation accuracy of MWRI data are required and the first step is to estimate the geolocation error accurately. However, the traditional method, such as the coastline inflection method (CIM), usually has the disadvantages of low accuracy and poor anti-noise ability. To overcome these limitations, this paper proposes a novel

ℓ_{p}

iterative closest point coastline inflection method (

ℓ_{p}

-ICP CIM). It assumes that the field of views (FOVs) across the coastline can degenerate into a step function and employs an

ℓ_{p} (0 \leq p < 1)

sparse regularization optimization model to solve the coastline point. After estimating the coastline points, the ICP algorithm is employed to estimate the corresponding relationship between the estimated coastline points and the real coastline. Finally, the geolocation error can be defined as the distance between the estimated coastline point and the corresponding point on the true coastline. Experimental results on simulated and real data sets show the effectiveness of our method over CIM. The accuracy of the geolocation error estimated by

ℓ_{p}

-ICP CIM is up to

0.1

pixel, in more than

90 %

of cases. We also show that the distribution of brightness temperature near the coastline is more consistent with the real coastline and the average geolocation error is reduced by

63 %

after geolocation error correction. Full article

(This article belongs to the Special Issue Hyperspectral Imagery Intelligent Processing for Coastal Environmental Studies)

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16 pages, 3132 KiB

Open AccessArticle

Anomaly Detection in Hyperspectral Imagery Based on Low-Rank Representation Incorporating a Spatial Constraint

by Kun Tan, Zengfu Hou, Donglei Ma, Yu Chen and Qian Du

Remote Sens. 2019, 11(13), 1578; https://doi.org/10.3390/rs11131578 - 3 Jul 2019

Cited by 39 | Viewed by 4515

Abstract

Hyperspectral imagery contains abundant spectral information. Each band contains some specific characteristics closely related to target objects. Therefore, using these characteristics, hyperspectral imagery can be used for anomaly detection. Recently, with the development of compressed sensing, low-rank-representation-based methods have been applied to hyperspectral anomaly detection. In this study, novel low-rank representation methods were developed for anomaly detection from hyperspectral images based on the assumption that hyperspectral pixels can be effectively decomposed into a low-rank component (for background) and a sparse component (for anomalies). In order to improve detection performance, we imposed a spatial constraint on the low-rank representation coefficients, and single or multiple local window strategies was applied to smooth the coefficients. Experiments on both simulated and real hyperspectral datasets demonstrated that the proposed approaches can effectively improve hyperspectral anomaly detection performance. Full article

(This article belongs to the Special Issue Hyperspectral Imagery Intelligent Processing for Coastal Environmental Studies)

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15 pages, 8891 KiB

Open AccessArticle

R-CNN-Based Ship Detection from High Resolution Remote Sensing Imagery

by Shaoming Zhang, Ruize Wu, Kunyuan Xu, Jianmei Wang and Weiwei Sun

Remote Sens. 2019, 11(6), 631; https://doi.org/10.3390/rs11060631 - 15 Mar 2019

Cited by 140 | Viewed by 9157

Abstract

Offshore and inland river ship detection has been studied on both synthetic aperture radar (SAR) and optical remote sensing imagery. However, the classic ship detection methods based on SAR images can cause a high false alarm ratio and be influenced by the sea surface model, especially on inland rivers and in offshore areas. The classic detection methods based on optical images do not perform well on small and gathering ships. This paper adopts the idea of deep networks and presents a fast regional-based convolutional neural network (R-CNN) method to detect ships from high-resolution remote sensing imagery. First, we choose GaoFen-2 optical remote sensing images with a resolution of 1 m and preprocess the images with a support vector machine (SVM) to divide the large detection area into small regions of interest (ROI) that may contain ships. Then, we apply ship detection algorithms based on a region-based convolutional neural network (R-CNN) on ROI images. To improve the detection result of small and gathering ships, we adopt an effective target detection framework, Faster-RCNN, and improve the structure of its original convolutional neural network (CNN), VGG16, by using multiresolution convolutional features and performing ROI pooling on a larger feature map in a region proposal network (RPN). Finally, we compare the most effective classic ship detection method, the deformable part model (DPM), another two widely used target detection frameworks, the single shot multibox detector (SSD) and YOLOv2, the original VGG16-based Faster-RCNN, and our improved Faster-RCNN. Experimental results show that our improved Faster-RCNN method achieves a higher recall and accuracy for small ships and gathering ships. Therefore, it provides a very effective method for offshore and inland river ship detection based on high-resolution remote sensing imagery. Full article

(This article belongs to the Special Issue Hyperspectral Imagery Intelligent Processing for Coastal Environmental Studies)

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17 pages, 3797 KiB

Open AccessArticle

Hyperspectral and LiDAR Data Fusion Classification Using Superpixel Segmentation-Based Local Pixel Neighborhood Preserving Embedding

by Yunsong Li, Chiru Ge, Weiwei Sun, Jiangtao Peng, Qian Du and Keyan Wang

Remote Sens. 2019, 11(5), 550; https://doi.org/10.3390/rs11050550 - 6 Mar 2019

Cited by 10 | Viewed by 4613

Abstract

A new method of superpixel segmentation-based local pixel neighborhood preserving embedding (SSLPNPE) is proposed for the fusion of hyperspectral and light detection and ranging (LiDAR) data based on the extinction profiles (EPs), superpixel segmentation and local pixel neighborhood preserving embedding (LPNPE). A new workflow is proposed to calibrate the Goddard’s LiDAR, hyperspectral and thermal (G-LiHT) data, which allows our method to be applied to actual data. Specifically, EP features are extracted from both sources. Then, the derived features of each source are fused by the SSLPNPE. Using the labeled samples, the final label assignment is produced by a classifier. For the open standard experimental data and the actual data, experimental results prove that the proposed method is fast and effective in hyperspectral and LiDAR data fusion. Full article

(This article belongs to the Special Issue Hyperspectral Imagery Intelligent Processing for Coastal Environmental Studies)

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17 pages, 13591 KiB

Open AccessArticle

The Spectral-Spatial Joint Learning for Change Detection in Multispectral Imagery

by Wuxia Zhang and Xiaoqiang Lu

Remote Sens. 2019, 11(3), 240; https://doi.org/10.3390/rs11030240 - 24 Jan 2019

Cited by 66 | Viewed by 6511

Abstract

Change detection is one of the most important applications in the remote sensing domain. More and more attention is focused on deep neural network based change detection methods. However, many deep neural networks based methods did not take both the spectral and spatial information into account. Moreover, the underlying information of fused features is not fully explored. To address the above-mentioned problems, a Spectral-Spatial Joint Learning Network (SSJLN) is proposed. SSJLN contains three parts: spectral-spatial joint representation, feature fusion, and discrimination learning. First, the spectral-spatial joint representation is extracted from the network similar to the Siamese CNN (S-CNN). Second, the above-extracted features are fused to represent the difference information that proves to be effective for the change detection task. Third, the discrimination learning is presented to explore the underlying information of obtained fused features to better represent the discrimination. Moreover, we present a new loss function that considers both the losses of the spectral-spatial joint representation procedure and the discrimination learning procedure. The effectiveness of our proposed SSJLN is verified on four real data sets. Extensive experimental results show that our proposed SSJLN can outperform the other state-of-the-art change detection methods. Full article

(This article belongs to the Special Issue Hyperspectral Imagery Intelligent Processing for Coastal Environmental Studies)

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