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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 December 2019) | Viewed by 54437

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Special Issue Editors

Dr. Brian Alan Johnson

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Guest Editor

Natural Resources and Ecosystem Services, Institute for Global Environmental Strategies, Kanagawa 240-0115, Japan
Interests: geographic information systems (GIS); remote sensing; spatial modeling; and data mining for urban and environmental analysis and planning; mapping urban land cover (green space, impervious surfaces, etc.); monitoring forest health using fine resolution satellite imagery
Special Issues, Collections and Topics in MDPI journals

Dr. Lei Ma

E-Mail Website
Guest Editor

Department of Geographic Information Science, Nanjing University, Nanjing 210046, China
Interests: land cover mapping; urban remote sensing; machine learning; deep learning; geoinformation; very high resolution; object-based image analysis; big data; automation; change detection; uncertainty; human geography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Image segmentation has become a major topic of interest in the environmental remote sensing field due to the ever-increasing quantity of high spatial resolution (HSR) imagery acquired from satellites, airplanes, unmanned aerial vehicles (UAVs), and other platforms. Image segmentation involves sub-dividing an image into homogeneous regions that ideally represent real-world objects of interest, and it has been shown to be particularly beneficial when the objects of interest in an image are larger than the image pixels, as is often the case with HSR images. Image segmentation is a fundamental component of geographic object-based image analysis (GEOBIA).

HSR data from various types of sensors, e.g., multispectral, hyperspectral, synthetic aperture radar (SAR), light detection and ranging (LIDAR), and thermal infrared sensors, are now becoming widely available. Additionally, a large and ever-growing archive of freely-available moderate spatial resolution imagery (e.g., Landsat and Sentinel data) also bring new challenges for segmentation and analysis of dense time-series imagery. Aside from the data acquired by traditional sensors, citizen sensor data, e.g., volunteered geographic information (VGI), has become yet another promising source of geo-data for environmental monitoring and analysis. Thus, new image segmentation and GEOBIA approaches that can effectively utilize these types of multi-sensor/multi-temporal data are particularly needed.

This Special Issue welcomes submissions representing advances in remote sensing image segmentation methods, strategies, and/or applications. Submissions may cover a wide range of topics including (but not limited to):

Image segmentation algorithm development and evaluation
Segmentation parameter selection and “optimization”
Segmentation approaches for multi-source/multi-sensor data analysis
Segmentation approaches for multi-temporal/time-series data analysis (e.g., vegetation phenology monitoring or land use/land cover change mapping)
Segmentation approaches for big data analysis

Dr. Brian Alan Johnson
Dr. Lei Ma
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.

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Keywords

Image segmentation
GEOBIA
Remote sensing data fusion
Object-based image classification
Segmentation parameter optimization

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Published Papers (9 papers)

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Editorial

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9 pages, 1679 KiB

Open AccessEditorial

Image Segmentation and Object-Based Image Analysis for Environmental Monitoring: Recent Areas of Interest, Researchers’ Views on the Future Priorities

by Brian Alan Johnson and Lei Ma

Remote Sens. 2020, 12(11), 1772; https://doi.org/10.3390/rs12111772 - 1 Jun 2020

Cited by 31 | Viewed by 5508

Abstract

Image segmentation and geographic object-based image analysis (GEOBIA) were proposed around the turn of the century as a means to analyze high-spatial-resolution remote sensing images. Since then, object-based approaches have been used to analyze a wide range of images for numerous applications. In this Editorial, we present some highlights of image segmentation and GEOBIA research from the last two years (2018–2019), including a Special Issue published in the journal Remote Sensing. As a final contribution of this special issue, we have shared the views of 45 other researchers (corresponding authors of published papers on GEOBIA in 2018–2019) on the current state and future priorities of this field, gathered through an online survey. Most researchers surveyed acknowledged that image segmentation/GEOBIA approaches have achieved a high level of maturity, although the need for more free user-friendly software and tools, further automation, better integration with new machine-learning approaches (including deep learning), and more suitable accuracy assessment methods was frequently pointed out. Full article

(This article belongs to the Special Issue Image Segmentation for Environmental Monitoring)

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Research

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22 pages, 6716 KiB

Open AccessArticle

Landslides Information Extraction Using Object-Oriented Image Analysis Paradigm Based on Deep Learning and Transfer Learning

by Heng Lu, Lei Ma, Xiao Fu, Chao Liu, Zhi Wang, Min Tang and Naiwen Li

Remote Sens. 2020, 12(5), 752; https://doi.org/10.3390/rs12050752 - 25 Feb 2020

Cited by 70 | Viewed by 6679

Abstract

How to acquire landslide disaster information quickly and accurately has become the focus and difficulty of disaster prevention and relief by remote sensing. Landslide disasters are generally featured by sudden occurrence, proposing high demand for emergency data acquisition. The low-altitude Unmanned Aerial Vehicle (UAV) remote sensing technology is widely applied to acquire landslide disaster data, due to its convenience, high efficiency, and ability to fly at low altitude under cloud. However, the spectrum information of UAV images is generally deficient and manual interpretation is difficult for meeting the need of quick acquisition of emergency data. Based on this, UAV images of high-occurrence areas of landslide disaster in Wenchuan County and Baoxing County in Sichuan Province, China were selected for research in the paper. Firstly, the acquired UAV images were pre-processed to generate orthoimages. Subsequently, multi-resolution segmentation was carried out to obtain image objects, and the barycenter of each object was calculated to generate a landslide sample database (including positive and negative samples) for deep learning. Next, four landslide feature models of deep learning and transfer learning, namely Histograms of Oriented Gradients (HOG), Bag of Visual Word (BOVW), Convolutional Neural Network (CNN), and Transfer Learning (TL) were compared, and it was found that the TL model possesses the best feature extraction effect, so a landslide extraction method based on the TL model and object-oriented image analysis (TLOEL) was proposed; finally, the TLOEL method was compared with the object-oriented nearest neighbor classification (NNC) method. The research results show that the accuracy of the TLOEL method is higher than the NNC method, which can not only achieve the edge extraction of large landslides, but also detect and extract middle and small landslides accurately that are scatteredly distributed. Full article

(This article belongs to the Special Issue Image Segmentation for Environmental Monitoring)

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20 pages, 4536 KiB

Open AccessArticle

A Robust Rule-Based Ensemble Framework Using Mean-Shift Segmentation for Hyperspectral Image Classification

by Majid Shadman Roodposhti, Arko Lucieer, Asim Anees and Brett A. Bryan

Remote Sens. 2019, 11(17), 2057; https://doi.org/10.3390/rs11172057 - 1 Sep 2019

Cited by 7 | Viewed by 4257

Abstract

This paper assesses the performance of DoTRules—a dictionary of trusted rules—as a supervised rule-based ensemble framework based on the mean-shift segmentation for hyperspectral image classification. The proposed ensemble framework consists of multiple rule sets with rules constructed based on different class frequencies and sequences of occurrences. Shannon entropy was derived for assessing the uncertainty of every rule and the subsequent filtering of unreliable rules. DoTRules is not only a transparent approach for image classification but also a tool to map rule uncertainty, where rule uncertainty assessment can be applied as an estimate of classification accuracy prior to image classification. In this research, the proposed image classification framework is implemented using three world reference hyperspectral image datasets. We found that the overall accuracy of classification using the proposed ensemble framework was superior to state-of-the-art ensemble algorithms, as well as two non-ensemble algorithms, at multiple training sample sizes. We believe DoTRules can be applied more generally to the classification of discrete data such as hyperspectral satellite imagery products. Full article

(This article belongs to the Special Issue Image Segmentation for Environmental Monitoring)

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33 pages, 8671 KiB

Open AccessArticle

Direct, ECOC, ND and END Frameworks—Which One Is the Best? An Empirical Study of Sentinel-2A MSIL1C Image Classification for Arid-Land Vegetation Mapping in the Ili River Delta, Kazakhstan

by Alim Samat, Naoto Yokoya, Peijun Du, Sicong Liu, Long Ma, Yongxiao Ge, Gulnura Issanova, Abdula Saparov, Jilili Abuduwaili and Cong Lin

Remote Sens. 2019, 11(16), 1953; https://doi.org/10.3390/rs11161953 - 20 Aug 2019

Cited by 12 | Viewed by 4674

Abstract

To facilitate the advances in Sentinel-2A products for land cover from Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat imagery, Sentinel-2A MultiSpectral Instrument Level-1C (MSIL1C) images are investigated for large-scale vegetation mapping in an arid land environment that is located in the Ili River delta, Kazakhstan. For accurate classification purposes, multi-resolution segmentation (MRS) based extended object-guided morphological profiles (EOMPs) are proposed and then compared with conventional morphological profiles (MPs), MPs with partial reconstruction (MPPR), object-guided MPs (OMPs), OMPs with mean values (OMPsM), and object-oriented (OO)-based image classification techniques. Popular classifiers, such as C4.5, an extremely randomized decision tree (ERDT), random forest (RaF), rotation forest (RoF), classification via random forest regression (CVRFR), ExtraTrees, and radial basis function (RBF) kernel-based support vector machines (SVMs) are adopted to answer the question of whether nested dichotomies (ND) and ensembles of ND (END) are truly superior to direct and error-correcting output code (ECOC) multiclass classification frameworks. Finally, based on the results, the following conclusions are drawn: 1) the superior performance of OO-based techniques over MPs, MPPR, OMPs, and OMPsM is clear for Sentinel-2A MSIL1C image classification, while the best results are achieved by the proposed EOMPs; 2) the superior performance of ND, ND with class balancing (NDCB), ND with data balancing (NDDB), ND with random-pair selection (NDRPS), and ND with further centroid (NDFC) over direct and ECOC frameworks is not confirmed, especially in the cases of using weak classifiers for low-dimensional datasets; 3) from computationally efficient, high accuracy, redundant to data dimensionality and easy of implementations points of view, END, ENDCB, ENDDB, and ENDRPS are alternative choices to direct and ECOC frameworks; 4) surprisingly, because in the ensemble learning (EL) theorem, “weaker” classifiers (ERDT here) always have a better chance of reaching the trade-off between diversity and accuracy than “stronger” classifies (RaF, ExtraTrees, and SVM here), END with ERDT (END-ERDT) achieves the best performance with less than a 0.5% difference in the overall accuracy (OA) values, but is 100 to 10000 times faster than END with RaF and ExtraTrees, and ECOC with SVM while using different datasets with various dimensions; and, 5) Sentinel-2A MSIL1C is better choice than the land cover products from MODIS and Landsat imagery for vegetation species mapping in an arid land environment, where the vegetation species are critically important, but sparsely distributed. Full article

(This article belongs to the Special Issue Image Segmentation for Environmental Monitoring)

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20 pages, 16859 KiB

Open AccessEditor’s ChoiceArticle

Operational Large-Scale Segmentation of Imagery Based on Iterative Elimination

by James D. Shepherd, Pete Bunting and John R. Dymond

Remote Sens. 2019, 11(6), 658; https://doi.org/10.3390/rs11060658 - 18 Mar 2019

Cited by 39 | Viewed by 8940

Abstract

Image classification and interpretation are greatly aided through the use of image segmentation. Within the field of environmental remote sensing, image segmentation aims to identify regions of unique or dominant ground cover from their attributes such as spectral signature, texture and context. However, many approaches are not scalable for national mapping programmes due to limits in the size of images that can be processed. Therefore, we present a scalable segmentation algorithm, which is seeded using k-means and provides support for a minimum mapping unit through an innovative iterative elimination process. The algorithm has also been demonstrated for the segmentation of time series datasets capturing both the intra-image variation and change regions. The quality of the segmentation results was assessed by comparison with reference segments along with statistics on the inter- and intra-segment spectral variation. The technique is computationally scalable and is being actively used within the national land cover mapping programme for New Zealand. Additionally, 30-m continental mosaics of Landsat and ALOS-PALSAR have been segmented for Australia in support of national forest height and cover mapping. The algorithm has also been made freely available within the open source Remote Sensing and GIS software Library (RSGISLib). Full article

(This article belongs to the Special Issue Image Segmentation for Environmental Monitoring)

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15 pages, 3010 KiB

Open AccessArticle

Improving Ecotope Segmentation by Combining Topographic and Spectral Data

by Julien Radoux, Axel Bourdouxhe, William Coos, Marc Dufrêne and Pierre Defourny

Remote Sens. 2019, 11(3), 354; https://doi.org/10.3390/rs11030354 - 11 Feb 2019

Cited by 23 | Viewed by 6035

Abstract

Ecotopes are the smallest ecologically distinct landscape features in a landscape mapping and classification system. Mapping ecotopes therefore enables the measurement of ecological patterns, process and change. In this study, a multi-source GEOBIA workflow is used to improve the automated delineation and descriptions of ecotopes. Aerial photographs and LIDAR data provide input for landscape segmentation based on spectral signature, height structure and topography. Each segment is then characterized based on the proportion of land cover features identified at 2 m pixel-based classification. The results show that the use of hillshade bands simultaneously with spectral bands increases the consistency of the ecotope delineation. These results are promising to further describe biotopes of high ecological conservation value, as suggested by a successful test on ravine forest biotope. Full article

(This article belongs to the Special Issue Image Segmentation for Environmental Monitoring)

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20 pages, 19347 KiB

Open AccessFeature PaperArticle

Multiscale Optimized Segmentation of Urban Green Cover in High Resolution Remote Sensing Image

by Pengfeng Xiao, Xueliang Zhang, Hongmin Zhang, Rui Hu and Xuezhi Feng

Remote Sens. 2018, 10(11), 1813; https://doi.org/10.3390/rs10111813 - 15 Nov 2018

Cited by 21 | Viewed by 4992

Abstract

The urban green cover in high-spatial resolution (HR) remote sensing images have obvious multiscale characteristics, it is thus not possible to properly segment all features using a single segmentation scale because over-segmentation or under-segmentation often occurs. In this study, an unsupervised cross-scale optimization method specifically for urban green cover segmentation is proposed. A global optimal segmentation is first selected from multiscale segmentation results by using an optimization indicator. The regions in the global optimal segmentation are then isolated into under- and fine-segmentation parts. The under-segmentation regions are further locally refined by using the same indicator as that in global optimization. Finally, the fine-segmentation part and the refined under-segmentation part are combined to obtain the final cross-scale optimized result. The green cover objects can be segmented at their specific optimal segmentation scales in the optimized segmentation result to reduce both under- and over-segmentation errors. Experimental results on two test HR datasets verify the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Image Segmentation for Environmental Monitoring)

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19 pages, 6489 KiB

Open AccessArticle

Edge Dependent Chinese Restaurant Process for Very High Resolution (VHR) Satellite Image Over-Segmentation

by Hong Tang, Xuejun Zhai and Wei Huang

Remote Sens. 2018, 10(10), 1519; https://doi.org/10.3390/rs10101519 - 21 Sep 2018

Cited by 2 | Viewed by 3694

Abstract

Image over-segmentation aims to partition an image into spatially adjacent and spectrally homogeneous regions. It could reduce the complexity of image representation and enhance the efficiency of subsequent image processing. Previously, many methods for image over-segmentation have been proposed, but almost of them need to assign model parameters in advance, e.g., the number of segments. In this paper, a nonparametric clustering model is employed to the over-segmentation of Very High Resolution (VHR) satellite images, in which the number of segments can automatically be inferred from the observed data. The proposed model is called the Edge Dependent Chinese restaurant process (EDCRP), which extends the distance dependent Chinese restaurant process to make full use of local image structure information, i.e., edges. Experimental results show that the presented methods outperform state of the art methods for image over-segmentation in terms of both metrics based direct evaluation and classification based indirect evaluation. Full article

(This article belongs to the Special Issue Image Segmentation for Environmental Monitoring)

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23 pages, 22046 KiB

Open AccessFeature PaperArticle

Scale Matters: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Large and Heterogeneous Satellite Images

by Stefanos Georganos, Tais Grippa, Moritz Lennert, Sabine Vanhuysse, Brian Alan Johnson and Eléonore Wolff

Remote Sens. 2018, 10(9), 1440; https://doi.org/10.3390/rs10091440 - 9 Sep 2018

Cited by 40 | Viewed by 6388

Abstract

To classify Very-High-Resolution (VHR) imagery, Geographic Object Based Image Analysis (GEOBIA) is the most popular method used to produce high quality Land-Use/Land-Cover maps. A crucial step in GEOBIA is the appropriate parametrization of the segmentation algorithm prior to the classification. However, little effort has been made to automatically optimize GEOBIA algorithms in an unsupervised and spatially meaningful manner. So far, most Unsupervised Segmentation Parameter Optimization (USPO) techniques, assume spatial stationarity for the whole study area extent. This can be questionable, particularly for applications in geographically large and heterogeneous urban areas. In this study, we employed a novel framework named Spatially Partitioned Unsupervised Segmentation Parameter Optimization (SPUSPO), which optimizes segmentation parameters locally rather than globally, for the Sub-Saharan African city of Ouagadougou, Burkina Faso, using WorldView-3 imagery (607 km²). The results showed that there exists significant spatial variation in the optimal segmentation parameters suggested by USPO across the whole scene, which follows landscape patterns—mainly of the various built-up and vegetation types. The most appropriate automatic spatial partitioning method from the investigated techniques, was an edge-detection cutline algorithm, which achieved higher classification accuracy than a global optimization, better predicted built-up regions, and did not suffer from edge effects. The overall classification accuracy using SPUSPO was 90.5%, whilst the accuracy from undertaking a traditional USPO approach was 89.5%. The differences between them were statistically significant (p < 0.05) based on a McNemar’s test of similarity. Our methods were validated further by employing a segmentation goodness metric, Area Fit Index (AFI)on building objects across Ouagadougou, which suggested that a global USPO was more over-segmented than our local approach. The mean AFI values for SPUSPO and USPO were 0.28 and 0.36, respectively. Finally, the processing was carried out using the open-source software GRASS GIS, due to its efficiency in raster-based applications. Full article

(This article belongs to the Special Issue Image Segmentation for Environmental Monitoring)

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