Global warming is one of the main challenges of recent times. The glaciers are melting faster than expected which has resulted in global mean sea level rise and increased the risk of floods. The development of modern remote sensing technology has made it possible to obtain images more frequently than ever before. On the other side, the availability of high-performance computing hardware and processing techniques have made it possible to provide a cost-effective solution to monitor the temporal changes of glaciers at a large scale. In this study, supervised machine learning methods are investigated for automatic classification of glacier covers from multi-temporal Sentinel-2 imagery using texture, topographic, and spectral data. Three most commonly used supervised machine learning techniques were investigated: support vector machine (SVM), artificial neural network (ANN) and random forest (RF). The proposed method was employed on the data obtained from Passu watershed in Hunza Basin located along the Hunza river in Pakistan. Three main classes were considered: glaciers, debris-covered glaciers and non-glaciated areas. The data was split into training (70%) and testing datasets (30%). Finally, an area-based accuracy assessment was performed by comparing the results obtained for each classifier with the reference data. Experiments showed that the results produced for all classifiers were highly accurate and visually more consistent with the depiction of glacier cover types. For all experiments, random forest performed the best (Kappa= 0.95, f-measure =95.06%) on all three classes compared to ANN (Kappa= 0.92, f-measure =92.05) and SVM (Kappa= 0.89, f-measure =91.86% on average). The high classification accuracy obtained to distinguished debris-covered glaciers using our approach will be useful to determine the actual available water resources which can be further helpful for hazard and water resource management.

Content-based video indexing and retrieval has become an interesting research area with the tremendous growth in the amount of digital media. In addition to the audio-visual content, text appearing in videos can serve as a powerful tool for semantic indexing and retrieval of videos. This paper proposes a method based on edge-features for horizontally aligned artificial Urdu text detection from video images. The system exploits edge based segmentation to extract textual content from videos. We first find the vertical gradients in the input video image and average the gradient magnitude in a fixed neighborhood of each pixel. The resulting image is binarized and the horizontal run length smoothing algorithm (RLSA) is applied to merge possible text regions. An edge density filter is then applied to eliminate noisy non-text regions. Finally, the candidate regions satisfying certain geometrical constraints are accepted as text regions. The proposed approach evaluated on a data set of 150 video images exhibited promising results.

Understanding tree species distribution and land use/cover classes plays a key role for developing environmental monitoring and decision support systems. This study investigates a method based on integration of multiple classifiers to improve the classification accuracy for extraction of tree species and land use/cover classes from large scale data. First, a diverse set of classifiers from different families of statistical learning was selected as base classifiers namely: support vector machine, artificial neural network, and random forest. Both spectral and spatial features were, then, extracted and fed into individual classifiers to classify data into four classes (tea gardens, other trees, impervious surfaces, and bare land). Finally, the results obtained from each classifier were combined to obtain final output by maximum voting. The proposed method was evaluated by using an area-based accuracy assessment on a dataset consisting of ten high-resolution digital orthophoto maps. Experimental results showed that integrating the outputs of individual classifiers improved (4%–7%) overall classification accuracy.