In this paper, an object-based change detection method for multi-temporal remote sensing images based on the Chi Square Transformation (CST) and sample selection is proposed to measure change in the large-size multi-temporal remotely sensed images used for monitoring national geographic conditions, In this new change detection method, image segmentation is used to obtain image objects. Secondly, multiple features are extracted from image objects, and a weighted difference is calculated for each image object based on CST. Then, with adaptively selected training samples a change threshold is automatically calculated using Expectation Maximization (EM) and a Bayesian rule with a minimum error rate. The weighted difference image is segmented into a binary image with a change threshold to derive change detection results. Multi-temporal high-resolution images of the Wuhan East Lake New Technology Development Zone were used for land cover change detection, experimental results show that the proposed method can obtain the most accurate change threshold among three tested methods. These highly accuracy change detection results effectively reduce the rate of lost detection, and are currently used for monitoring geographic conditions.