Features extraction from multi-spectral remote sensing images based on multi-threshold binarization.

In this paper, we propose a solution to resolve the limitation of deep CNN models in real-time applications. The proposed approach uses multi-threshold binarization over the whole multi-spectral remote sensing image to extract the vector of discriminative features for classification. We compare the classification accuracy and the training time of the proposed approach with ResNet and Ensemble CNN models. The proposed approach shows a significant advantage in accuracy for small datasets, while keeping very close recall score to both deep CNN models for larger datasets. On the other hand, regardless of the dataset size, the proposed multi-threshold binarization provides approximately 5 times lower training and inference time than both ResNet and Ensemble CNN models.

Spatial point patterns generation on remote sensing data using convolutional neural networks with further statistical analysis.

Continuous technological growth and the corresponding environmental implications are triggering the enhancement of advanced environmental monitoring solutions, such as remote sensing. In this paper, we propose a new method for the spatial point patterns generation by classifying remote sensing images using convolutional neural network. To increase the accuracy, the training samples are extended by the suggested data augmentation scheme based on the similarities of images within the same part of the landscape for a limited observation time. The image patches are classified in accordance with the labels of previously classified images of the manually prepared training and test samples. This approach has improved the accuracy of image classification by 7% compared to current best practices of data augmentation. A set of image patch centers of a particular class is considered as a random point configuration, while the class labels are used as marks for every point. A marked point pattern is regarded as a combination of several subpoint patterns with the same qualitative marks. We analyze the bivariate point pattern to identify the relationships between points of different types using the features of a marked random point pattern.

Artificial Intelligence and Its Application to Minimal Hepatic Encephalopathy Diagnosis.

Hepatic encephalopathy (HE) is a brain dysfunction caused by liver insufficiency and/or portosystemic shunting. HE manifests as a spectrum of neurological or psychiatric abnormalities. Diagnosis of overt HE (OHE) is based on the typical clinical manifestation, but covert HE (CHE) has only very subtle clinical signs and minimal HE (MHE) is detected only by specialized time-consuming psychometric tests, for which there is still no universally accepted gold standard. Significant progress has been made in artificial intelligence and its application to medicine. In this review, we introduce how artificial intelligence has been used to diagnose minimal hepatic encephalopathy thus far, and we discuss its further potential in analyzing speech and handwriting data, which are probably the most accessible data for evaluating the cognitive state of the patient.