32 research outputs found
Enhancing Multivariate Time Series Classifiers through Self-Attention and Relative Positioning Infusion
Time Series Classification (TSC) is an important and challenging task for
many visual computing applications. Despite the extensive range of methods
developed for TSC, relatively few utilized Deep Neural Networks (DNNs). In this
paper, we propose two novel attention blocks (Global Temporal Attention and
Temporal Pseudo-Gaussian augmented Self-Attention) that can enhance deep
learning-based TSC approaches, even when such approaches are designed and
optimized for a specific dataset or task. We validate this claim by evaluating
multiple state-of-the-art deep learning-based TSC models on the University of
East Anglia (UEA) benchmark, a standardized collection of 30 Multivariate Time
Series Classification (MTSC) datasets. We show that adding the proposed
attention blocks improves base models' average accuracy by up to 3.6%.
Additionally, the proposed TPS block uses a new injection module to include the
relative positional information in transformers. As a standalone unit with less
computational complexity, it enables TPS to perform better than most of the
state-of-the-art DNN-based TSC methods. The source codes for our experimental
setups and proposed attention blocks are made publicly available
Cloud and Cloud Shadow Segmentation for Remote Sensing Imagery via Filtered Jaccard Loss Function and Parametric Augmentation
Cloud and cloud shadow segmentation are fundamental processes in optical remote sensing image analysis. Current methods for cloud/shadow identification in geospatial imagery are not as accurate as they should, especially in the presence of snow and haze. This paper presents a deep learning-based framework for the detection of cloud/shadow in Landsat 8 images. Our method benefits from a convolutional neural network, Cloud-Net+ (a modification of our previously proposed Cloud-Net) that is trained with a novel loss function (Filtered Jaccard Loss). The proposed loss function is more sensitive to the absence of foreground objects in an image and penalizes/rewards the predicted mask more accurately than other common loss functions. In addition, a sunlight direction-aware data augmentation technique is developed for the task of cloud shadow detection to extend the generalization ability of the proposed model by expanding existing training sets. The combination of Cloud-Net+, Filtered Jaccard Loss function, and the proposed augmentation algorithm delivers superior results on four public cloud/shadow detection datasets. Our experiments on Pascal VOC dataset exemplifies the applicability and quality of our proposed network and loss function in other computer vision applications
Automatic building detection in aerial and satellite images
Abstract—Automatic creation of 3D urban city maps could be an innovative way for providing geometric data for varieties of applications such as civilian emergency situations, natural disaster management, military situations, and urban planning. Reliable and consistent extraction of quantitative information from remotely sensed imagery is crucial to the success of any of the above applications. This paper describes the development of an automated roof detection system from single monocular electro-optic satellite imagery. The system employs a fresh ap-proach in which each input image is segmented at several levels. The border line definition of such segments combined with line segments detected on the original image are used to generate a set of quadrilateral rooftop hypotheses. For each hypothesis a probability score is computed that represents the evidence of true building according to the image gradient field and line segment definitions. The presented results demonstrate that the system is capable of detecting small gabled residential rooftops with variant light reflection properties with high positional accuracies. Index Terms—Building extraction, satellite image processing, aerial image processing, photogrammetry, computer vision, geo-metrical shape extraction. I
Green and practical synthesis of benzopyran and 3-sunstituted coumarin derivatives by Brønsted acid ionic liquid [(CH2)4SO3HMIM][HSO4]
Different benzopyran and 3-substituted coumarin derivatives were synthesized by a green and practical procedure in the presence of catalytic amount of Brønsted acid ionic liquid (BAIL) [(CH2)4SO3HMIM][HSO4] in water.KEY WORDS: Benzopyran derivatives, 3-Substituted coumarin derivatives, Brønsted acid ionic liquid, 1-(4-Sulfonic acid)butyl-3-methylimidazolium hydrogen sulfate Bull. Chem. Soc. Ethiop. 2011, 25(2), 315-320.