Results 131 to 140 of about 417 (152)

Multiscale Superpixel Kernel-Based Low-Rank Representation for Hyperspectral Image Classification

IEEE Geoscience and Remote Sensing Letters, 2020
Classification plays an important role in the field of hyperspectral image (HSI) remote sensing. In this letter, a novel multiscale superpixel kernel-based low-rank representation (MSKLRR) classifier is proposed for HSI classification. A multiscale superpixel segmentation method is first used to generate several homogeneous regions at different scales.
Tianming Zhan, Minghua Wan, Zhenyu Lu
exaly   +2 more sources

Multiscale superpixel classification for tumor segmentation in breast ultrasound images

2012 19th IEEE International Conference on Image Processing, 2012
Tumor localization and segmentation in breast ultrasound (BUS) images is an important as well as intractable problem for computer-aided diagnosis (CAD) due to the high variation in shape and appearance. We propose a novel algorithm in this paper without making any assumption on tumor, compared to most previous works.
Zhihui Hao, Qiang Wang 0023, Haibing Ren, Kuanhong Xu, Yeong Kyeong Seong, Ji-yeun Kim   +5 more
openaire   +1 more source

HUMAN BODY SEGMENTATION IN A STATIC IMAGE WITH ON-LINE ADABOOST AT MULTISCALE SUPERPIXELS

International Journal of Image and Graphics, 2012
In this work, we propose a new method for accurate human body detection in a static image with multi-scale superpixels based on two models. First, based on the face detection, we use designed torso part model to estimate the torso part region to provide the positive samples of on-line AdaBoost.
Shifeng Li, Meng Yao, Huchuan Lu
openaire   +1 more source

Multiscale Superpixel Kernel Learning for Spatial-Spectral Hyperspectral Image Classification

2021 5th International Conference on Automation, Control and Robots (ICACR), 2021
In this paper, to further explore the spatial-spectral information, we propose a novel multiple kernel learning (MKL) algorithm embedded with multiscale superpixels, whose shapes and sizes are changed adaptly according to the local structural features. Specifically, we first introduce the superpixel to generate multiscale homogeneous regions.
Ling Wang, Hongqiao Wang, Guangyuan Fu
openaire   +1 more source

Discriminant sub-dictionary learning with adaptive multiscale superpixel representation for hyperspectral image classification

Neurocomputing, 2020
Abstract Sparse representation and dictionary learning have been successfully applied in hyperspectral image classification. Generally, it is more effective to learn the sub-dictionary for each class and utilize multiple scale strategy. However, the sub-dictionary may only consider the within-class information and ignore the discriminative ...
Quansen Sun, Xiaobo Shen, Zexuan Ji
exaly   +2 more sources

Multiscale superpixel segmentation-based band expansion for change detection

Remote Sensing Letters, 2023
Change detection (CD) for remotely sensed images has gained great relevance in the last decade due to an increase in the number of Earth Observation (EO) missions, improved temporal resolutions, and open data policies. However, efficient exploitation and integration of spatial information for CD remains a challenging issue.
openaire   +2 more sources

Multiscale superpixel-based fusion framework for hyperspectral image classification

2018 Fifth International Workshop on Earth Observation and Remote Sensing Applications (EORSA), 2018
Since it is usually difficult and time-consuming to obtain sufficient labeled samples in practice, the samll number of sample is one of the challenging issue for hyperspectral image classifiction. Fortunately, due to the spatial correlation of the surface of the materials, it is feasible to improve classification performance from the perspective of ...
Sen Jia, Xianglong Deng, Kuilin Wu
openaire   +1 more source

Multiscale Superpixel-Level Subspace-Based Support Vector Machines for Hyperspectral Image Classification

IEEE Geoscience and Remote Sensing Letters, 2017
This letter introduces a new spectral–spatial classification method for hyperspectral images. A multiscale superpixel segmentation is first used to model the distribution of classes based on spatial information. In this context, the original hyperspectral image is integrated with segmentation maps via a feature fusion process in different scales such ...
Bing Zhang, Wenzhi Liao, Haoyang Yu
exaly   +2 more sources

A Multiscale Superpixel-Guided Filter Approach for VHR Remote Sensing Image Classification

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019
This paper presents a novel multiscale superpixel-guided filter (MSGF) approach for very high resolution (VHR) remote sensing image classification. Different from the traditional guided filter (GF) classification method, the proposed method utilizes a guidance image that constructed from the superpixel segmentation image, which is capable to provide ...
Sicong Liu 0001, Qing Hu, Alim Samat, Xiaohua Tong   +3 more
openaire   +1 more source

Superpixel-guided multiscale kernel collaborative representation for hyperspectral image classification

Remote Sensing Letters, 2016
ABSTRACTThis article presents a superpixel-guided multiscale kernel collaborative representation method for robust classification of hyperspectral images. This novel method first exploits the spatial multiscale information of hyperspectral images by extending a superpixel segmentation algorithm, and then proposes a spatial-spectral information fusion ...
Jianjun Liu, Zhiyong Xiao, Liang Xiao
openaire   +1 more source