Secret spatial information hiding technique for remote sensing image

2007 ◽  
Author(s):  
Xianmin Wang ◽  
Cheng Wang ◽  
Jianzhong Zhou ◽  
Yongchuan Zhang
Keyword(s):  
Remote Sensing ◽  
Information Hiding ◽  
Spatial Information ◽  
Remote Sensing Image

scholarly journals High resolution remote sensing image segmentation based on graph theory and fractal net evolution approach

2015 ◽  
Vol XL-7/W4 ◽  
pp. 197-201 ◽  
Author(s):  
Y. Yang ◽  
H. T. Li ◽  
Y. S. Han ◽  
H. Y. Gu
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Graph Theory ◽  
High Resolution ◽  
Spatial Information ◽  
Remote Sensing Image ◽  
Sensing Applications ◽  
Initial Object ◽  
Object Layer ◽  
Evolution Approach

Image segmentation is the foundation of further object-oriented image analysis, understanding and recognition. It is one of the key technologies in high resolution remote sensing applications. In this paper, a new fast image segmentation algorithm for high resolution remote sensing imagery is proposed, which is based on graph theory and fractal net evolution approach (FNEA). Firstly, an image is modelled as a weighted undirected graph, where nodes correspond to pixels, and edges connect adjacent pixels. An initial object layer can be obtained efficiently from graph-based segmentation, which runs in time nearly linear in the number of image pixels. Then FNEA starts with the initial object layer and a pairwise merge of its neighbour object with the aim to minimize the resulting summed heterogeneity. Furthermore, according to the character of different features in high resolution remote sensing image, three different merging criterions for image objects based on spectral and spatial information are adopted. Finally, compared with the commercial remote sensing software eCognition, the experimental results demonstrate that the efficiency of the algorithm has significantly improved, and the result can maintain good feature boundaries.

scholarly journals Weighted Spatial Pyramid Matching Collaborative Representation for Remote-Sensing-Image Scene Classification

2019 ◽  
Vol 11 (5) ◽  
pp. 518 ◽  
Author(s):  
Bao-Di Liu ◽  
Jie Meng ◽  
Wen-Yang Xie ◽  
Shuai Shao ◽  
Ye Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Information ◽  
Remote Sensing Image ◽  
Superior Performance ◽  
Collaborative Representation ◽  
Scene Classification ◽  
Spatial Pyramid Matching ◽  
Classification Tasks ◽  
Pyramid Matching ◽  
Spatial Pyramid

At present, nonparametric subspace classifiers, such as collaborative representation-based classification (CRC) and sparse representation-based classification (SRC), are widely used in many pattern-classification and -recognition tasks. Meanwhile, the spatial pyramid matching (SPM) scheme, which considers spatial information in representing the image, is efficient for image classification. However, for SPM, the weights to evaluate the representation of different subregions are fixed. In this paper, we first introduce the spatial pyramid matching scheme to remote-sensing (RS)-image scene-classification tasks to improve performance. Then, we propose a weighted spatial pyramid matching collaborative-representation-based classification method, combining the CRC method with the weighted spatial pyramid matching scheme. The proposed method is capable of learning the weights of different subregions in representing an image. Finally, extensive experiments on several benchmark remote-sensing-image datasets were conducted and clearly demonstrate the superior performance of our proposed algorithm when compared with state-of-the-art approaches.

scholarly journals SPATIAL-SPECTRAL FUZZY K-MEANS CLUSTERING FOR REMOTE SENSING IMAGE SEGMENTATION

2018 ◽  
Vol 56 (2) ◽  
pp. 257 ◽  
Author(s):  
Mai Dinh Sinh ◽  
Ngo Thanh Long ◽  
Trinh Le Hang
Keyword(s):  
Remote Sensing ◽  
Fuzzy Clustering ◽  
Spectral Clustering ◽  
Clustering Algorithm ◽  
Spatial Information ◽  
Remote Sensing Image ◽  
Information Value ◽  
Local Optimum ◽  
Mathematical Framework ◽  
Data Space

Spectral clustering is a clustering method based on algebraic graph theory. The clustering effect by using spectral method depends heavily on the description of similarity between instances of the datasets. Althought, spectral clustering has been significant interest in recent times, but the raw spectral clustering is often based on Euclidean distance, but it is impossible to accurately reflect the complexity of the data. Despite having a well-defined mathematical framework, good performance and simplicity, it suffers from several drawbacks, such as it is unable to determine a reasonable cluster number, sensitive to initial condition and not robust to outliers. In this paper, we present a new approach named spatial-spectral fuzzy clustering which combines spectral clustering and fuzzy clustering with spatial information into a unified framework to solve these problems, the paper consists of three main steps: Step 1, calculate the spatial information value of the pixels, step 2 applies the spectral clustering algorithm to change the data space from the color space to the new space and step 3 clusters the data in new data space by fuzzy clustering algorithm. Experimental results on the remote sensing image were evaluated based on a number of indicators, such as IQI, MSE, DI and CSI, show that it can improve the clustering accuracy and avoid falling into local optimum. 

Research on the Optimal Classification Method for Remote Sensing Image Based on the Gabor-PCA Analysis

2014 ◽  
Vol 989-994 ◽  
pp. 3617-3620
Author(s):  
Jing Hui Yang ◽  
Li Guo Wang ◽  
Jin Xi Qian
Keyword(s):  
Remote Sensing ◽  
Spatial Information ◽  
Gabor Filter ◽  
Principal Component ◽  
Remote Sensing Image ◽  
Classification Method ◽  
Spectral Features ◽  
Classification Methods ◽  
Remote Sensing Image Classification ◽  
Core Idea

According to the problem that the traditional remote sensing image classification methods focus only on analyzing the spectral features and have low utilization of the spatial information, a new spatial-spectral classification method is proposed in this paper, its core idea is to combine the spectral features base on the Principal Component Analysis (PCA) algorithm with the spatial features extracted by the Gabor filter. Experiments show that, compared with the traditional classification methods, the proposed method can improve the classification accuracy and the Kappa coefficient, which means to bring better classification and visual effects.

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