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Object Based Image Analysis for Remote Sensing

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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 September 2020) | Viewed by 51898

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Special Issue Editors

Dr. Peter Hofmann

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1. ITC, University Twente, Hengelosestraat 99, 7514 AE Enschede, The Netherlands
2. Deggendorf Institute of Technology, Dieter-Görlitz-Platz 1, 94469 Deggendorf, Germany
Interests: remote sensing; (object-based) image analysis; artificial intelligence; GIScience
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Raul Queiroz Feitosa

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Department of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro 22451-040, RJ, Brazil
Interests: pattern recognition for remote sensing; image analysis; remote sensing applications; change detection
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Cláudia Maria de Almeida

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Division for Earth Observation and Geinformatics, National Institute for Space Research—INPE, Av. dos Astronautas, 1758-SERE I-Room 6, 12220-140 Sao Jose dos Campos, SP, Brazil
Interests: cellular automata modeling; machine and deep learning for environmental sciences; GEOBIA; high spatial resolution sensors; urban remote sensing; urban modeling
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Prof. Dr. Gilson Alexandre Ostwald Pedro da Costa

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Department of Informatics and Computer Science, Institute of Mathematics and Statistics (IME), Rio de Janeiro State University (UERJ), Rio de Janeiro 20550-013, RJ, Brazil
Interests: pattern recognition for remote sensing; image analysis; remote sensing applications; change detection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Object-based Image Analysis (OBIA) has evolved to a widespread methodology for image analysis, especially in the context of remote sensing. With the emergence of Very High Resolution (VHR) remote sensing data it turned out that methods which operate on image segments instead of single pixels show lots of advantages when analyzing the content of remote sensing data. With the advent of user friendly software, which allowed to analyze remote sensing data in OBIA manner, OBIA has been further boosted in the remote sensing and GIS community. The numerous scientific publications dealing with OBIA in the remote sensing and GIS domain show, that this methodology has meanwhile established – probably even as a paradigm for image analysis. Simultaneously, the methodology itself underwent a step-by-step evolution, comprising the development of new segmentation methods, the integration of new classification methods and the development of new methods for change detection and monitoring, just to name a few. Meanwhile, the integration of new methods - mainly originating in AI - plays an important role for OBIA. Exemplary, the explicit formulation and management of knowledge, the application of artificial learning and learning mechanisms, but also self-organizing agent-based systems are interesting new developments in OBIA which originate in AI.

In this special issue, we first intend to outline the state-of-the-art in OBIA for remote sensing and the methodologies it comprises meanwhile. Further, we intend to present recent concepts, frameworks and new methods which found their way to OBIA in conjunction with recent applications and success stories of OBIA in remote sensing. This will span a wide spectrum ranging from: image segmentation methods, software engineering in the context of OBIA, semantic modelling and reasoning, ontologies and knowledge representations, classification methods including Complex Neural Networks (CNNs) to self-organizing approaches such as multi-agent systems. Further, OBIA-specific approaches of change detection and monitoring as well as the incorporation of non-remote sensing and even unstructured data are further aspects we want to deal with. Last but not least cloud computing and Big Earth Data in the context of OBIA are challenging fields we would like to spot at.

We would like to invite colleagues to submit articles about their recent research on any of the following topics but not restricted to:

Image segmentation and joined aspects, such as optimization, quality assessment, transferability, etc.
Software development and engineering in the context of OBIA including robustness and quality assessment.
Knowledge representation and management, including ontologies and reasoning.
Classification methods including CNNs and other ANN-based methods.
Self-organizing methods such as Multi-Agent Systems in OBIA.
Object-based change detection and monitoring methods.
Data integration and usage.
Cloud computing and Big Earth Data in OBIA.
Applications of OBIA in remote sensing and success stories with OBIA.

Prof. Dr. Raul Queiroz Feitosa
Dr. Peter Hofmann
Prof. Dr. Cláudia Maria de Almeida
Prof. Dr. Gilson Alexandre Ostwald Pedro da Costa
Guest Editors

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Keywords

OBIA
GeOBIA
remote sensing
image analysis
artificial intelligence
knowledge representation
big earth data

Published Papers (13 papers)

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Research

17 pages, 4941 KiB

Open AccessArticle

Uncertainty Analysis of Object-Based Land-Cover Classification Using Sentinel-2 Time-Series Data

by Lei Ma, Michael Schmitt and Xiaoxiang Zhu

Remote Sens. 2020, 12(22), 3798; https://doi.org/10.3390/rs12223798 - 19 Nov 2020

Cited by 9 | Viewed by 2537

Abstract

Recently, time-series from optical satellite data have been frequently used in object-based land-cover classification. This poses a significant challenge to object-based image analysis (OBIA) owing to the presence of complex spatio-temporal information in the time-series data. This study evaluates object-based land-cover classification in the northern suburbs of Munich using time-series from optical Sentinel data. Using a random forest classifier as the backbone, experiments were designed to analyze the impact of the segmentation scale, features (including spectral and temporal features), categories, frequency, and acquisition timing of optical satellite images. Based on our analyses, the following findings are reported: (1) Optical Sentinel images acquired over four seasons can make a significant contribution to the classification of agricultural areas, even though this contribution varies between spectral bands for the same period. (2) The use of time-series data alleviates the issue of identifying the “optimal” segmentation scale. The finding of this study can provide a more comprehensive understanding of the effects of classification uncertainty on object-based dense multi-temporal image classification. Full article

(This article belongs to the Special Issue Object Based Image Analysis for Remote Sensing)

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20 pages, 14199 KiB

Open AccessArticle

A Fast and Effective Method for Unsupervised Segmentation Evaluation of Remote Sensing Images

by Maofan Zhao, Qingyan Meng, Linlin Zhang, Die Hu, Ying Zhang and Mona Allam

Remote Sens. 2020, 12(18), 3005; https://doi.org/10.3390/rs12183005 - 15 Sep 2020

Cited by 4 | Viewed by 3146

Abstract

The segmentation of remote sensing images with high spatial resolution is important and fundamental in geographic object-based image analysis (GEOBIA), so evaluating segmentation results without prior knowledge is an essential part in segmentation algorithms comparison, segmentation parameters selection, and optimization. In this study, we proposed a fast and effective unsupervised evaluation (UE) method using the area-weighted variance (WV) as intra-segment homogeneity and the difference to neighbor pixels (DTNP) as inter-segment heterogeneity. Then these two measures were combined into a fast-global score (FGS) to evaluate the segmentation. The effectiveness of DTNP and FGS was demonstrated by visual interpretation as qualitative analysis and supervised evaluation (SE) as quantitative analysis. For this experiment, the ‘‘Multi-resolution Segmentation’’ algorithm in eCognition was adopted in the segmentation and four typical study areas of GF-2 images were used as test data. The effectiveness analysis of DTNP shows that it can keep stability and remain sensitive to both over-segmentation and under-segmentation compared to two existing inter-segment heterogeneity measures. The effectiveness and computational cost analysis of FGS compared with two existing UE methods revealed that FGS can effectively evaluate segmentation results with the lowest computational cost. Full article

(This article belongs to the Special Issue Object Based Image Analysis for Remote Sensing)

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27 pages, 5987 KiB

Open AccessArticle

Introducing GEOBIA to Landscape Imageability Assessment: A Multi-Temporal Case Study of the Nature Reserve “Kózki”, Poland

by Szymon Chmielewski, Andrzej Bochniak, Asya Natapov and Piotr Wężyk

Remote Sens. 2020, 12(17), 2792; https://doi.org/10.3390/rs12172792 - 27 Aug 2020

Cited by 11 | Viewed by 3606

Abstract

Geographic object-based image analysis (GEOBIA) is a primary remote sensing tool utilized in land-cover mapping and change detection. Land-cover patches are the primary data source for landscape metrics and ecological indicator calculations; however, their application to visual landscape character (VLC) indicators was little investigated to date. To bridge the knowledge gap between GEOBIA and VLC, this paper puts forward the theoretical concept of using viewpoint as a landscape imageability indicator into the practice of a multi-temporal land-cover case study and explains how to interpret the indicator. The study extends the application of GEOBIA to visual landscape indicator calculations. In doing so, eight different remote sensing imageries are the object of GEOBIA, starting from a historical aerial photograph (1957) and CORONA declassified scene (1965) to contemporary (2018) UAV-delivered imagery. The multi-temporal GEOBIA-delivered land-cover patches are utilized to find the minimal isovist set of viewpoints and to calculate three imageability indicators: the number, density, and spacing of viewpoints. The calculated indicator values, viewpoint rank, and spatial arrangements allow us to describe the scale, direction, rate, and reasons for VLC changes over the analyzed 60 years of landscape evolution. We found that the case study nature reserve (“Kózki”, Poland) landscape imageability transformed from visually impressive openness to imageability due to the impression of several landscape rooms enclosed by forest walls. Our results provide proof that the number, rank, and spatial arrangement of viewpoints constitute landscape imageability measured with the proposed indicators. Discussing the method’s technical limitations, we believe that our findings contribute to a better understanding of land-cover change impact on visual landscape structure dynamics and further VLC indicator development. Full article

(This article belongs to the Special Issue Object Based Image Analysis for Remote Sensing)

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26 pages, 7755 KiB

Open AccessArticle

Uncertainty Analysis for Object-Based Change Detection in Very High-Resolution Satellite Images Using Deep Learning Network

by Ahram Song, Yongil Kim and Youkyung Han

Remote Sens. 2020, 12(15), 2345; https://doi.org/10.3390/rs12152345 - 22 Jul 2020

Cited by 20 | Viewed by 3658

Abstract

Object-based image analysis (OBIA) is better than pixel-based image analysis for change detection (CD) in very high-resolution (VHR) remote sensing images. Although the effectiveness of deep learning approaches has recently been proved, few studies have investigated OBIA and deep learning for CD. Previously proposed methods use the object information obtained from the preprocessing and postprocessing phase of deep learning. In general, they use the dominant or most frequently used label information with respect to all the pixels inside an object without considering any quantitative criteria to integrate the deep learning network and object information. In this study, we developed an object-based CD method for VHR satellite images using a deep learning network to denote the uncertainty associated with an object and effectively detect the changes in an area without the ground truth data. The proposed method defines the uncertainty associated with an object and mainly includes two phases. Initially, CD objects were generated by unsupervised CD methods, and the objects were used to train the CD network comprising three-dimensional convolutional layers and convolutional long short-term memory layers. The CD objects were updated according to the uncertainty level after the learning process was completed. Further, the updated CD objects were considered as the training data for the CD network. This process was repeated until the entire area was classified into two classes, i.e., change and no-change, with respect to the object units or defined epoch. The experiments conducted using two different VHR satellite images confirmed that the proposed method achieved the best performance when compared with the performances obtained using the traditional CD approaches. The method was less affected by salt and pepper noise and could effectively extract the region of change in object units without ground truth data. Furthermore, the proposed method can offer advantages associated with unsupervised CD methods and a CD network subjected to postprocessing by effectively utilizing the deep learning technique and object information. Full article

(This article belongs to the Special Issue Object Based Image Analysis for Remote Sensing)

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24 pages, 88603 KiB

Open AccessArticle

Object-Based Ensemble Learning for Pan-European Riverscape Units Mapping Based on Copernicus VHR and EU-DEM Data Fusion

by Luca Demarchi, Wouter van de Bund and Alberto Pistocchi

Remote Sens. 2020, 12(7), 1222; https://doi.org/10.3390/rs12071222 - 10 Apr 2020

Cited by 20 | Viewed by 4030

Abstract

Recent developments in the fields of geographical object-based image analysis (GEOBIA) and ensemble learning (EL) have led the way to the development of automated processing frameworks suitable to tackle large-scale problems. Mapping riverscape units has been recognized in fluvial remote sensing as an important concern for understanding the macrodynamics of a river system and, if applied at large scales, it can be a powerful tool for monitoring purposes. In this study, the potentiality of GEOBIA and EL algorithms were tested for the mapping of key riverscape units along the main European river network. The Copernicus VHR Image Mosaic and the EU Digital Elevation Model (EU-DEM)—both made available through the Copernicus Land Monitoring Service—were integrated within a hierarchical object-based architecture. In a first step, the most well-known EL techniques (bagging, boosting and voting) were tested for the automatic classification of water, sediment bars, riparian vegetation and other floodplain units. Random forest was found to be the best-to-use classifier, and therefore was used in a second phase to classify the entire object-based river network. Finally, an independent validation was performed taking into consideration the polygon area within the accuracy assessment, hence improving the efficiency of the classification accuracy of the GEOBIA-derived map, both globally and by geographical zone. As a result, we automatically processed almost 2 million square kilometers at a spatial resolution of 2.5 meters, producing a riverscape-units map with a global overall accuracy of 0.915, and with per-class F1 accuracies in the range 0.79–0.97. The obtained results may allow for future studies aimed at quantitative, objective and continuous monitoring of river evolutions and fluvial geomorphological processes at the scale of Europe. Full article

(This article belongs to the Special Issue Object Based Image Analysis for Remote Sensing)

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18 pages, 3256 KiB

Open AccessArticle

A Meta-Methodology for Improving Land Cover and Land Use Classification with SAR Imagery

by Marinalva Dias Soares, Luciano Vieira Dutra, Gilson Alexandre Ostwald Pedro da Costa, Raul Queiroz Feitosa, Rogério Galante Negri and Pedro M. A. Diaz

Remote Sens. 2020, 12(6), 961; https://doi.org/10.3390/rs12060961 - 16 Mar 2020

Cited by 6 | Viewed by 2761

Abstract

Per-point classification is a traditional method for remote sensing data classification, and for radar data in particular. Compared with optical data, the discriminative power of radar data is quite limited, for most applications. A way of trying to overcome these difficulties is to use Region-Based Classification (RBC), also referred to as Geographical Object-Based Image Analysis (GEOBIA). RBC methods first aggregate pixels into homogeneous objects, or regions, using a segmentation procedure. Moreover, segmentation is known to be an ill-conditioned problem because it admits multiple solutions, and a small change in the input image, or segmentation parameters, may lead to significant changes in the image partitioning. In this context, this paper proposes and evaluates novel approaches for SAR data classification, which rely on specialized segmentations, and on the combination of partial maps produced by classification ensembles. Such approaches comprise a meta-methodology, in the sense that they are independent from segmentation and classification algorithms, and optimization procedures. Results are shown that improve the classification accuracy from Kappa = 0.4 (baseline method) to a Kappa = 0.77 with the presented method. Another test site presented an improvement from Kappa = 0.36 to a maximum of 0.66 also with radar data. Full article

(This article belongs to the Special Issue Object Based Image Analysis for Remote Sensing)

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21 pages, 6835 KiB

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Optimized Segmentation Based on the Weighted Aggregation Method for Loess Bank Gully Mapping

by Hu Ding, Kai Liu, Xiaozheng Chen, Liyang Xiong, Guoan Tang, Fang Qiu and Josef Strobl

Remote Sens. 2020, 12(5), 793; https://doi.org/10.3390/rs12050793 - 02 Mar 2020

Cited by 25 | Viewed by 3560

Abstract

The Chinese Loess Plateau suffers severe gully erosion. Gully mapping is a fundamental task for gully erosion monitoring in this region. Among the different gully types in the Loess Plateau, the bank gully is usually regarded as the most important source for the generation of sediment. However, approaches for bank gully extraction are still limited. This study put forward an integrated framework, including segmentation optimization, evaluation and Extreme Gradient Boosting (XGBoost)-based classification, for the bank gully mapping of Zhifanggou catchment in the Chinese Loess Plateau. The approach was conducted using a 1-m resolution digital elevation model (DEM), based on unmanned aerial vehicle (UAV) photogrammetry and WorldView-3 imagery. The methodology first divided the study area into different watersheds. Then, segmentation by weighted aggregation (SWA) was implemented to generate multi-level segments. For achieving an optimum segmentation, area-weighted variance (WV) and Moran’s I (MI) were adopted and calculated within each sub-watershed. After that, a new discrepancy metric, the area-number index (ANI), was developed for evaluating the segmentation results, and the results were compared with the multi-resolution segmentation (MRS) algorithm. Finally, bank gully mappings were obtained based on the XGBoost model after fine-tuning. The experiment results demonstrate that the proposed method can achieve superior segmentation compared to MRS. Moreover, the overall accuracy of the bank gully extraction results was 78.57%. The proposed approach provides a credible tool for mapping bank gullies, which could be useful for the catchment-scale gully erosion process. Full article

(This article belongs to the Special Issue Object Based Image Analysis for Remote Sensing)

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22 pages, 9344 KiB

Open AccessArticle

Structure-Adaptive Clutter Suppression for Infrared Small Target Detection: Chain-Growth Filtering

by Suqi Huang, Yuhan Liu, Yanmin He, Tianfang Zhang and Zhenming Peng

Remote Sens. 2020, 12(1), 47; https://doi.org/10.3390/rs12010047 - 20 Dec 2019

Cited by 31 | Viewed by 3652

Abstract

Robust detection of infrared small target is an important and challenging task in many photoelectric detection systems. Using the difference of a specific feature between the target and the background, various detection methods were proposed in recent decades. However, most methods extract the feature in a region with fixed shape, especially in a rectangular region, which causes a problem: when faced with complex-shape clutters, the rectangular region involves the pixels inside and outside the clutters, and the significant grey-level difference among these pixels leads to a relatively large feature in the clutter area, interfering with the target detection. In this paper, we propose a structure-adaptive clutter suppression method, called chain-growth filtering, for robust infrared small target detection. The well-designed filtering model can adjust its shape to fit various clutter structures such as lines, curves and irregular edges, and thus has a more robust clutter suppression capability than the fixed-shape feature extraction strategy. In addition, the proposed method achieves a considerable anti-noise ability by employing guided filter as a preprocessing approach and enjoys the capability of multi-scale target detection without complex parameter tuning. In the experiment, we evaluate the performance of the detection method through 12 typical infrared scenes which contain different types of clutters. Compared with seven state-of-the-art methods, the proposed method shows the superior clutter-suppression effects for various types of clutters and the excellent detection performance for various scenes. Full article

(This article belongs to the Special Issue Object Based Image Analysis for Remote Sensing)

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29 pages, 15203 KiB

Open AccessArticle

Canopy Height Estimation from Single Multispectral 2D Airborne Imagery Using Texture Analysis and Machine Learning in Structurally Rich Temperate Forests

by Christos Boutsoukis, Ioannis Manakos, Marco Heurich and Anastasios Delopoulos

Remote Sens. 2019, 11(23), 2853; https://doi.org/10.3390/rs11232853 - 01 Dec 2019

Cited by 5 | Viewed by 4567

Abstract

Canopy height is a fundamental biophysical and structural parameter, crucial for biodiversity monitoring, forest inventory and management, and a number of ecological and environmental studies and applications. It is a determinant for linking the classification of land cover to habitat categories towards building one-to-one relationships. Light detection and ranging (LiDAR) or 3D Stereoscopy are the commonly used and most accurate remote sensing approaches to measure canopy height. However, both require significant time and budget resources. This study proposes a cost-effective methodology for canopy height approximation using texture analysis on a single 2D image. An object-oriented approach is followed using land cover (LC) map as segmentation vector layer to delineate landscape objects. Global texture feature descriptors are calculated for each land cover object and used as variables in a number of classifiers, including single and ensemble trees, and support vector machines. The aim of the analysis is the discrimination among classes in a wide range of height values used for habitat mapping (from less than 5 cm to 40 m). For that task, different spatial resolutions are tested, representing a range from airborne to spaceborne quality ones, as well as their combinations, forming a multiresolution training set. Multiple dataset alternatives are formed based on the missing data handling, outlier removal, and data normalization techniques. The approach was applied using orthomosaics from DMC II airborne images, and evaluated against a reference LiDAR-derived canopy height model (CHM). Results reached overall object-based accuracies of 67% with the percentage of total area correctly classified exceeding 88%. Sentinel-2 simulation and multiresolution analysis (MRA) experiments achieved even higher accuracies of up to 85% and 91%, respectively, at reduced computational cost, showing potential in terms of transferability of the framework to large spatial scales. Full article

(This article belongs to the Special Issue Object Based Image Analysis for Remote Sensing)

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21 pages, 13187 KiB

Open AccessArticle

Identifying Vegetation in Arid Regions Using Object-Based Image Analysis with RGB-Only Aerial Imagery

by Micha Silver, Arti Tiwari and Arnon Karnieli

Remote Sens. 2019, 11(19), 2308; https://doi.org/10.3390/rs11192308 - 03 Oct 2019

Cited by 16 | Viewed by 5968

Abstract

Vegetation state is usually assessed by calculating vegetation indices (VIs) derived from remote sensing systems where the near infrared (NIR) band is used to enhance the vegetation signal. However VIs are pixel-based and require both visible and NIR bands. Yet, most archived photographs were obtained with cameras that record only the three visible bands. Attempts to construct VIs with the visible bands alone have shown only limited success, especially in drylands. The current study identifies vegetation patches in the hyperarid Israeli desert using only the visible bands from aerial photographs by adapting an alternative geospatial object-based image analysis (GEOBIA) routine, together with recent improvements in preprocessing. The preprocessing step selects a balanced threshold value for image segmentation using unsupervised parameter optimization. Then the images undergo two processes: segmentation and classification. After tallying modeled vegetation patches that overlap true tree locations, both true positive and false positive rates are obtained from the classification and receiver operating characteristic (ROC) curves are plotted. The results show successful identification of vegetation patches in multiple zones from each study area, with area under the ROC curve values between 0.72 and 0.83. Full article

(This article belongs to the Special Issue Object Based Image Analysis for Remote Sensing)

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25 pages, 96820 KiB

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Object-Based Flood Analysis Using a Graph-Based Representation

by Bos Debusscher and Frieke Van Coillie

Remote Sens. 2019, 11(16), 1883; https://doi.org/10.3390/rs11161883 - 12 Aug 2019

Cited by 11 | Viewed by 3959

Abstract

The amount of freely available satellite data is growing rapidly as a result of Earth observation programmes, such as Copernicus, an initiative of the European Space Agency. Analysing these huge amounts of geospatial data and extracting useful information is an ongoing pursuit. This paper presents an alternative method for flood detection based on the description of spatio-temporal dynamics in satellite image time series (SITS). Since synthetic aperture radar (SAR) satellite data has the capability of capturing images day and night, irrespective of weather conditions, it is the preferred tool for flood mapping from space. An object-based approach can limit the necessary computer power and computation time, while a graph-based approach allows for a comprehensible interpretation of dynamics. This method proves to be a useful tool to gain insight in a flood event. Graph representation helps to identify and locate entities within the study site and describe their evolution throughout the time series. Full article

(This article belongs to the Special Issue Object Based Image Analysis for Remote Sensing)

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16 pages, 4676 KiB

Open AccessArticle

Object-Based Window Strategy in Thermal Sharpening

by Haiping Xia, Yunhao Chen, Jinling Quan and Jing Li

Remote Sens. 2019, 11(6), 634; https://doi.org/10.3390/rs11060634 - 15 Mar 2019

Cited by 12 | Viewed by 2716

Abstract

The trade-off between spatial and temporal resolutions has led to the disaggregation of remotely sensed land surface temperatures (LSTs) for better applications. The window used for regression is one of the primary factors affecting the disaggregation accuracy. Global window strategies (GWSs) and local window strategies (LWSs) have been widely used and discussed, while object-based window strategies (OWSs) have rarely been considered. Therefore, this study presents an OWS based on a segmentation algorithm and provides a basis for selecting an optimal window size balancing both accuracy and efficiency. The OWS is tested with Landsat 8 data and simulated data via the “aggregation-then-disaggregation” strategy, and compared with the GWS and LWS. Results tested with the Landsat 8 data indicate that the proposed OWS can accurately and efficiently generate high-resolution LSTs. In comparison to the GWS, the OWS improves the mean accuracy by 0.19 K at different downscaling ratios, in particular by 0.30 K over urban areas; compared with the LWS, the OWS performs better in most cases but performs slightly worse due to the increasing downscaling ratio in some cases. Results tested with the simulated data indicate that the OWS is always superior to both GWS and LWS regardless of the downscaling ratios, and the OWS improves the mean accuracy by 0.44 K and 0.19 K in comparison to the GWS and LWS, respectively. These findings suggest the potential ability of the OWS to generate super-high-resolution LSTs over heterogeneous regions when the pixels within the object-based windows derived via segmentation algorithms are more homogenous. Full article

(This article belongs to the Special Issue Object Based Image Analysis for Remote Sensing)

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17 pages, 3408 KiB

Open AccessArticle

Fully Convolutional Networks and Geographic Object-Based Image Analysis for the Classification of VHR Imagery

by Nicholus Mboga, Stefanos Georganos, Tais Grippa, Moritz Lennert, Sabine Vanhuysse and Eléonore Wolff

Remote Sens. 2019, 11(5), 597; https://doi.org/10.3390/rs11050597 - 12 Mar 2019

Cited by 52 | Viewed by 5697

Abstract

Land cover Classified maps obtained from deep learning methods such as Convolutional neural networks (CNNs) and fully convolutional networks (FCNs) usually have high classification accuracy but with the detailed structures of objects lost or smoothed. In this work, we develop a methodology based on fully convolutional networks (FCN) that is trained in an end-to-end fashion using aerial RGB images only as input. Skip connections are introduced into the FCN architecture to recover high spatial details from the lower convolutional layers. The experiments are conducted on the city of Goma in the Democratic Republic of Congo. We compare the results to a state-of-the art approach based on a semi-automatic Geographic object image-based analysis (GEOBIA) processing chain. State-of-the art classification accuracies are obtained by both methods whereby FCN and the best baseline method have an overall accuracy of 91.3% and 89.5% respectively. The maps have good visual quality and the use of an FCN skip architecture minimizes the rounded edges that is characteristic of FCN maps. Additional experiments are done to refine FCN classified maps using segments obtained from GEOBIA generated at different scale and minimum segment size. High OA of up to 91.5% is achieved accompanied with an improved edge delineation in the FCN maps, and future work will involve explicitly incorporating boundary information from the GEOBIA segmentation into the FCN pipeline in an end-to-end fashion. Finally, we observe that FCN has a lower computational cost than the standard patch-based CNN approach especially at inference. Full article

(This article belongs to the Special Issue Object Based Image Analysis for Remote Sensing)

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