The linear detection method is usually difficult to adapt to the demand of remote sensing image processing because of it exhibits poor imaging regularity

complex background texture

and strong noises. To address these problems

we proposed a new method that possesses visual saliency in the remote sensing image and can detect principal features. First

the possibility of constructing principal lines according to the Gestalt laws and the use of extracted lines as geometric primitives were analyzed. The complex projection of the lines in the principal lines was then examined

and the definition of the principal lines was provided. Furthermore

the cumulative weight matrix of the principal line and linear statistical matrix were constructed. Meanwhile

the distribution regularity of the short line weight was studied according to the Gestalt laws

and the model of the short linear weight was constructed. Accordingly

the weight allocation pattern of the different lines in the same principal line was also discussed. Finally

detailed algorithm steps were proposed according to these analyses.The key algorithm steps were described as follows: first

the chain code marshaling algorithms were employed to extract the straight lines. Second

the accumulative weighted matrix and linear statistical matrix of the principal lines were constructed. Third

the lines were sorted on the basis of their spatial positions. Fourth

according to linear weight distribution regularity

all the lines were elected to a cumulative weight matrix according to the linear weight model and distribution rules

and the results were recorded in the linear statistical matrix. Fifth

the local maximum value of the accumulative matrix was obtained to prevent parallel overlapping among the principal lines. Sixth

constraint analysis on the continuity and purity of the accumulative weighted matrix and linear statistical matrix was conducted to prevent the appearance of false principal lines. Finally

the parameters of the principal lines were obtained according to the sorting results of the weight voting matrix and weight values. Meanwhile

the principal line was obtained through its endpoints.The results of multiple SAR and optical remote sensing satellite images with strong noises showed that the traditional line extraction method can obtain only the disordered linear information

which is not clear and useful for image processing. In this study

our proposed method obtained clear principal lines by using Gestalt law on the basis of traditional linear extraction algorithm

and the results were basically in agreement with artificial visual perception. Meanwhile

the results suggest that our algorithm is superior to the traditional cluster algorithm in terms of operation efficiency and experimental effects. The experimental results indicate the potential application of our method in various fields

such as road extraction

image matching

and object recognition. However

this method also presents several shortcomings. First

the extraction results of the principal lines rely heavily on previous results. In addition

whether the linear-weighted Gaussian model established in this study is in full compliance with the Gestalt law requires further investigation. Finally

several parameter settings are experience values acquired by a large number of experiments. Thus

we hope to achieve the adaptive processing of these parameters in our future research.