Remote Sensing Image can be degraded by a variety of causes during acquisition, transmission, compression, storage and reconstruction. Noise is one of the most important degradation factors. Quantifying its impact on the image may be useful for applications such as improving the acquisition system and thus the quality of the produced images. Objective Image Quality Measure (IQA) methods can be classified by whether a reference image, representing the original signal exists. In the case of remote sensing, the ideal un-degraded image is not available. No-reference (NR) method is required to blindly assess the image quality. In this paper, a new no-reference algorithm is proposed to quantify noise based on local phase coherence (LPC). This algorithm assumes that the input image is contaminated by additive zero mean Gaussian noise. Firstly, a LPC map of degraded image is constructed and the image edge is extracted by modifying the noise threshold. Secondly, the edge is removed from the LPC map. Then, the noise level can be quantified by the remaining noise information and little “residual” information of the LPC map. Experiment results show that the proposed algorithm correlates well with subjective quality evaluations and has high estimation accuracy especially for Gaussian noise-infected images.