Integration of Segmentation Techniques for Classification of Hyperspectral Images

Ghamisi, Pedram; Couceiro, Micael S.; Fauvel, Mathieu; Benediktsson, Jón Atli

doi:10.1109/lgrs.2013.2257675

Cited by 61 publications

(19 citation statements)

References 12 publications

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“…[4] this paper a new spectral-spatial algorithm for classification of hyperspectral images is defined. The proposed approach is based on two segmentation methods, fractional-order Darwinian particle swarm optimization and mean shift segmentation.…”

Section: International Journal For Research In Applied Science and Engimentioning

confidence: 99%

A Brief Review of Image Segmentation Based on Thresholding Technique

Dilip¹

2017

IJRASET

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Abstract:Image processing plays an important role in computer vision. The process of image segmentation provides the partition of image into different segments per their feature attribute. Image segmentation divided into two types based on similarity and based on discontinuity. Region based segmentation is a type similarity based segmentation. Another type of segmentation is called thresholding based segmentation. In thresholding based segmentation method some thresholding techniques are used. Moreover, technologies required for one field of image may not suitable for other types of image. Many image applications can be found on binary or gray image but almost none for color image segmentation. In this paper, we present the study of various image segmentation techniques.

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Section: International Journal For Research In Applied Science and Engimentioning

confidence: 99%

A Brief Review of Image Segmentation Based on Thresholding Technique

Dilip¹

2017

IJRASET

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“…It consists of two prevalent techniques, namely high-resolution image segmentation and active learning. The segmentation processing is applied on the high-resolution PAN image, which can be realized through any existing algorithms that consider spatial-spectral information [25][26][27][28]. Then for a given labeled sample, the pixels that locate in the same object can be labeled with high confidence as belonging to the same class (here we call it the object label) as this labeled sample [21].…”

Section: Related Workmentioning

confidence: 99%

Incorporating Diversity into Self-Learning for Synergetic Classification of Hyperspectral and Panchromatic Images

Lu¹,

Zhang²,

Li³

et al. 2016

Remote Sensing

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Abstract:Derived from semi-supervised learning and active learning approaches, self-learning (SL) was recently developed for the synergetic classification of hyperspectral (HS) and panchromatic (PAN) images. Combining the image segmentation and active learning techniques, SL aims at selecting and labeling the informative unlabeled samples automatically, thereby improving the classification accuracy under the condition of small samples. This paper presents an improved synergetic classification scheme based on the concept of self-learning for HS and PAN images. The investigated scheme considers three basic rules, namely the identity rule, the uncertainty rule, and the diversity rule. By integrating the diversity of samples into the SL scheme, a more stable classifier is trained by using fewer samples. Experiments on three synthetic and real HS and PAN images reveal that the diversity criterion can avoid the problem of bias sampling, and has a certain advantage over the primary self-learning approach.

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“…For instance, we can obtain more accurate classification maps by a pixel-wise classifier using spatial contextual information such as grey level co-occurrence matrix (GLCM) [5,6], extended morphological profiles (EMP) [7], pixel shape index [8], extended morphological attribute profile (EAP) [9], texture information based on Gabor filter [10,11], wavelet texture feature [12][13][14], etc. Another way for performing spectral-spatial classification was achieved by different segmentation techniques of watershed [15], mean shift [16,17], hierarchical segmentation [18,19], superpixel segmentation [20], extraction and classification of homogeneous objects [21], minimum spanning forest [22], fractal net evolution approach-based segmentation [6], etc. Apart from those efforts, some advanced spectral-spatial classification methods have been presented, by using multiple kernels learning [23] and generalized composite kernels [24], to integrate spatial features with spectral signatures.…”

Section: Introductionmentioning

confidence: 99%

Spectral-Spatial Classification of Hyperspectral Images Using Joint Bilateral Filter and Graph Cut Based Model

2016

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Abstract:Hyperspectral image classification can be achieved by modeling an energy minimization problem on a graph of image pixels. In this paper, an effective spectral-spatial classification method for hyperspectral images based on joint bilateral filtering (JBF) and graph cut segmentation is proposed. In this method, a novel technique for labeling regions obtained by the spectral-spatial segmentation process is presented. Our method includes the following steps. First, the probabilistic support vector machines (SVM) classifier is used to estimate probabilities belonging to each information class. Second, an extended JBF is employed to perform image smoothing on the probability maps. By using our JBF process, salt-and-pepper classification noise in homogeneous regions can be effectively smoothed out while object boundaries in the original image are better preserved as well. Third, a sequence of modified bi-labeling graph cut models is constructed for each information class to extract the desirable object belonging to the corresponding class from the smoothed probability maps. Finally, a classification map is achieved by merging the segmentation maps obtained in the last step using a simple and effective rule. Experimental results based on three benchmark airborne hyperspectral datasets with different resolutions and contexts demonstrate that our method can achieve 8.56%-13.68% higher overall accuracies than the pixel-wise SVM classifier. The performance of our method was further compared to several classical hyperspectral image classification methods using objective quantitative measures and a visual qualitative evaluation.

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Integration of Segmentation Techniques for Classification of Hyperspectral Images

Cited by 61 publications

References 12 publications

A Brief Review of Image Segmentation Based on Thresholding Technique

A Brief Review of Image Segmentation Based on Thresholding Technique

Incorporating Diversity into Self-Learning for Synergetic Classification of Hyperspectral and Panchromatic Images

Spectral-Spatial Classification of Hyperspectral Images Using Joint Bilateral Filter and Graph Cut Based Model

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