This letter presents a landcover segmentation approach by analyzing multi-spectral, multi-temporal Sentinel-2 satellite images through deep learning based fully convolutional networks (FCNs). Existing segmentation methods face problems in generating accurate segmentation masks for satellite scenes with bright pixels and those containing cloud cover, and incur a high computational cost. To tackle these problems, first, we generated cloud cover masks by employing a trained U-Net++ based architecture with ResNet-50 backbone and a lightweight attention mechanism to remove too cloudy satellite scenes as they hide the ground view. Second, to incorporate the benefits of attention mechanisms while keeping the computational cost low, a 3D FCN-based satellite image segmentation architecture with lightweight Coordinate Attention (CA) mechanism is proposed for generating landcover segmentation masks. Third, a novel dataset comprising of multi-temporal, multi-spectral Sentinel-2 satellite images for the year 2020 along with their ground truth mask was composed for the chosen study site which is a semi-arid region spanning over an area of 4978 km2of Ludhiana district located in the state of Punjab, India. Understanding the patterns of land use and landcover in the context of such agriculturally rich regions experiencing significant urban population increase and cropland loss makes this study crucial. Mean Intersection over Union (mIoU) and F1 scores of 92.13% and 96.76%, respectively, were achieved.

Summary Cross‐domain sentiment classification is a significant task of sentiment analysis that objectives to predict the opinion orientation of text documents in the target domain by using the source domain's learned classifier. Most of the existing approaches of domain‐adaptation in sentiment classification focus on sharing low‐dimensional features across the domain using domain independent and specific features to mitigate the gap between domains. Earlier cross‐domain sentiment classification approaches mainly focused on document level and sentence level, they cannot consider the full impact of aspect words, position of the words, and long‐term dependencies. To address this concern, we propose a model for cross‐domain sentiment classification, which is based on decoding‐enhanced BERT with disentangled attention (DeBERTa). DeBERTa is a pretrained language model based on transformer architecture. In this article, we perform sentence and aspect embedding to mine wordpiece information from text document. DeBERTa language‐model utilize disentangled attention mechanism and an enhanced mask decoder to understand the expression features. Disentangled attention mechanism is used to encode each word into two vectors (i.e., content and position vector). In order to predict the masked tokens during model pretraining, an enhanced mask decoder is employed, which incorporates absolute positions in the decoding layer. Finally, experiments are conducted on the benchmark dataset that demonstrates the superiority of fine‐tuned DeBERTa model for cross‐domain sentiment classification tasks.

Abstract The purpose of cross‐domain opinion classification is to leverage useful information acquired from the source domain to train a classifier for opinion classification in the target domain, which has a huge amount of unlabeled data. An opinion classifier trained on a specific domain usually acts poorly, when directly employed to another domain. Annotating the data for all the domains is a laborious and costly process. The majority of available approaches are centered on identifying invariant features among domains. Unluckily, they are unable to properly capture the context within the sentences and better utilization of unlabeled data. To properly address this issue, we propose an aspect‐based attention model for cross‐domain opinion classification. By incorporating knowledge of aspects and sentences, the proposed model provides a transfer mechanism for better‐transferring opinions among domains. We introduce two learning networks, first learning network aims to recognize the shared features between domains, while the purpose of the second learning network is to extract the information from the aspects by utilizing shared words as a bridge. We benefit from BERT and bidirectional gated recurrent unit to get a deep understanding and deep level semantic information of the text. Further, the joint attention learning mechanism is performed for these two learning modules so that the aspects and sentences can impact the resulting opinion expression. In addition, we introduce a gradient reversal layer to obtain invariance features. The comprehensive experiments are performed on Amazon multidomain product datasets and show the effectiveness and significance of the proposed model over state‐of‐the‐art techniques.

The rapid growth of internet facilities has increased the comments, posts, blogs, feedback, etc., on a large scale on social networking sites. These social media data are available in an unstructured form, which includes images, text, and videos. The processing of these data is difficult, but some sentiment analysis, information retrieval, and recommender systems are used to process these unstructured data. To extract the opinion and sentiment of internet users from their written social media text, a sentiment analysis system is required to develop, which can work on both monolingual and bilingual phonetic text. Therefore, a sentiment analysis (SA) system is developed, which performs well on different domain datasets. The system performance is tested on four different datasets and achieved better accuracy of 3% on social media datasets, 1.5% on movie reviews, 1.35% on Amazon product reviews, and 4.56% on large Amazon product reviews than the state-of-art techniques. Also, the stemmer (StemVerb) for verbs of the English language is proposed, which improves the SA system's performance.