Developing an Image-Based Classifier for Detecting Poetic Content in Historic Newspaper Collections

Developing an Image-Based Classifier for Detecting Poetic Content in Historic Newspaper Collections details and analyzes the first stage of work of the Image Analysis for Archival Discovery project team. Our team is is investigating the use of image analysis to identify poetic content in historic newspapers. The project seeks both to augment the study of literary history by drawing attention to the magnitude of poetry published in newspapers and by making the poetry more readily available for study, as well as to advance work on the use of digital images in facilitating discovery in digital libraries and other digitized collections. We have recently completed the process of training our classifier for identifying poetic content, and as we prepare to move in to the deployment stage, we are making available our methods for classification and testing in order to promote further research and discussion. The precision and recall values achieved during the training (90.58%; 79.4%) and testing (74.92%; 61.84%) stages are encouraging. In addition to discussing why such an approach is needed and relevant and situating our project alongside related work, this paper analyzes preliminary results, which support the feasibility and viability of our approach to detecting poetic content in historic newspaper collections

Enhancing sea ice segmentation in Sentinel-1 images with atrous convolutions

Due to the growing volume of remote sensing data and the low latency required for safe marine navigation, machine learning (ML) algorithms are being developed to accelerate sea ice chart generation, currently a manual interpretation task. However, the low signal-to-noise ratio of the freely available Sentinel-1 Synthetic Aperture Radar (SAR) imagery, the ambiguity of backscatter signals for ice types, and the scarcity of open-source high-resolution labelled data makes automating sea ice mapping challenging. We use Extreme Earth version 2, a high-resolution benchmark dataset generated for ML training and evaluation, to investigate the effectiveness of ML for automated sea ice mapping. Our customized pipeline combines ResNets and Atrous Spatial Pyramid Pooling for SAR image segmentation. We investigate the performance of our model for: i) binary classification of sea ice and open water in a segmentation framework; and ii) a multiclass segmentation of five sea ice types. For binary ice-water classification, models trained with our largest training set have weighted F1 scores all greater than 0.95 for January and July test scenes. Specifically, the median weighted F1 score was 0.98, indicating high performance for both months. By comparison, a competitive baseline U-Net has a weighted average F1 score of ranging from 0.92 to 0.94 (median 0.93) for July, and 0.97 to 0.98 (median 0.97) for January. Multiclass ice type classification is more challenging, and even though our models achieve 2% improvement in weighted F1 average compared to the baseline U-Net, test weighted F1 is generally between 0.6 and 0.80. Our approach can efficiently segment full SAR scenes in one run, is faster than the baseline U-Net, retains spatial resolution and dimension, and is more robust against noise compared to approaches that rely on patch classification

A Comprehensive, Automated Approach to Determining Sea Ice Thickness from SAR Data

This paper documents an approach to sea ice classification through a combination of methods, both algorithmic and heuristic. The resulting system is a comprehensive technique, which uses dynamic local thresholding as a classification basis and then supplements that initial classification using heuristic geophysical knowledge organized in expert systems. The dynamic local thresholding method allows separation of the ice into thickness classes based on local intensity distributions. Because it utilizes the data within each image, it can adapt to varying ice thickness intensities to regional and seasonal charges and is not subject to limitations caused by using predefined parameters

Automated Ice-Water Classification using Dual Polarization SAR Imagery

Mapping ice and open water in ocean bodies is important for numerous purposes including environmental analysis and ship navigation. The Canadian Ice Service (CIS) currently has several expert ice analysts manually generate ice maps on a daily basis. The CIS would like to augment their current process with an automated ice-water discrimination algorithm capable of operating on dual-pol synthetic aperture radar (SAR) images produced by RADARSAT-2. Automated methods can provide mappings in larger volumes, with more consistency, and in finer resolutions that are otherwise impractical to generate. We have developed such an automated ice-water discrimination system called MAGIC. The algorithm first classifies the HV scene using the glocal method, a hierarchical region-based classification method. The glocal method incorporates spatial context information into the classification model using a modified watershed segmentation and a previously developed MRF classification algorithm called IRGS. Second, a pixel-based support vector machine (SVM) using a nonlinear RBF kernel classification is performed exploiting SAR grey-level co-occurrence matrix (GLCM) texture and backscatter features. Finally, the IRGS and SVM classification results are combined using the IRGS approach but with a modified energy function to accommodate the SVM pixel-based information. The combined classifier was tested on 61 ground truthed dual-pol RADARSAT-2 scenes of the Beaufort Sea containing a variety of ice types and water patterns across melt, summer, and freeze-up periods. The average leave-one-out classification accuracy with respect to these ground truths is 95.8% and MAGIC attains an accuracy of 90% or above on 88% of the scenes. The MAGIC system is now under consideration by CIS for operational use

Automated Ice-Water Classification using Dual Polarization SAR Imagery

Mapping ice and open water in ocean bodies is important for numerous purposes including environmental analysis and ship navigation. The Canadian Ice Service (CIS) currently has several expert ice analysts manually generate ice maps on a daily basis. The CIS would like to augment their current process with an automated ice-water discrimination algorithm capable of operating on dual-pol synthetic aperture radar (SAR) images produced by RADARSAT-2. Automated methods can provide mappings in larger volumes, with more consistency, and in finer resolutions that are otherwise impractical to generate. We have developed such an automated ice-water discrimination system called MAGIC. The algorithm first classifies the HV scene using the glocal method, a hierarchical region-based classification method. The glocal method incorporates spatial context information into the classification model using a modified watershed segmentation and a previously developed MRF classification algorithm called IRGS. Second, a pixel-based support vector machine (SVM) using a nonlinear RBF kernel classification is performed exploiting SAR grey-level co-occurrence matrix (GLCM) texture and backscatter features. Finally, the IRGS and SVM classification results are combined using the IRGS approach but with a modified energy function to accommodate the SVM pixel-based information. The combined classifier was tested on 61 ground truthed dual-pol RADARSAT-2 scenes of the Beaufort Sea containing a variety of ice types and water patterns across melt, summer, and freeze-up periods. The average leave-one-out classification accuracy with respect to these ground truths is 95.8% and MAGIC attains an accuracy of 90% or above on 88% of the scenes. The MAGIC system is now under consideration by CIS for operational use

A comprehensive, automated approach to determining sea ice thickness from SAR data

Abstract-This paper documents an approach to sea ice classification through a combination of methods, both algorithmic and heuristic. The resulting system is a comprehensive technique, which uses dynamic local thresholding as a classification basis and then supplements that initial classification using heuristic geophysical knowledge organized in expert systems. The dynamic local thresholding method allows separation of the ice into thickness classes based on local intensity distributions. Because it utilizes the data within each image, it can adapt to varying ice thickness intensities to regional and seasonal charges and is not subject to limitations caused by using predefined parameters. I

Determination of surface water area using multitemporal SAR imagery

Inland water and freshwater constitute a valuable natural resource in economic, cultural, scientific and educational terms. Their conservation and management are critical to the interests of all humans, nations and governments. In many regions these precious heritages are in crisis. The main focus of this research is to investigate the capability of time variable ENVISAT ASAR imagery to extract water surface and assess the water surface area variations of lake Poyang in the basin of Yangtze river, the largest freshwater lake in China. Nevertheless, the lake has been in a critical situation in recent years due to a decrease of surface water caused by climate change and human activities. In order to classify water and land areas and to achieve the temporal changes of water surface area from ASAR images during the period 2006-2011, the image segmentation technique was implemented. For this purpose, a thorough analysis of the SAR system and its properties is first discussed. Indeed, some impairments can affect the SAR imaging signals. These impairments such as different types of scattering, surface roughness, dielectric property of water, speckle and geometric distortions can reduce SAR image quality. To avoid these distortions or to reduce their impact, it is therefore important to pre-process SAR images effectively and accurately. All the images were pre-processed using NEST software provided by ESA. To calculate the water surface area, each image was tiled into 9 parts and then it is segmented using two different methods. Firstly histogram for each tile is observed. Using a local adaptive thresholding technique, two local maxima were determined on the histogram and then in between these local maxima, a local minimum is determined which can be considered as the threshold. In the second technique a Gaussian curve was fitted using Levenberg-Marquardt method (1944 and 1963) to obtain a threshold. These thresholds are used to segment the image into homogeneous land and water regions. Later, the time series for both methods is derived from the estimated water surface areas. The results indicate an intense decreasing trend in Poyang Lake surface area during the period 2006-2011. Especially between 2010 and 2011, the lake significantly lost its surface area as compared to the year 2006. Finally, the results are presented for both locally adaptive thresholding and Levenberg-Marquardt methods. These results illustrate the effectiveness of the locally adaptive thresholding method to detect water surface change. A continuous monitoring of water surface change would lead to a long term time series, which is definitely beneficial for water management purposes

Investigation of coastal dynamics of the Antarctic Ice Sheet using sequential Radarsat SAR images

Increasing human activities have brought about a global warming trend, and cause global sea level rise. Investigations of variations in coastal margins of Antarctica and in the glacial dynamics of the Antarctic Ice Sheet provide useful diagnostic information for understanding and predicting sea level changes. This research investigates the coastal dynamics of the Antarctic Ice Sheet in terms of changes in the coastal margin and ice flow velocities. The primary methods used in this research include image segmentation based coastline extraction and image matching based velocity derivation. The image segmentation based coastline extraction method uses a modified adaptive thresholding algorithm to derive a high-resolution, complete coastline of Antarctica from 2000 orthorectified SAR images at the continental scale. This new coastline is compared with the 1997 coastline also derived from orthorectified Radarsat SAR images, and the 1963 coastline derived from Argon Declassified Intelligence Satellite Photographs for change detection analysis of the ice margins. The analysis results indicate, in the past four decades, the Antarctic ice sheet experienced net retreat and its areal extent has been reduced significantly. Especially, the ice shelves and glaciers on the Antarctic Peninsula reveal a sustained retreating trend. In addition, the advance, retreat, and net change rates have been measured and inventoried for 200 ice shelves and glaciers. A multi-scale image matching algorithm is developed to track ice motion and to measure ice velocity for a number of sectors of the Antarctic coast based on 1997 and 2000 SAR image pairs. The results demonstrate that a multi-scale image matching algorithm is much more efficient and accurate compared with the conventional algorithm. The velocity measurements from the image matching method have been compared with those derived from InSAR techniques and those observed from conventional ground surveys during 1970-1971. The comparison reveals that the ice velocity in the front part of the Amery Ice Shelf has increased by about 50-200 m/a. The rates of ice calving and temporal variation of ice flow pattern have been also analyzed by integrating the ice margin change measurement with the ice flow velocity at the terminus of the outlet glacier

Automated Remote Sensing Image Interpretation with Limited Labeled Training Data

Automated remote sensing image interpretation has been investigated for more than a decade. In early years, most work was based on the assumption that there are sufficient labeled samples to be used for training. However, ground-truth collection is a very tedious and time-consuming task and sometimes very expensive, especially in the field of remote sensing that usually relies on field surveys to collect ground truth. In recent years, as the development of advanced machine learning techniques, remote sensing image interpretation with limited ground-truth has caught the attention of researchers in the fields of both remote sensing and computer science. Three approaches that focus on different aspects of the interpretation process, i.e., feature extraction, classification, and segmentation, are proposed to deal with the limited ground truth problem. First, feature extraction techniques, which usually serve as a pre-processing step for remote sensing image classification are explored. Instead of only focusing on feature extraction, a joint feature extraction and classification framework is proposed based on ensemble local manifold learning. Second, classifiers in the case of limited labeled training data are investigated, and an enhanced ensemble learning method that outperforms state-of-the-art classification methods is proposed. Third, image segmentation techniques are investigated, with the aid of unlabeled samples and spatial information. A semi-supervised self-training method is proposed, which is capable of expanding the number of training samples by its own and hence improving classification performance iteratively. Experiments show that the proposed approaches outperform state-of-the-art techniques in terms of classification accuracy on benchmark remote sensing datasets.4 month