A new algorithm for remotely sensed image texture classification and segmentation

In this paper, we propose a new algorithm for remotely sensed image texture classification and segmentation. We observe that the traditional method least square error (LSE) is unstable in practical applications. This motivates us to develop a more stable method. We have proposed the regularization technique to suppress the instability of LSE in previous research. Our contribution in this paper is that we propose a new stable method, which is based on the total variation (TV) for reducing instability in texture analysis, and apply it to remotely sensed image texture classification and segmentation. Experimental results on remotely sensed images demonstrate that our new algorithm is superior to LSE and seems promising in applications.

[1]  Rama Chellappa,et al.  Estimation and choice of neighbors in spatial-interaction models of images , 1983, IEEE Trans. Inf. Theory.

[2]  Mark S. C. Reed,et al.  Method of Modern Mathematical Physics , 1972 .

[3]  Wen Gao,et al.  A new method for classification and segmentation of the textured images , 2003 .

[4]  J. Besag.  Spatial Interaction and the Statistical Analysis of Lattice Systems , 1974 .

[5]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Anil K. Jain,et al.  Texture classification and segmentation using multiresolution simultaneous autoregressive models , 1992, Pattern Recognit..

[7]  Rangasami L. Kashyap,et al.  A Model-Based Method for Rotation Invariant Texture Classification , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Wen Gao,et al.  A regularized simultaneous autoregressive model for texture classification , 2003, Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS '03..

[9]  Alireza Khotanzad,et al.  Unsupervised Segmentation of Textured Images by Edge Detection in Multidimensional Feature , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[11]  Curtis R. Vogel,et al.  A Fast, Robust Algorithm for Total Variation Based Reconstruction of Noisy, Blurred Images , 1998 .

[12]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[13]  Curtis R. Vogel,et al.  Ieee Transactions on Image Processing Fast, Robust Total Variation{based Reconstruction of Noisy, Blurred Images , 2022 .

[14]  Rangasami L. Kashyap,et al.  Composite edge detection with random field models , 1990, IEEE Trans. Syst. Man Cybern..