Recent developments and the widespread adoption of remote sensing data (RSD) gave rise to various hyperspectral imaging applications (HSI). With its detailed spectral information, HSI has been adopted for use in various agriculture-related applications. These applications demand more accurate classification, highlighting the significance of HSI in plant disease detection, crop classification, etc. Despite existing models for RSD-based agricultural applications, such models lack generalizability for plant classification. This task is challenging for the UNet-based architectures due to non-linear combinations of the pixels in the hyperspectral image, reduced and diverse information of each pixel, and high dimensions. Furthermore, a shortage of labeled data makes achieving high classification accuracy challenging. This study proposes an improved 3D UNet architecture based on a modified convolutional neural network that uses spatial and spectral information. This approach solves the limitations of existing UNet-based models, which suffer to deal with non-linear combinations and reduced and diverse information of small pixels. The proposed model employs a semantic segmentation strategy with modified architecture for more accurate classification and segmentation. The studies employ widely recognized benchmark HSI datasets, such as the Indian Pines (IP), Salinas, Pavia University (PU), Honghu, and Xiong'an datasets. These datasets are assessed using average accuracy, overall accuracy, and the Kappa coefficient. The suggested model demonstrated exceptional classification accuracy, achieving 99.60% for the Indian Pines dataset and 99.67% for the Pavia University dataset. The proposed approach is further validated and proven to be superior and robust through comparisons with existing models.

Several deep learning and transformer models have been recommended in previous research to deal with the classification of hyperspectral images (HSIs). Among them, one of the most innovative is the bidirectional encoder representation from transformers (BERT), which applies a distance-independent approach to capture the global dependency among all pixels in a selected region. However, this model does not consider the local spatial-spectral and spectral sequential relations. In this paper, a dual-dimensional (i.e., spatial and spectral) BERT (the so-called D²BERT) is proposed, which improves the existing BERT model by capturing more global and local dependencies between sequential spectral bands regardless of distance. In the proposed model, two BERT branches work in parallel to investigate relations among pixels and spectral bands, respectively. In addition, the layer intermediate information is used for supervision during the training phase to enhance the performance. We used two widely employed datasets for our experimental analysis. The proposed D²BERT shows superior classification accuracy and computational efficiency with respect to some state-of-the-art neural networks and the previously developed BERT model for this task.

Convolutional neural networks (CNNs) recently have achieved impressive performance for hyperspectral image denoising. However, current CNNs have limitations in exploring spectral correlations across various bands and the interactions among features within each band. Although transformers are introduced to capture spatial-spectral correlation in HSI, generally, they either explore inter-correlation in bands or intra-correlation between bands, neglecting the combination of intra- and inter-correlation in HSI cubes. Besides, transformer methods rarely address hierarchical (i.e., the low- and high-level) features in an adaptive manner. That is, features at different levels are of different importance, whereas these features are tackled equally in these methods. To alleviate these limitations, we introduce a joint architecture (the so-called MAOTformer) of mixed-attention transformers and octave modules for HSI denoising. On the one hand, the mixed attention transformer (MATB) is designed to simultaneously capture pixel relationships inter- and intra-bands by incorporating naive spatial self-attention, bidirectional recurrent channel attention, and progressive channel attention. Besides, a U-net based on mixed attention is equipped with attentive skip connections in MATB, which enables the proposed MAOTformer to explore hierarchical features by U-net and adaptively connect these hierarchical features by the attentive skip connections. On the other hand, we introduce an octave module behind each MATB to utilize multi-scale features for separating noise in high-frequency components. Extensive experiments are conducted on synthetic and real-world hyperspectral images (HSI), showing that the proposed method outperforms state-of-the-art methods.

The information has evolved rapidly over the World Wide Web in the past few years. To satisfy information needs, users mostly submit a query via traditional search engines, which retrieve results on the basis of keyword matching principle. However, a keyword-based search cannot recognize the meanings of keywords and the semantic relationship among the terms in the user’s query; thus, this technique cannot retrieve satisfactory results. The expansion of an initial query with relevant meaningful terms can solve this issue and enhance information retrieval. Generally, query expansion methods consider concepts that are semantically related to query terms within the ontology as candidates in expanding the initial query. An analysis of the correct sense of query terms, rather than only considering semantic relations, is necessary to overcome language ambiguity problems. In this work, we proposed a query expansion framework on the basis of query sense analysis and semantics mining using computer science domain ontology, followed by working prototype of the system. The experts analyzed the results of system prototype over test dataset and Web data, and found a remarkable improvement in the overall search performance. Furthermore, the proposed framework demonstrated better mean average precision and recall values than the baseline method.

Usability of Government websites plays pivotal role in order to provide benefits and services to the citizens. This study presents a usability evaluation for investigating the Nielsen’s usability attributes in Government websites. Based on the previous studies, a proposed website template is used in this study. This template is compared with a selected Government website. Thirty (30) participants performed three (3) representative tasks for each website. The results show that the user responses for the parameters of efficiency, memorability and pleasantness are improved for the proposed template. This effort is a part of the study that may lead to the principles for improving the usability of Government websites of Pakistan.