In this work, we propose a method based on Deep-Learning and Convolutional Neural Network (CNN) ensemble fine-tuning for the task of remote sensing imagery registration and processing. Our method is based on the CNN transfer learning technique that allows the use of large-scale models that are already pre-trained on big general datasets and finetunes them for a particular application area. This approach can significantly decrease the needed size of the training set, for cases where such big training datasets are not available, and improve the quality of classification using a larger CNN or an ensemble of CNNs. This paper addresses the challenges encountered at each stage of the proposed pipeline. For image registration, objects of predefined type are detected, such as roads with hardcover and buildings using a CNN ensemble. Also, a CNN ensemble is used to detect undesirable structures in the image, such as clouds or rocks on the agricultural fields. Our image segmentation method can be used for image matching and fusion. To test our approach, we use an annotated dataset from the Kaggle contest "Dstl Satellite Imagery Feature Detection," UC Merced Land Use Dataset, and a custom annotated dataset of remote sensing imagery of agricultural areas.