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Superpixel based Analysis and Classification of Remote Sensing Images

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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 January 2019) | Viewed by 36809

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Dr. Jinchang Ren

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National Subsea Centre, Robert Gordon University, Aberdeen, UK
Interests: hyperspectral imaging; remote sensing; computer vision; machine learning; big data analytics
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Prof. Dr. Qihao Weng

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Center for Urban and Environmental Change, Department of Earth and Environmental Systems, Indiana State University, Terre Haute, IN 47809, USA
Interests: remote sensing; imaging science; GIS; land use and land cover change; urban environment and ecosystem
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Dr. Xiuping Jia

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School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, Australia
Interests: photogrammetry and remote sensing; image processing
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Prof. Dr. Genyun Sun

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College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China
Interests: urban and marine environment remote sensing; hyperspectral remote sensing; multisource remote sensing applications
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Prof. Dr. Jon Atli Benediktsson

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Faculty of Electrical and Computer Engineering, University of Iceland, IS 102 Reykjavík, Iceland
Interests: remote sensing; image analysis; pattern recognition; signal processing
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Special Issue Information

Dear Colleagues,

In recent years, superpixels have attracted increasing attention in image analysis, especially for remote-sensing images. As a fundamental element of digital images, pixels represent little semantic entities. By expanding pixels to superpixels, similar pixels are grouped together to consequently form perceptually meaningful entities for image interpretation.

Consistent with the mechanisms of human visual systems, superpixel-based image analysis has become mainstream in remote-sensing image analysis. As superpixels can adhere to the natural image boundaries than square patches, they can achieve a better perceptual representation of images than pixels. As a result, they have been successfully applied in image segmentation, image classification, object detection and image annotation/retrieval for analyzing remote-sensing images.

However, there are still some unsolved problems with superpixels, such as the generation of superpixels and the determination of their optimal sizes, as well as how they can be combined with other image analysis techniques in further extended applications, i.e., superpixels-based graph representation for image analysis.

The aim of this Special Issue is to gather cutting-edge work currently being developed using superpixels for analysis and classification of remote-sensing images. Both original contributions with theoretical novelty and practical solutions for addressing particular problems in remote sensing are solicited. The main topics include, but not limited to:

Algorithms for superpixel generation
Optimal design of superpixels
Superpixel based feature extraction
Superpixel based image segmentation
Superpixel based object detection
Superpixel based image classification
Superpixel based graph algorithms
Superpixels for multispectral/hyperspectral/SAR and other images
Superpixel-based applications in remote sensing, such as mapping of urban heat and impervious surface, disaster assessment, surface mapping of land and ocean, etc.

Dr. Jinchang Ren
Prof. Qihao Weng
Dr. Xiuping Jia
Dr Genyun Sun
Prof. Jon Atli Benediktsson
Guest Editors

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Keywords

remote sensing
superpixels
image analysis
image classification
image segmentation

Published Papers (8 papers)

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22 pages, 17183 KiB

Open AccessArticle

Mapping of Coastal Cities Using Optimized Spectral–Spatial Features Based Multi-Scale Superpixel Classification

by Aizhu Zhang, Shuang Zhang, Genyun Sun, Feng Li, Hang Fu, Yunhua Zhao, Hui Huang, Ji Cheng and Zhenjie Wang

Remote Sens. 2019, 11(9), 998; https://doi.org/10.3390/rs11090998 - 26 Apr 2019

Cited by 4 | Viewed by 3514

Abstract

The high interior heterogeneity of land surface covers in high-resolution image of coastal cities makes classification challenging. To meet this challenge, a Multi-Scale Superpixels-based Classification method using Optimized Spectral–Spatial features, denoted as OSS-MSSC, is proposed in this paper. In the proposed method, the multi-scale superpixels are firstly generated to capture the local spatial structures of the ground objects with various sizes. Then, the normalized difference vegetation index and extend multi-attribute profiles are introduced to extract the spectral–spatial features from the multi-spectral bands of the image. To reduce the redundancy of the spectral–spatial features, the crossover-based search algorithm is utilized for feature optimization. The pre-classification results at each single scale are, therefore, obtained based on the optimized spectral–spatial features and random forest classifier. Finally, the ultimate classification is generated via the majority voting of those pre-classification results in each scale. Experimental results on the Gaofen-2 image of Qingdao and WorldView-2 image of Hong Kong, China confirmed the effectiveness of the proposed method. The experiments verify that the OSS-MSSC method not only works effectively on the homogeneous regions, but also is able to preserve the small local spatial structures in the high-resolution remote sensing images of coastal cities. Full article

(This article belongs to the Special Issue Superpixel based Analysis and Classification of Remote Sensing Images)

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17 pages, 1245 KiB

Open AccessArticle

Superpixel based Feature Specific Sparse Representation for Spectral-Spatial Classification of Hyperspectral Images

by He Sun, Jinchang Ren, Huimin Zhao, Yijun Yan, Jaime Zabalza and Stephen Marshall

Remote Sens. 2019, 11(5), 536; https://doi.org/10.3390/rs11050536 - 05 Mar 2019

Cited by 36 | Viewed by 3673

Abstract

To improve the performance of the sparse representation classification (SRC), we propose a superpixel-based feature specific sparse representation framework (SPFS-SRC) for spectral-spatial classification of hyperspectral images (HSI) at superpixel level. First, the HSI is divided into different spatial regions, each region is shape- and size-adapted and considered as a superpixel. For each superpixel, it contains a number of pixels with similar spectral characteristic. Since the utilization of multiple features in HSI classification has been proved to be an effective strategy, we have generated both spatial and spectral features for each superpixel. By assuming that all the pixels in a superpixel belongs to one certain class, a kernel SRC is introduced to the classification of HSI. In the SRC framework, we have employed a metric learning strategy to exploit the commonalities of different features. Experimental results on two popular HSI datasets have demonstrated the efficacy of our proposed methodology. Full article

(This article belongs to the Special Issue Superpixel based Analysis and Classification of Remote Sensing Images)

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20 pages, 9962 KiB

Open AccessArticle

Weakly Supervised Segmentation of SAR Imagery Using Superpixel and Hierarchically Adversarial CRF

by Fei Ma, Fei Gao, Jinping Sun, Huiyu Zhou and Amir Hussain

Remote Sens. 2019, 11(5), 512; https://doi.org/10.3390/rs11050512 - 02 Mar 2019

Cited by 36 | Viewed by 5590

Abstract

Synthetic aperture radar (SAR) image segmentation aims at generating homogeneous regions from a pixel-based image and is the basis of image interpretation. However, most of the existing segmentation methods usually neglect the appearance and spatial consistency during feature extraction and also require a large number of training data. In addition, pixel-based processing cannot meet the real time requirement. We hereby present a weakly supervised algorithm to perform the task of segmentation for high-resolution SAR images. For effective segmentation, the input image is first over-segmented into a set of primitive superpixels. This algorithm combines hierarchical conditional generative adversarial nets (CGAN) and conditional random fields (CRF). The CGAN-based networks can leverage abundant unlabeled data learning parameters, reducing their reliance on the labeled samples. In order to preserve neighborhood consistency in the feature extraction stage, the hierarchical CGAN is composed of two sub-networks, which are employed to extract the information of the central superpixels and the corresponding background superpixels, respectively. Afterwards, CRF is utilized to perform label optimization using the concatenated features. Quantified experiments on an airborne SAR image dataset prove that the proposed method can effectively learn feature representations and achieve competitive accuracy to the state-of-the-art segmentation approaches. More specifically, our algorithm has a higher Cohen’s kappa coefficient and overall accuracy. Its computation time is less than the current mainstream pixel-level semantic segmentation networks. Full article

(This article belongs to the Special Issue Superpixel based Analysis and Classification of Remote Sensing Images)

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20 pages, 13047 KiB

Open AccessArticle

Superpixel-Based Segmentation of Polarimetric SAR Images through Two-Stage Merging

by Wei Wang, Deliang Xiang, Yifang Ban, Jun Zhang and Jianwei Wan

Remote Sens. 2019, 11(4), 402; https://doi.org/10.3390/rs11040402 - 16 Feb 2019

Cited by 8 | Viewed by 3635

Abstract

Image segmentation plays a fundamental role in image understanding and region-based applications. This paper presents a superpixel-based segmentation method for Polarimetric SAR (PolSAR) data, in which a two-stage merging strategy is proposed. First, based on the initial superpixel partition, the Wishart-merging stage (WMS) simultaneously merges the regions in homogeneous areas. The edge penalty is combined with the Wishart energy loss to ensure that the superpixels to be merged are from the same land cover. The second stage follows the iterative merging procedure, and applies the doubly flexible KummerU distribution to better characterize the resultant regions from WMS, which are usually located in heterogeneous areas. Moreover, the edge penalty and the proposed homogeneity penalty are adopted in the KummerU-merging stage (KUMS) to further improve the segmentation accuracy. The two-stage merging strategy applies the general statistical model for the superpixels without ambiguity, and more advanced model for the regions with ambiguity. Therefore, the implementing efficiency can be improved based on the WMS, and the accuracy can be increased through the KUMS. Experimental results on two real PolSAR datasets show that the proposed method can effectively improve the computation efficiency and segmentation accuracy compared with the classical merging-based methods. Full article

(This article belongs to the Special Issue Superpixel based Analysis and Classification of Remote Sensing Images)

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18 pages, 2844 KiB

Open AccessArticle

Modified Superpixel Segmentation for Digital Surface Model Refinement and Building Extraction from Satellite Stereo Imagery

by Zeinab Gharibbafghi, Jiaojiao Tian and Peter Reinartz

Remote Sens. 2018, 10(11), 1824; https://doi.org/10.3390/rs10111824 - 17 Nov 2018

Cited by 15 | Viewed by 5918

Abstract

Superpixels, as a state-of-the-art segmentation paradigm, have recently been widely used in computer vision and pattern recognition. Despite the effectiveness of these algorithms, there are still many limitations and challenges dealing with Very High-Resolution (VHR) satellite images especially in complex urban scenes. In this paper, we develop a superpixel algorithm as a modified edge-based version of Simple Linear Iterative Clustering (SLIC), which is here called ESLIC, compatible with VHR satellite images. Then, based on the modified properties of generated superpixels, a heuristic multi-scale approach for building extraction is proposed, based on the stereo satellite imagery along with the corresponding Digital Surface Model (DSM). First, to generate the modified superpixels, an edge-preserving term is applied to retain the main building boundaries and edges. The resulting superpixels are then used to initially refine the stereo-extracted DSM. After shadow and vegetation removal, a rough building mask is obtained from the normalized DSM, which highlights the appropriate regions in the image, to be used as the input of a multi-scale superpixel segmentation of the proper areas to determine the superpixels inside the building. Finally, these building superpixels with different scales are integrated and the output is a unified building mask. We have tested our methods on building samples from a WorldView-2 dataset. The results are promising, and the experiments show that superpixels generated with the proposed ESLIC algorithm are more adherent to the building boundaries, and the resulting building mask retains urban object shape better than those generated with the original SLIC algorithm. Full article

(This article belongs to the Special Issue Superpixel based Analysis and Classification of Remote Sensing Images)

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17 pages, 7101 KiB

Open AccessArticle

Hyperspectral Classification via Superpixel Kernel Learning-Based Low Rank Representation

by Tianming Zhan, Le Sun, Yang Xu, Guowei Yang, Yan Zhang and Zebin Wu

Remote Sens. 2018, 10(10), 1639; https://doi.org/10.3390/rs10101639 - 16 Oct 2018

Cited by 22 | Viewed by 3148

Abstract

High dimensional image classification is a fundamental technique for information retrieval from hyperspectral remote sensing data. However, data quality is readily affected by the atmosphere and noise in the imaging process, which makes it difficult to achieve good classification performance. In this paper, multiple kernel learning-based low rank representation at superpixel level (Sp_MKL_LRR) is proposed to improve the classification accuracy for hyperspectral images. Superpixels are generated first from the hyperspectral image to reduce noise effect and form homogeneous regions. An optimal superpixel kernel parameter is then selected by the kernel matrix using a multiple kernel learning framework. Finally, a kernel low rank representation is applied to classify the hyperspectral image. The proposed method offers two advantages. (1) The global correlation constraint is exploited by the low rank representation, while the local neighborhood information is extracted as the superpixel kernel adaptively learns the high-dimensional manifold features of the samples in each class; (2) It can meet the challenges of multiscale feature learning and adaptive parameter determination in the conventional kernel methods. Experimental results on several hyperspectral image datasets demonstrate that the proposed method outperforms several state-of-the-art classifiers tested in terms of overall accuracy, average accuracy, and kappa statistic. Full article

(This article belongs to the Special Issue Superpixel based Analysis and Classification of Remote Sensing Images)

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30 pages, 15286 KiB

Open AccessArticle

Multiscale and Multifeature Segmentation of High-Spatial Resolution Remote Sensing Images Using Superpixels with Mutual Optimal Strategy

by Zhongliang Fu, Yangjie Sun, Liang Fan and Yutao Han

Remote Sens. 2018, 10(8), 1289; https://doi.org/10.3390/rs10081289 - 15 Aug 2018

Cited by 28 | Viewed by 5463

Abstract

High spatial resolution (HSR) image segmentation is considered to be a major challenge for object-oriented remote sensing applications that have been extensively studied in the past. In this paper, we propose a fast and efficient framework for multiscale and multifeatured hierarchical image segmentation (MMHS). First, the HSR image pixels were clustered into a small number of superpixels using a simple linear iterative clustering algorithm (SLIC) on modern graphic processing units (GPUs), and then a region adjacency graph (RAG) and nearest neighbors graph (NNG) were constructed based on adjacent superpixels. At the same time, the RAG and NNG successfully integrated spectral information, texture information, and structural information from a small number of superpixels to enhance its expressiveness. Finally, a multiscale hierarchical grouping algorithm was implemented to merge these superpixels using local-mutual best region merging (LMM). We compared the experiments with three state-of-the-art segmentation algorithms, i.e., the watershed transform segmentation (WTS) method, the mean shift (MS) method, the multiresolution segmentation (MRS) method integrated in commercial software, eCognition9, on New York HSR image datasets, and the ISPRS Potsdam dataset. Computationally, our algorithm was dozens of times faster than the others, and it also had the best segmentation effect through visual assessment. The supervised and unsupervised evaluation results further proved the superiority of the MMHS algorithm. Full article

(This article belongs to the Special Issue Superpixel based Analysis and Classification of Remote Sensing Images)

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18 pages, 4336 KiB

Open AccessTechnical Note

Monitoring Forest Loss in ALOS/PALSAR Time-Series with Superpixels

by Charlie Marshak, Marc Simard and Michael Denbina

Remote Sens. 2019, 11(5), 556; https://doi.org/10.3390/rs11050556 - 07 Mar 2019

Cited by 11 | Viewed by 4115

Abstract

We present a flexible methodology to identify forest loss in synthetic aperture radar (SAR) L-band ALOS/PALSAR images. Instead of single pixel analysis, we generate spatial segments (i.e., superpixels) based on local image statistics to track homogeneous patches of forest across a time-series of ALOS/PALSAR images. Forest loss detection is performed using an ensemble of Support Vector Machines (SVMs) trained on local radar backscatter features derived from superpixels. This method is applied to time-series of ALOS-1 and ALOS-2 radar images over a boreal forest within the Laurentides Wildlife Reserve in Québec, Canada. We evaluate four spatial arrangements including (1) single pixels, (2) square grid cells, (3) superpixels based on segmentation of the radar images, and (4) superpixels derived from ancillary optical Landsat imagery. Detection of forest loss using superpixels outperforms single pixel and regular square grid cell approaches, especially when superpixels are generated from ancillary optical imagery. Results are validated with official Québec forestry data and Hansen et al. forest loss products. Our results indicate that this approach can be applied to monitor forest loss across large study areas using L-band radar instruments such as ALOS/PALSAR, particularly when combined with superpixels generated from ancillary optical data. Full article

(This article belongs to the Special Issue Superpixel based Analysis and Classification of Remote Sensing Images)

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