Tree species composition mapping with dimension reduction and post-classification using very high-resolution hyperspectral imaging.

Tree species' composition of forests is essential in forest management and nature conservation. We aimed to identify the tree species structure of a floodplain forest area using a hyperspectral image. We proposed an efficient novel strategy including the testing of three dimension reduction (DR) methods: Principal Component Analysis, Minimum Noise Fraction (MNF) and Indipendent Component Analysis with five machine learning (ML) algorithms (Maximum Likelihood Classifier, Support Vector Classification, Support Vector Machine, Random Forest and Artificial Neural Network) to find the most accurate outcome; altogether 300 models were calculated. Post-classification was applied by combining the multiresolution segmentation and filtering. MNF was the most efficient DR technique, and at least 7 components were needed to gain an overall accuracy (OA) of > 75%. Forty-five models had > 80% OAs; MNF was 43, and the Maximum Likelihood was 19 times among these models. Best classification belonged to MNF with 10 components and Maximum Likelihood classifier with the OA of 83.3%. Post-classification increased the OA to 86.1%. We quantified the differences among the possible DR and ML methods, and found that even > 10% worse model can be found using popular standard procedures related to the best results. Our workflow calls the attention of careful model selection to gain accurate maps.

Segmentation of the uterine wall by an ensemble of fully convolutional neural networks.

In the past decades, the number of in vitro fertilization (IVF) procedures for the conception of a child has been rising continuously, however, the success rate of artificial insemination remained low. According to current statistics, large portion of unsuccessful IVF relates to some women' factors. As the directly related female organ, the proper investigation of the uterus has primary importance. Namely, visible markers may indicate inflammations or other negative effects that jeopardize successful implantation. The purpose of this study is to support the observability of the uterus from this aspect by providing computer-aided tools for the extraction of its wall from video hysteroscopy. As for methodology, fully convolutional neural networks (FCNNs) are used for the automatic segmentation of the video frames to determine the region of interest. We provide the necessary steps for the applicability of the general deep learning framework for this specific task. Moreover, we increase segmentation accuracy with applying ensemble-based approaches at two levels. First, the predictions of a given FCNN are aggregated for the overlapping regions of subimages, which are derived from the splitting of the original images. Next, the segmentation results of different FCNNs are fused via a weighted combination model; optimization for adjusting the weights are also provided. Based on our experimental results, we have achieved 91.56% segmentation accuracy regarding the recognition of the uterus wall.