Background and Objectives: Cadastral boundary detection deals with locating the boundary of the ownership and use of land. Recently, there has been High demand for accelerating and improving the automatic detection of cadastral mapping. As this problem is in its starting point, there are few researches using deep learning algorithms. Methods: In this paper, we develop an algorithm with a Mask R-CNN core followed with geometric post-processing methods that improve the quality of the output. Many researches use classification or semantic segmentation but our algorithm employs instance segmentation. Our algorithm includes two parts, each of which consists of a few phases. In the first part, we use Mask R-CNN with the backbone of a pre-trained ResNet-50 on the ImageNet dataset. In the second part, we apply three geometric post-processing methods to the output of the first part to get better overall output. Here, we also use computational geometry to introduce a new method for simplifying lines which we call pocket-based simplification algorithm.Results: We used 3 google map images with sizes 4963 × 2819, 3999 × 3999, and 5520 × 3776 pixels. And divide them to overlapping and non-overlapping 400×400 patches used for training the algorithm. Then we tested it on a google map image from Famenin region in Iran. To evaluate the performance of our algorithm, we use popular metrics Recall, Precision, and F-score. The Highest Recall is 95%, which also maintains a High precision of 72%. This results in an F-score of 82%.Conclusion: The idea of semantic segmentation to derive boundary of regions, is new. We used Mask R-CNN as the core of our algorithm, that is known as a Very suitable tools for semantic segmentation. Our algorithm performs geometric post-process improves the f-score by almost 10 percent. The scores for a region in Iran containing many small farms is Very good.

Many studies have investigated the problem of external disturbance rejection and also increasing the attitude control system's robustness against the parametric uncertainties. Due to stochastic properties, noise effect minimization becomes an interesting and challenging problem in the field of spacecraft attitude control that has been underestimated, while control actuators and attitude sensors themselves are important sources of noise generation., the main purpose of this paper is to (i)control the satellite’s attitude and (ii)minimize the variance of output, simultaneously. The Minimum Variance controller, which is considered the simplest type of model predictive controller, has a powerful capability for minimizing the effects of output noise. This feature makes it a suitable control scheme for space-based High-Resolution photography missions. so,, we described the conventional Minimum Variance regulator method at first, then an Incremental version of the regulator has been presented to solve the tracking problem. Finally, the generalized minimum variance controller which can control both minimum-phase and non-minimum-phase systems is derived for a High pointing accuracy spacecraft. The simulation results show the efficiency of the proposed controller to restrain the noise effects in a High-Resolution tri-stereo Imaging mission.

In the present research we have used gray level co-occurrence matrices (GLCM) and Gabor filters to extract texture features in order to classify satellite images. The main drawback of GLCM algorithm is its time-consuming nature. In this work, we proposed a fast GLCM algorithm to overcome the mentioned weakness of the traditional GLCM. The fast GLCM is capable of extracting approximately the same features as the traditional GLCM does, but in much less time (about 200 times faster). The other weakness of the traditional GLCM is its lower accuracy in the regions near the class borders. Since features extracted using Gabor filters are more accurate in boundary regions, we combined Gabor features with GLCM features. In this way we could compensate the latter mentioned weakness of GLCM. Experimental results show good capabilities of the proposed fast GLCM and the feature fusion method in classification of Very High Resolution remote sensing images.