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Data Mining and Feature Extraction from Satellite Images and Point...
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Data Mining and Feature Extraction from Satellite Images and Point Cloud Data

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (30 November 2019) | Viewed by 105634

Special Issue Editor

Prof. Dr. Pedram Ghamisi

grade E-Mail Website
Guest Editor
1.Helmholtz Institute Freiberg for Resource Technology, Helmholtz-Zentrum Dresden-Rossendorf (HZDR), D-09599 Freiberg, Germany
2. Institute of Advanced Research in Artificial Intelligence (IARAI), 1030 Wien, Austria
Interests: hyperspectral image interpretation; multisensor and multitemporal data fusion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The vibrant field of Earth observation (EO), or remote sensing, is now facing an entirely different dimension of challenge in image interpretation due to the tremendous volumes and huge variety of data being generated by EO missions. An enormous increase in the number of missions coupled with a wide variety of available sensors (e.g., radar, passive microwave, thermal and LiDAR) have led the community to an unprecedented number and complexity of data to process, which is already a major challenge to the existing algorithms.

Such an increase in dimensionality, volume, and varieties provide users with rich data contain for a plethora of applications. However, for a specific application, not all the measurements are important and useful. This data contain may cause a serious issue known as “curse of dimensionality”, which negatively influences on the corresponding feature space for representing the data and downgrades the quality of the further processing steps such as data classification. To address this issue, data mining, which includes feature generation, feature selection, and feature extraction, is a crucial step.

It is expected that the advancement of data mining will continue to push the remote sensing and photogrammetry communities forward. Hence, we passionately encourage authors to submit original research articles, case studies, and review papers from both theoretical and application-oriented perspectives on this important and vibrant subject. In more details, topics appropriate for this Special Issue include (but are not necessarily limited to):

  1. Dimensionality reduction
  2. Feature selection, extraction, and object tracking
  3. Deep learning
  4. Spectral, spatial, and elevation information extraction
  5. Feature fusion
  6. Low-rank models for classification, detection, unmixing, resolution enhancement, and denoising.

Dr. Pedram Ghamisi
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. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly 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 1700 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

  • dimensionality reduction
  • feature selection, feature extraction, and object tracking
  • deep learning
  • spectral, spatial, and elevation information extraction
  • feature fusion
  • low-rank models for classification, detection, unmixing, resolution enhancement, and denoising

Published Papers (16 papers)

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Research

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24 pages, 9000 KiB  
Article
Spatial Analysis of Seasonal Precipitation over Iran: Co-Variation with Climate Indices
by Majid Dehghani, Somayeh Salehi, Amir Mosavi, Narjes Nabipour, Shahaboddin Shamshirband and Pedram Ghamisi
ISPRS Int. J. Geo-Inf. 2020, 9(2), 73; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9020073 - 24 Jan 2020
Cited by 35 | Viewed by 3691
Abstract
Temporary changes in precipitation may lead to sustained and severe drought or massive floods in different parts of the world. Knowing the variation in precipitation can effectively help the water resources decision-makers in water resources management. Large-scale circulation drivers have a considerable impact [...] Read more.
Temporary changes in precipitation may lead to sustained and severe drought or massive floods in different parts of the world. Knowing the variation in precipitation can effectively help the water resources decision-makers in water resources management. Large-scale circulation drivers have a considerable impact on precipitation in different parts of the world. In this research, the impact of El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and North Atlantic Oscillation (NAO) on seasonal precipitation over Iran was investigated. For this purpose, 103 synoptic stations with at least 30 years of data were utilized. The Spearman correlation coefficient between the indices in the previous 12 months with seasonal precipitation was calculated, and the meaningful correlations were extracted. Then, the month in which each of these indices has the highest correlation with seasonal precipitation was determined. Finally, the overall amount of increase or decrease in seasonal precipitation due to each of these indices was calculated. Results indicate the Southern Oscillation Index (SOI), NAO, and PDO have the most impact on seasonal precipitation, respectively. Additionally, these indices have the highest impact on the precipitation in winter, autumn, spring, and summer, respectively. SOI has a diverse impact on winter precipitation compared to the PDO and NAO, while in the other seasons, each index has its special impact on seasonal precipitation. Generally, all indices in different phases may decrease the seasonal precipitation up to 100%. However, the seasonal precipitation may increase more than 100% in different seasons due to the impact of these indices. The results of this study can be used effectively in water resources management and especially in dam operation. Full article
(This article belongs to the Special Issue Data Mining and Feature Extraction from Satellite Images and Point Cloud Data)
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18 pages, 6329 KiB  
Article
A Data Augmentation Strategy Based on Simulated Samples for Ship Detection in RGB Remote Sensing Images
by Yiming Yan, Zhichao Tan and Nan Su
ISPRS Int. J. Geo-Inf. 2019, 8(6), 276; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8060276 - 13 Jun 2019
Cited by 30 | Viewed by 4797
Abstract
In this paper, we propose a data augmentation method for ship detection. Inshore ship detection using optical remote sensing imaging is a challenging task owing to an insufficient number of training samples. Although the multilayered neural network method has achieved excellent results in [...] Read more.
In this paper, we propose a data augmentation method for ship detection. Inshore ship detection using optical remote sensing imaging is a challenging task owing to an insufficient number of training samples. Although the multilayered neural network method has achieved excellent results in recent research, a large number of training samples is indispensable to guarantee the accuracy and robustness of ship detection. The majority of researchers adopt such strategies as clipping, scaling, color transformation, and flipping to enhance the samples. Nevertheless, these methods do not essentially increase the quality of the dataset. A novel data augmentation strategy was thus proposed in this study by using simulated remote sensing ship images to augment the positive training samples. The simulated images are generated by true background images and three-dimensional models on the same scale as real ships. A faster region-based convolutional neural network (Faster R-CNN) based on Res101netwok was trained by the dataset, which is composed of both simulated and true images. A series of experiments is designed under small sample conditions; the experimental results show that better detection is obtained with our data augmentation strategy. Full article
(This article belongs to the Special Issue Data Mining and Feature Extraction from Satellite Images and Point Cloud Data)
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34 pages, 16010 KiB  
Article
Voxel-based 3D Point Cloud Semantic Segmentation: Unsupervised Geometric and Relationship Featuring vs Deep Learning Methods
by Florent Poux and Roland Billen
ISPRS Int. J. Geo-Inf. 2019, 8(5), 213; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8050213 - 07 May 2019
Cited by 90 | Viewed by 27357
Abstract
Automation in point cloud data processing is central in knowledge discovery within decision-making systems. The definition of relevant features is often key for segmentation and classification, with automated workflows presenting the main challenges. In this paper, we propose a voxel-based feature engineering that [...] Read more.
Automation in point cloud data processing is central in knowledge discovery within decision-making systems. The definition of relevant features is often key for segmentation and classification, with automated workflows presenting the main challenges. In this paper, we propose a voxel-based feature engineering that better characterize point clusters and provide strong support to supervised or unsupervised classification. We provide different feature generalization levels to permit interoperable frameworks. First, we recommend a shape-based feature set (SF1) that only leverages the raw X, Y, Z attributes of any point cloud. Afterwards, we derive relationship and topology between voxel entities to obtain a three-dimensional (3D) structural connectivity feature set (SF2). Finally, we provide a knowledge-based decision tree to permit infrastructure-related classification. We study SF1/SF2 synergy on a new semantic segmentation framework for the constitution of a higher semantic representation of point clouds in relevant clusters. Finally, we benchmark the approach against novel and best-performing deep-learning methods while using the full S3DIS dataset. We highlight good performances, easy-integration, and high F1-score (> 85%) for planar-dominant classes that are comparable to state-of-the-art deep learning. Full article
(This article belongs to the Special Issue Data Mining and Feature Extraction from Satellite Images and Point Cloud Data)
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16 pages, 8661 KiB  
Article
Automatic Building Footprint Extraction from Multi-Resolution Remote Sensing Images Using a Hybrid FCN
by Philipp Schuegraf and Ksenia Bittner
ISPRS Int. J. Geo-Inf. 2019, 8(4), 191; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8040191 - 12 Apr 2019
Cited by 68 | Viewed by 6685
Abstract
Recent technical developments made it possible to supply large-scale satellite image coverage. This poses the challenge of efficient discovery of imagery. One very important task in applications like urban planning and reconstruction is to automatically extract building footprints. The integration of different information, [...] Read more.
Recent technical developments made it possible to supply large-scale satellite image coverage. This poses the challenge of efficient discovery of imagery. One very important task in applications like urban planning and reconstruction is to automatically extract building footprints. The integration of different information, which is presently achievable due to the availability of high-resolution remote sensing data sources, makes it possible to improve the quality of the extracted building outlines. Recently, deep neural networks were extended from image-level to pixel-level labelling, allowing to densely predict semantic labels. Based on these advances, we propose an end-to-end U-shaped neural network, which efficiently merges depth and spectral information within two parallel networks combined at the late stage for binary building mask generation. Moreover, as satellites usually provide high-resolution panchromatic images, but only low-resolution multi-spectral images, we tackle this issue by using a residual neural network block. It fuses those images with different spatial resolution at the early stage, before passing the fused information to the Unet stream, responsible for processing spectral information. In a parallel stream, a stereo digital surface model (DSM) is also processed by the Unet. Additionally, we demonstrate that our method generalizes for use in cities which are not included in the training data. Full article
(This article belongs to the Special Issue Data Mining and Feature Extraction from Satellite Images and Point Cloud Data)
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14 pages, 6167 KiB  
Article
Oil Film Classification Using Deep Learning-Based Hyperspectral Remote Sensing Technology
by Xueyuan Zhu, Ying Li, Qiang Zhang and Bingxin Liu
ISPRS Int. J. Geo-Inf. 2019, 8(4), 181; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8040181 - 06 Apr 2019
Cited by 25 | Viewed by 3656
Abstract
Marine oil spills seriously impact the marine environment and transportation. When oil spill accidents occur, oil spill distribution information, in particular, the relative thickness of the oil film, is vital for emergency decision-making and cleaning. Hyperspectral remote sensing technology is an effective means [...] Read more.
Marine oil spills seriously impact the marine environment and transportation. When oil spill accidents occur, oil spill distribution information, in particular, the relative thickness of the oil film, is vital for emergency decision-making and cleaning. Hyperspectral remote sensing technology is an effective means to extract oil spill information. In this study, the concept of deep learning is introduced to the classification of oil film thickness based on hyperspectral remote sensing technology. According to the spatial and spectral characteristics, the stacked autoencoder network model based on the support vector machine is improved, enhancing the algorithm’s classification accuracy in validating data sets. A method for classifying oil film thickness using the convolutional neural network is designed and implemented to solve the problem of space homogeneity and heterogeneity. Through numerous experiments and analyses, the potential of the two proposed deep learning methods for accurately classifying hyperspectral oil spill data is verified. Full article
(This article belongs to the Special Issue Data Mining and Feature Extraction from Satellite Images and Point Cloud Data)
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14 pages, 3262 KiB  
Article
A Spectral Feature Based Convolutional Neural Network for Classification of Sea Surface Oil Spill
by Bingxin Liu, Ying Li, Guannan Li and Anling Liu
ISPRS Int. J. Geo-Inf. 2019, 8(4), 160; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8040160 - 27 Mar 2019
Cited by 51 | Viewed by 4245
Abstract
Spectral characteristics play an important role in the classification of oil film, but the presence of too many bands can lead to information redundancy and reduced classification accuracy. In this study, a classification model that combines spectral indices-based band selection (SIs) and one-dimensional [...] Read more.
Spectral characteristics play an important role in the classification of oil film, but the presence of too many bands can lead to information redundancy and reduced classification accuracy. In this study, a classification model that combines spectral indices-based band selection (SIs) and one-dimensional convolutional neural networks was proposed to realize automatic oil films classification using hyperspectral remote sensing images. Additionally, for comparison, the minimum Redundancy Maximum Relevance (mRMR) was tested for reducing the number of bands. The support vector machine (SVM), random forest (RF), and Hu’s convolutional neural networks (CNN) were trained and tested. The results show that the accuracy of classifications through the one dimensional convolutional neural network (1D CNN) models surpassed the accuracy of other machine learning algorithms such as SVM and RF. The model of SIs+1D CNN could produce a relatively higher accuracy oil film distribution map within less time than other models. Full article
(This article belongs to the Special Issue Data Mining and Feature Extraction from Satellite Images and Point Cloud Data)
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15 pages, 3137 KiB  
Article
Polarimetric Target Decompositions and Light Gradient Boosting Machine for Crop Classification: A Comparative Evaluation
by Mustafa Ustuner and Fusun Balik Sanli
ISPRS Int. J. Geo-Inf. 2019, 8(2), 97; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8020097 - 21 Feb 2019
Cited by 50 | Viewed by 8951
Abstract
In terms of providing various scattering mechanisms, polarimetric target decompositions provide certain benefits for the interpretation of PolSAR images. This paper tested the capabilities of different polarimetric target decompositions in crop classification, while using a recently launched ensemble learning algorithm—namely Light Gradient Boosting [...] Read more.
In terms of providing various scattering mechanisms, polarimetric target decompositions provide certain benefits for the interpretation of PolSAR images. This paper tested the capabilities of different polarimetric target decompositions in crop classification, while using a recently launched ensemble learning algorithm—namely Light Gradient Boosting Machine (LightGBM). For the classification of different crops (maize, potato, wheat, sunflower, and alfalfa) in the test site, multi-temporal polarimetric C-band RADARSAT-2 images were acquired over an agricultural area near Konya, Turkey. Four different decomposition models (Cloude–Pottier, Freeman–Durden, Van Zyl, and Yamaguchi) were employed to evaluate polarimetric target decomposition for crop classification. Besides the polarimetric target decomposed parameters, the original polarimetric features (linear backscatter coefficients, coherency, and covariance matrices) were also incorporated for crop classification. The experimental results demonstrated that polarimetric target decompositions, with the exception of Cloude–Pottier, were found to be superior to the original features in terms of overall classification accuracy. The highest classification accuracy (92.07%) was achieved by Yamaguchi, whereas the lowest (75.99%) was achieved by the covariance matrix. Model-based decompositions achieved higher performance with respect to eigenvector-based decompositions in terms of class-based accuracies. Furthermore, the results emphasize the added benefits of model-based decompositions for crop classification using PolSAR data. Full article
(This article belongs to the Special Issue Data Mining and Feature Extraction from Satellite Images and Point Cloud Data)
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18 pages, 5093 KiB  
Article
TLS Measurement during Static Load Testing of a Railway Bridge
by Pelagia Gawronek and Maria Makuch
ISPRS Int. J. Geo-Inf. 2019, 8(1), 44; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8010044 - 17 Jan 2019
Cited by 18 | Viewed by 4456
Abstract
Terrestrial laser scanning (TLS) technology has become increasingly popular in investigating displacement and deformation of natural and anthropogenic objects. Regardless of the accuracy of deformation identification, TLS provides remote comprehensive information about the measured object in a short time. These features of TLS [...] Read more.
Terrestrial laser scanning (TLS) technology has become increasingly popular in investigating displacement and deformation of natural and anthropogenic objects. Regardless of the accuracy of deformation identification, TLS provides remote comprehensive information about the measured object in a short time. These features of TLS were why TLS measurement was used for a static load test of an old, steel railway bridge. The results of the measurement using the Z + F Imager 5010 scanner and traditional surveying methods (for improved georeferencing) were compared to results of precise reflectorless tacheometry and precise levelling. The analyses involved various procedures for the determination of displacement from 3D data (black & white target analysis, point cloud analysis, and mesh surface analysis) and the need to pre-process the· 3D data was considered (georeferencing, automated filtering). The results demonstrate that TLS measurement can identify vertical displacement in line with the results of traditional measurements down to ±1 mm. Full article
(This article belongs to the Special Issue Data Mining and Feature Extraction from Satellite Images and Point Cloud Data)
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11 pages, 7589 KiB  
Article
Multi-Temporal Sentinel-1 and -2 Data Fusion for Optical Image Simulation
by Wei He and Naoto Yokoya
ISPRS Int. J. Geo-Inf. 2018, 7(10), 389; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi7100389 - 26 Sep 2018
Cited by 76 | Viewed by 7405
Abstract
In this paper, we present the optical image simulation from synthetic aperture radar (SAR) data using deep learning based methods. Two models, i.e., optical image simulation directly from the SAR data and from multi-temporal SAR-optical data, are proposed to testify the possibilities. The [...] Read more.
In this paper, we present the optical image simulation from synthetic aperture radar (SAR) data using deep learning based methods. Two models, i.e., optical image simulation directly from the SAR data and from multi-temporal SAR-optical data, are proposed to testify the possibilities. The deep learning based methods that we chose to achieve the models are a convolutional neural network (CNN) with a residual architecture and a conditional generative adversarial network (cGAN). We validate our models using the Sentinel-1 and -2 datasets. The experiments demonstrate that the model with multi-temporal SAR-optical data can successfully simulate the optical image; meanwhile, the state-of-the-art model with simple SAR data as input failed. The optical image simulation results indicate the possibility of SAR-optical information blending for the subsequent applications such as large-scale cloud removal, and optical data temporal super-resolution. We also investigate the sensitivity of the proposed models against the training samples, and reveal possible future directions. Full article
(This article belongs to the Special Issue Data Mining and Feature Extraction from Satellite Images and Point Cloud Data)
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20 pages, 10671 KiB  
Article
Feature Extraction and Selection of Sentinel-1 Dual-Pol Data for Global-Scale Local Climate Zone Classification
by Jingliang Hu, Pedram Ghamisi and Xiao Xiang Zhu
ISPRS Int. J. Geo-Inf. 2018, 7(9), 379; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi7090379 - 18 Sep 2018
Cited by 66 | Viewed by 5951
Abstract
The concept of the local climate zone (LCZ) has been recently proposed as a generic land-cover/land-use classification scheme. It divides urban regions into 17 categories based on compositions of man-made structures and natural landscapes. Although it was originally designed for temperature study, the [...] Read more.
The concept of the local climate zone (LCZ) has been recently proposed as a generic land-cover/land-use classification scheme. It divides urban regions into 17 categories based on compositions of man-made structures and natural landscapes. Although it was originally designed for temperature study, the morphological structure concealed in LCZs also reflects economic status and population distribution. To this end, global LCZ classification is of great value for worldwide studies on economy and population. Conventional classification approaches are usually successful for an individual city using optical remote sensing data. This paper, however, attempts for the first time to produce global LCZ classification maps using polarimetric synthetic aperture radar (PolSAR) data. Specifically, we first produce polarimetric features, local statistical features, texture features, and morphological features and compare them, with respect to their classification performance. Here, an ensemble classifier is investigated, which is trained and tested on already separated transcontinental cities. Considering the challenging global scope this work handles, we conclude the classification accuracy is not yet satisfactory. However, Sentinel-1 dual-Pol SAR data could contribute the classification for several LCZ classes. According to our feature studies, the combination of local statistical features and morphological features yields the best classification results with 61.8% overall accuracy (OA), which is 3% higher than the OA produced by the second best features combination. The 3% is considerably large for a global scale. Based on our feature importance analysis, features related to VH polarized data contributed the most to the eventual classification result. Full article
(This article belongs to the Special Issue Data Mining and Feature Extraction from Satellite Images and Point Cloud Data)
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21 pages, 5712 KiB  
Article
Road Extraction from VHR Remote-Sensing Imagery via Object Segmentation Constrained by Gabor Features
by Li Chen, Qing Zhu, Xiao Xie, Han Hu and Haowei Zeng
ISPRS Int. J. Geo-Inf. 2018, 7(9), 362; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi7090362 - 02 Sep 2018
Cited by 23 | Viewed by 4558
Abstract
Automatic road extraction from remote-sensing imagery plays an important role in many applications. However, accurate and efficient extraction from very high-resolution (VHR) images remains difficult because of, for example, increased data size and superfluous details, the spatial and spectral diversity of road targets, [...] Read more.
Automatic road extraction from remote-sensing imagery plays an important role in many applications. However, accurate and efficient extraction from very high-resolution (VHR) images remains difficult because of, for example, increased data size and superfluous details, the spatial and spectral diversity of road targets, disturbances (e.g., vehicles, shadows of trees, and buildings), the necessity of finding weak road edges while avoiding noise, and the fast-acquisition requirement of road information for crisis response. To solve these difficulties, a two-stage method combining edge information and region characteristics is presented. In the first stage, convolutions are executed by applying Gabor wavelets in the best scale to detect Gabor features with location and orientation information. The features are then merged into one response map for connection analysis. In the second stage, highly complete, connected Gabor features are used as edge constraints to facilitate stable object segmentation and limit region growing. Finally, segmented objects are evaluated by some fundamental shape features to eliminate nonroad objects. The results indicate the validity and superiority of the proposed method to efficiently extract accurate road targets from VHR remote-sensing images. Full article
(This article belongs to the Special Issue Data Mining and Feature Extraction from Satellite Images and Point Cloud Data)
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19 pages, 3776 KiB  
Article
Representative Band Selection for Hyperspectral Image Classification
by Fuding Xie, Fangfei Li, Cunkuan Lei and Lina Ke
ISPRS Int. J. Geo-Inf. 2018, 7(9), 338; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi7090338 - 22 Aug 2018
Cited by 23 | Viewed by 3992
Abstract
The high dimensionality of hyperspectral images (HSIs) brings great difficulty for their later data processing. Band selection, as a commonly used dimension reduction technique, is the selection of optimal band combinations from the original bands, while attempting to remove the redundancy between bands [...] Read more.
The high dimensionality of hyperspectral images (HSIs) brings great difficulty for their later data processing. Band selection, as a commonly used dimension reduction technique, is the selection of optimal band combinations from the original bands, while attempting to remove the redundancy between bands and maintain a good classification ability. In this study, a novel hybrid filter-wrapper band selection method is proposed by a three-step strategy, i.e., band subset decomposition, band selection and band optimization. Based on the information gain (IG) and the spectral curve of the hyperspectral dataset, the band subset decomposition technique is improved, and a random selection strategy is suggested. The implementation of the first two steps addresses the problem of reducing inter-band redundancy. An optimization strategy based on a gray wolf optimizer (GWO) ensures that the selected band combination has a good classification ability. The classification performance of the selected band combination is verified on the Indian Pines, Pavia University and Salinas hyperspectral datasets with the aid of support vector machine (SVM) with a five-fold cross-validation. By comparing the proposed IG-GWO method with five state-of-the-art band selection approaches, the superiority of the proposed method for HSIs classification is experimentally demonstrated on three well-known hyperspectral datasets. Full article
(This article belongs to the Special Issue Data Mining and Feature Extraction from Satellite Images and Point Cloud Data)
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20 pages, 10128 KiB  
Article
Analysis of Scattering Properties of Continuous Slow-Release Slicks on the Sea Surface Based on Polarimetric Synthetic Aperture Radar
by Guannan Li, Ying Li, Bingxin Liu, Yongchao Hou and Jianchao Fan
ISPRS Int. J. Geo-Inf. 2018, 7(7), 237; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi7070237 - 21 Jun 2018
Cited by 15 | Viewed by 3662
Abstract
This study employs polarimetric synthetic aperture radar (Pol-SAR) to examine the scattering properties of continuous slow-release oil slicks on the sea surface. The objective is to extract and analyze the general polarization scattering properties of continuous slow-release slicks, i.e., those slicks that consist [...] Read more.
This study employs polarimetric synthetic aperture radar (Pol-SAR) to examine the scattering properties of continuous slow-release oil slicks on the sea surface. The objective is to extract and analyze the general polarization scattering properties of continuous slow-release slicks, i.e., those slicks that consist of substances released at a fairly slow and relatively constant rate, and to determine the influence of the slick formation process on these properties. Using multi-polarization feature parameters derived from the averaged coherency matrix, we find that the scattering mechanisms related to the continuous slow-release slicks differ from those of anthropogenic slicks, possibly as a result of the multiple scattering mechanisms that occur between the interfaces formed by the thick slick layer. Combinations of entropy (H) and modified anisotropy (A12) are relatively robust parameters for identifying continuous slow-release slicks under different sea conditions, and may serve as a reference parameter for slick detection. Full article
(This article belongs to the Special Issue Data Mining and Feature Extraction from Satellite Images and Point Cloud Data)
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20 pages, 16327 KiB  
Article
Semi-Supervised Ground-to-Aerial Adaptation with Heterogeneous Features Learning for Scene Classification
by Zhipeng Deng, Hao Sun and Shilin Zhou
ISPRS Int. J. Geo-Inf. 2018, 7(5), 182; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi7050182 - 10 May 2018
Cited by 9 | Viewed by 3630
Abstract
Currently, huge quantities of remote sensing images (RSIs) are becoming available. Nevertheless, the scarcity of labeled samples hinders the semantic understanding of RSIs. Fortunately, many ground-level image datasets with detailed semantic annotations have been collected in the vision community. In this paper, we [...] Read more.
Currently, huge quantities of remote sensing images (RSIs) are becoming available. Nevertheless, the scarcity of labeled samples hinders the semantic understanding of RSIs. Fortunately, many ground-level image datasets with detailed semantic annotations have been collected in the vision community. In this paper, we attempt to exploit the abundant labeled ground-level images to build discriminative models for overhead-view RSI classification. However, images from the ground-level and overhead view are represented by heterogeneous features with different distributions; how to effectively combine multiple features and reduce the mismatch of distributions are two key problems in this scene-model transfer task. Specifically, a semi-supervised manifold-regularized multiple-kernel-learning (SMRMKL) algorithm is proposed for solving these problems. We employ multiple kernels over several features to learn an optimal combined model automatically. Multi-kernel Maximum Mean Discrepancy (MK-MMD) is utilized to measure the data mismatch. To make use of unlabeled target samples, a manifold regularized semi-supervised learning process is incorporated into our framework. Extensive experimental results on both cross-view and aerial-to-satellite scene datasets demonstrate that: (1) SMRMKL has an appealing extension ability to effectively fuse different types of visual features; and (2) manifold regularization can improve the adaptation performance by utilizing unlabeled target samples. Full article
(This article belongs to the Special Issue Data Mining and Feature Extraction from Satellite Images and Point Cloud Data)
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16 pages, 6276 KiB  
Article
Multilevel Cloud Detection for High-Resolution Remote Sensing Imagery Using Multiple Convolutional Neural Networks
by Yang Chen, Rongshuang Fan, Muhammad Bilal, Xiucheng Yang, Jingxue Wang and Wei Li
ISPRS Int. J. Geo-Inf. 2018, 7(5), 181; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi7050181 - 09 May 2018
Cited by 70 | Viewed by 7116
Abstract
In high-resolution image data, multilevel cloud detection is a key task for remote sensing data processing. Generally, it is difficult to obtain high accuracy for multilevel cloud detection when using satellite imagery which only contains visible and near-infrared spectral bands. So, multilevel cloud [...] Read more.
In high-resolution image data, multilevel cloud detection is a key task for remote sensing data processing. Generally, it is difficult to obtain high accuracy for multilevel cloud detection when using satellite imagery which only contains visible and near-infrared spectral bands. So, multilevel cloud detection for high-resolution remote sensing imagery is challenging. In this paper, a new multilevel cloud detection technique is proposed based on the multiple convolutional neural networks for high-resolution remote sensing imagery. In order to avoid input the entire image into the network for cloud detection, the adaptive simple linear iterative clustering (A-SCLI) algorithm was applied to the segmentation of the satellite image to obtain good-quality superpixels. After that, a new multiple convolutional neural networks (MCNNs) architecture is designed to extract multiscale features from each superpixel, and the superpixels are marked as thin cloud, thick cloud, cloud shadow, and non-cloud. The results suggest that the proposed method can detect multilevel clouds and obtain a high accuracy for high-resolution remote sensing imagery. Full article
(This article belongs to the Special Issue Data Mining and Feature Extraction from Satellite Images and Point Cloud Data)
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20 pages, 4061 KiB  
Review
Comparison of Main Approaches for Extracting Behavior Features from Crowd Flow Analysis
by Zeinab Ebrahimpour, Wanggen Wan, Ofelia Cervantes, Tianhang Luo and Hidayat Ullah
ISPRS Int. J. Geo-Inf. 2019, 8(10), 440; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8100440 - 07 Oct 2019
Cited by 23 | Viewed by 3925
Abstract
Extracting features from crowd flow analysis has become an important research challenge due to its social cost and the impact of inadequate planning of high-quality services and security monitoring on the lives of citizens. This paper descriptively reviews and compares existing crowd analysis [...] Read more.
Extracting features from crowd flow analysis has become an important research challenge due to its social cost and the impact of inadequate planning of high-quality services and security monitoring on the lives of citizens. This paper descriptively reviews and compares existing crowd analysis approaches based on different data sources. This survey provides the fundamentals of crowd analysis and considers three main approaches: crowd video analysis, crowd spatio-temporal analysis, and crowd social media analysis. The key research contributions in each approach are presented, and the most significant techniques and algorithms used to improve the precision of results that could be integrated into solutions to enhance the quality of services in a smart city are analyzed. Full article
(This article belongs to the Special Issue Data Mining and Feature Extraction from Satellite Images and Point Cloud Data)
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