Analysis of remote sensing images by methods of convolutional neural networks and marked random point fields

The methodology of remote sensing image analysis for detection of dependences in the process of development of biological species is proposed. Classification methods based on convolutional networks are applied to a set of fragments of the input image. In order to increase the accuracy of classification by increasing the training and test samples, an original method of data augmentation is proposed. For a series of images of one part of the landscape, the fragments of images are classified by their numbers, which coincide with the numbers of the previously classified image of the training and test samples which are created manually. This approach has improved the accuracy of classification compared to known methods of data augmentation. Numerous studies of various convolutional neural networks have shown the similarity of the classification results of the remote sensing images fragments with increasing learning time with the complication of the network structure. A set of image fragment centers of a particular class is considered as random point configuration, the class labels are used as a mark for every point. Marked point field is considered as consisting of several sub-point fields in each of which all points have the same qualitative marks. We perform the analysis of the bivariate point pattern to reveal relationships between points of different types, using the characteristics of marked random point fields. Such relationships can characterize dependences and relative degrees of dominance. A series of remote sensing images are studied to identify the relationships between point configurations that describe different classes to monitor their development.

On the statistics of anomalous clumps in random point images

New algorithms for calculating exact analytical formulas describing two related probabilities are proposed, substantiated and software implemented: 1) the probability of the formation of anomalously large local groups in a random point image; 2) the probability of the absence of significant local groupings in a random point image.