With the rapid development of remote sensing, digital image processing has become an important tool for the quantitative and statistical analysis of remotely sensed images. These images most often contain complex natural scenes. The robust interpretation of such images requires the use of different sources of information about the scenes under consideration. This paper presents an integrated approach to robust analysis of SPOT images with the aid of map information as well as a priori knowledge about the contextual information of images. Markov random field theory and the Bayes formula are used to formulate the image analysis problem as a problem of optimization of an objective function, which in turn permits the application of various existing optimization algorithms to solve the problem. To increase the robustness of the result, several techniques are proposed to effectively use map information and image contextual information. The first one is concerned with the estimation of the parameters in the objective function with the help of these two sources of information. The second one is the integration of map information in Bayes image modeling using a Markov random field. The third one is a new optimization algorithm which takes into account map information and image contextual information by means of a feedback control scheme. The last technique proposed to increase the robustness of the result is concerned with the fusion of several (intermediate) analysis results by again using map knowledge and image contextual information for the estimation of the reliability of these results