Superpixel Generating Algorithm Based on Pixel Intensity and Location Similarity for SAR Image Classification

Xiang, Deliang; Tang, Tao; Zhao, Lingjun; Su, Yi

doi:10.1109/lgrs.2013.2259214

Cited by 88 publications

(43 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It offers flexibility in regard to compactness and the number of superpixels it generates. Due to these advantages, Xiang et al (2013) and Feng, Cao, and Pi (2014) introduced it to obtain superpixels for SAR and PolSAR data, respectively.…”

Section: Superpixel Generation For Polsar Datamentioning

confidence: 99%

Unsupervised classification of polarimetric SAR imagery using large-scale spectral clustering with spatial constraints

Song

Yang

Bai

et al. 2015

International Journal of Remote Sensing

View full text Add to dashboard Cite

Spectral clustering is a very popular approach which has been successfully used in unsupervised classification of polarimetric synthetic aperture radar (PolSAR) imagery. However, due to its high computational complexity, spectral clustering can only be applied to small data sets. This article provides a framework for spectral clustering of large-scale PolSAR data. As computing and processing the pairwise-based affinity matrix is the bottleneck of the spectral clustering approach, we first introduce a representative points-based scheme in which a memory-saving and computationally tractable affinity matrix is designed. The subsequent spectral analysis can be solved efficiently. Second, a simple one-parameter superpixel algorithm is introduced to generate representative points. Through these superpixels, spatial constraints are also naturally integrated into the classification framework. We test the proposed approach on both airborne and space-borne PolSAR images. Experimental results demonstrate its effectiveness.

show abstract

Section: Superpixel Generation For Polsar Datamentioning

confidence: 99%

Unsupervised classification of polarimetric SAR imagery using large-scale spectral clustering with spatial constraints

Song

Yang

Bai

et al. 2015

International Journal of Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Therefore, object generation plays a key role in this kind of processing, where the images are segmented into many homogeneous regions. A superpixel is defined as a local region which preserves most of the object information and well adheres to the object boundaries (Xiang et al 2013). To better preserve the polarimetric and statistical characteristics of the images and also overcome the influence of speckle noise in the meantime, superpixel generation and segmentation with regular size and shape seem promising for PolSAR data.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, there are very few superpixel generation and segmentation approaches proposed for SAR and PolSAR images. Xiang et al (Xiang et al 2013) developed a novel superpixel generation algorithm based on pixel intensity and location similarity, which modified the similarity measure of SLIC to make it applicative for SAR images. For PolSAR data, Liu et al (Liu et al 2013) incorporated the revised Wishart distance and edge map into the Normalized cuts algorithm to produce superpixels.…”

Section: Introductionmentioning

confidence: 99%

Superpixel Segmentation for Polsar Images With Local Iterative Clustering and Heterogeneous Statistical Model

Xiang

Zhang

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Superpixel segmentation has an advantage that can well preserve the target shape and details. In this research, an adaptive polarimetric SLIC (Pol-ASLIC) superpixel segmentation method is proposed. First, the spherically invariant random vector (SIRV) product model is adopted to estimate the normalized covariance matrix and texture for each pixel. A new edge detector is then utilized to extract PolSAR image edges for the initialization of central seeds. In the local iterative clustering, multiple cues including polarimetric, texture, and spatial information are considered to define the similarity measure. Moreover, a polarimetric homogeneity measurement is used to automatically determine the tradeoff factor, which can vary from homogeneous areas to heterogeneous areas. Finally, the SLIC superpixel segmentation scheme is applied to the airborne Experimental SAR and PiSAR L-band PolSAR data to demonstrate the effectiveness of this proposed segmentation approach. This proposed algorithm produces compact superpixels which can well adhere to image boundaries in both natural and urban areas. The detail information in heterogeneous areas can be well preserved.

show abstract

“…Recent developments [5][6][7][8][9] have allowed application of SPS segmentation to SAR imagery, as well, even though SAR images contain speckle, a result of coherent combining and cancelling of the multi-path backscattered radar energy. In the formed SAR image, speckle is a multiplicative phenomenon.…”

Section: Introductionmentioning

confidence: 99%

“…Xiang et.al. [9] incorporate a Gaussian-smoothed intensity ratio distance into a local k-means algorithm to extract superpixels.…”

Section: Introductionmentioning

confidence: 99%

Superpixel segmentation using multiple SAR image products

et al. 2014

View full text Add to dashboard Cite

Sandia National Laboratories produces copious amounts of high-resolution, single-polarization Synthetic Aperture Radar (SAR) imagery, much more than available researchers and analysts can examine. Automating the recognition of terrains and structures in SAR imagery is highly desired. The optical image processing community has shown that superpixel segmentation (SPS) algorithms divide an image into small compact regions of similar intensity. Applying these SPS algorithms to optical images can reduce image complexity, enhance statistical characterization and improve segmentation and categorization of scene objects. SPS algorithms typically require high SNR (signal-to-noise-ratio) images to define segment boundaries accurately. Unfortunately, SAR imagery contains speckle, a product of coherent image formation, which complicates the extraction of superpixel segments and could preclude their use.Some researchers have developed modified SPS algorithms that discount speckle for application to SAR imagery. We apply two widely-used SPS algorithms to speckle-reduced SAR image products, both single SAR products and combinations of multiple SAR products, which include both single polarization and multi-polarization SAR images. To evaluate the quality of resulting superpixels, we compute research-standard segmentation quality measures on the match between superpixels and hand-labeled ground-truth, as well as statistical characterization of the radar-cross-section within each superpixel. Results of this quality analysis determine the best input/algorithm/parameter set for SAR imagery. Simple Linear Iterative Clustering provides faster computation time, superpixels that conform to scene-relevant structures, direct control of average superpixel size and more uniform superpixel sizes for improved statistical estimation which will facilitate subsequent terrain/structure categorization and segmentation into scene-relevant regions.

show abstract

Superpixel Generating Algorithm Based on Pixel Intensity and Location Similarity for SAR Image Classification

Cited by 88 publications

References 16 publications

Unsupervised classification of polarimetric SAR imagery using large-scale spectral clustering with spatial constraints

Unsupervised classification of polarimetric SAR imagery using large-scale spectral clustering with spatial constraints

Superpixel Segmentation for Polsar Images With Local Iterative Clustering and Heterogeneous Statistical Model

Superpixel segmentation using multiple SAR image products

Contact Info

Product

Resources

About