One of the consequences of a fire is smoke. Occasionally, monitoring and detection of this smoke can be a solution to prevent occurrence or spreading a fire. On the other hand, due to the destructive effects of the smoke spreading on human health, measures can be taken to improve the level of health services by zoning and monitoring its expansion process. In this paper, an automated method is proposed to detect the dilute smoke caused by large fires in multispectral images. The main idea of this method is the impossibility of precisely reconstructing the smoke in the bands affected by smoke (blue band) using regression models from other spectral bands. In the first step of the proposed method, the absolute value of the residuals of the regression estimation of blue spectral band is transformed into a binary mask with the help of Otsu thresholding. Afterwards, in an iterative process, non-smoke areas are detected and then clustered. In the iteration process, a regression model is fitted for each cluster and for each pixel, coefficients with the least error of the blue band reconstruction is used. Through more accurate estimation of the blue band, it reduces the effect of First Positive Error and leads the mask of residuals obtained from thresholding process to the smoke areas. The final step of the proposed method is to refine and remove the incorrect image segments. This method has been successful in detecting diluted smokes and also in disregarding smoke in non-smoky images. The results show the average accuracy of  99.04 percent in several datasets with diluted smokes.