The drastic increase of the number of space debris in orbit around the earth poses a serious threat to operational spacecraft. Rapid small space debris detection with space-based surveillance platform is very important for spacecraft emergency avoidance at distance. Nevertheless, small space debris detection is a great challenge because of its fast movement and noise caused by cosmic rays in space-based surveillance platform. Inspired by strong pattern recognition capacity of deep learning, a deep convolutional neural network based small space debris saliency detection method for space-based surveillance system is proposed. First, the spatial contrast map of the space debris image is generated using local contrast method. Then, the spatiotemporal saliency information is captured incorporating with the above contrast map. The experimental results prove the applicability of the proposed method.