Remote sensing images of rivers are heterogenous and the intensities of the area inside the background are complicated. Drawing upon the traditional active contour models to segment the remote sensing images of rivers is ineffective. A hybrid active contour model with regional information fusion is proposed to improve the river extraction accuracy in remote sensing images of rivers to address this problem. The hybrid model combines the external region energy constraint terms of the CV model and the cross entropy-based active contour model. The different normalized ratio coefficients are incorporated into the above external region energy constraint terms. During evolution

both the variance and cross entropy information of the pixel grayscale values in the internal and external areas of the contour curve are calculated to guide the curve to approach the object edges. Moreover

the within-cluster absolute differences of the internal and external areas of the curve are incorporated to adaptively adjust the internal and external area energy weights instead of the original area energy weights and accelerate the hybrid model evolution. The proportion of the internal and external area energy can change continuously with the curve evolution

which ultimately improves the segmentation efficiency of the proposed model. Experiments are conducted on a large quantity of remote sensing images of rivers. The sensitivity of the proposed hybrid model is at almost 100 percent

which is similar to those of the CV model

geodesic active contours model

cross-entropy based active contour model

a hybrid model based on the CV and geodesic active contours models

and the LGIF model. Its accuracy is much higher than the other models

which exceeds 90 percent. False alarm incidents decreased by 50 percent. The iterative number and running time of the proposed model are also less than those of the other methods. This study proposes a novel hybrid active contour model with regional information fusion to achieve accurate river extraction in remote sensing images of rivers

which has a relative contrast between the area inside the background and area inside the river. The proposed model utilizes two area description criteria that are within-cluster variance and cross entropy to guide the curve evolution. This approach is more accurate than using a single-area description; hence

it can obtain better segmentation results. Moreover

the internal and external area energy weights of the proposed model can be adjusted adaptively

which leads to the rapid curve evolution. Extensive experiment results show that the proposed hybrid model not only obtains higher accuracy and sensitivity but also records fewer false alarms than the CV model

geodesic active contours model

cross entropy-based active contour model

a hybrid model based on the CV and geodesic active contours models

and the LGIF model. The proposed model has clear advantages in both segmentation performance and segmentation efficiency.