In recent years, the utilization of hyperspectral sensors for remote sensing has marked a profound advancement due to the success of machine learning techniques. Nevertheless, difficulties still exist, especially in locations with shadows. The heterogeneity in spectral data due to different shadow origins, such as different types of clouds and different building designs, poses a significant obstacle to the advancement of shadow-aware classification algorithms. Furthermore, precisely labeling the underlying structures in shadowed areas is a very cumbersome effort. We present a loss function-based strategy based on generative adversarial networks to address this problem. Using the context of correlated samples, our loss function combines unpaired matchings and transitive style modifications via the fusion of contrastive learning, dual learning, cycle consistency, and curriculum learning algorithms. Our work transforms the non-shadowed training instances into the shadowed counterparts for use as synthetic training samples, as opposed to the conventional method of correcting shadowed pixels to their non-shadowed counterparts. We propose learning this transformation model with unpaired data samples, which is particularly advantageous compared with the collection process of the same samples with and without shadow. Synthetic samples for shadow-obscured regions can be produced when this method is used, and these samples improve the model’s performance in classification tasks. Rigorously tested through a combination of qualitative and quantitative evaluations, the introduced data augmentation technique improves the performance of terrain classification models, especially with limited data samples.
Deep learning-based approaches have shown highly successful performance in the categorization of digitized biopsy samples. The commonly used setting in these approaches is to employ convolutional neural networks for classification of data sets consisting of images all having the same size. However, the clinical practice in breast histopathology necessitates multi-class categorization of regions of interest (ROI) in biopsy samples where these regions can have arbitrary shapes and sizes. The typical solution to this problem is to aggregate the classification results of fixed-sized patches cropped from these images to obtain image-level classification scores. Another limitation of these approaches is the independent processing of individual patches where the rich contextual information in the complex tissue structures has not yet been sufficiently exploited. We propose a generic methodology to incorporate local inter-patch context through a graph convolution network (GCN) that admits a graph-based ROI representation. The proposed GCN model aims to propagate information over neighboring patches in a progressive manner towards classifying the whole ROI into a diagnostic class. The experiments using a challenging data set for a 4-class ROI-level classification task and comparisons with several baseline approaches show that the proposed model that incorporates the spatial context by using graph convolutional layers performs better than commonly used fusion rules.
A novel method to detect flames in infrared (IR) video is proposed. Image regions containing flames appear as bright regions in IR video. In addition to ordinary motion and brightness clues, the flame flicker process is also detected by using a hidden Markov model (HMM) describing the temporal behavior. IR image frames are also analyzed spatially. Boundaries of flames are represented in wavelet domain and the high frequency nature of the boundaries of fire regions is also used as a clue to model the flame flicker. All of the temporal and spatial clues extracted from the IR video are combined to reach a final decision. False alarms due to ordinary bright moving objects are greatly reduced because of the HMM-based flicker modeling and wavelet domain boundary modeling.
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