Multiclass Non-Randomized Spectral–Spatial Active Learning for Hyperspectral Image Classification

Mazzara, Manuel; Raza, Rana Aamir; Distefano, Salvatore; Asif, Muhammad; Sarfraz, Muhammad Shahzad; Khan, Adil; Sohaib, Ahmed

doi:10.3390/app10144739

Cited by 20 publications

(9 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cluster analysis is also used to study the distribution of crimes. We have used k-means [36,37] clustering for cluster analysis over the spatial data of crime. Clusters are formed in a region where there is a greater tendency of the crime rate.…”

Section: Spatial Crime Analysismentioning

confidence: 99%

Spatiotemporal Analysis of Web News Archives for Crime Prediction

et al. 2020

Self Cite

View full text Add to dashboard Cite

In today’s world, security is the most prominent aspect which has been given higher priority. Despite the rapid growth and usage of digital devices, lucrative measurement of crimes in under-developing countries is still challenging. In this work, unstructural crime data (900 records) from the news archives of the previous eight years were extracted to predict the behavior of criminals’ networks and transform it into useful information using natural language processing (NLP). To estimate the next move of criminals in Pakistan, we performed hotspot-based spatial analysis. Later, this information is fed to two different classifiers for possible identification and prediction. We achieved the maximum accuracy of 92% using K-Nearest Neighbor (KNN) and 62% using the Random Forest algorithm. In terms of crimes, the results showed that the most prevalent crime events are robberies. Thus, the usage of digital information archives, spatial analysis, and machine learning techniques can open new ways of handling a peaceful and sustainable society in eradicating crimes for countries having paucity of financial resources.

show abstract

Section: Spatial Crime Analysismentioning

confidence: 99%

Spatiotemporal Analysis of Web News Archives for Crime Prediction

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…ere are several image classification tasks performed using D-CNN [11,[104][105][106][107][108][109]. One of the vital image classification tasks is handwritten digit recognition which recognizes numbers between 0 and 9, where the data from the MNIST database are obtained to predict the correct label for the handwritten digits.…”

Section: Image Classification Using the D-cnnmentioning

confidence: 99%

Deep CNN and Deep GAN in Computational Visual Perception‐Driven Image Analysis

et al. 2021

View full text Add to dashboard Cite

Computational visual perception, also known as computer vision, is a field of artificial intelligence that enables computers to process digital images and videos in a similar way as biological vision does. It involves methods to be developed to replicate the capabilities of biological vision. The computer vision’s goal is to surpass the capabilities of biological vision in extracting useful information from visual data. The massive data generated today is one of the driving factors for the tremendous growth of computer vision. This survey incorporates an overview of existing applications of deep learning in computational visual perception. The survey explores various deep learning techniques adapted to solve computer vision problems using deep convolutional neural networks and deep generative adversarial networks. The pitfalls of deep learning and their solutions are briefly discussed. The solutions discussed were dropout and augmentation. The results show that there is a significant improvement in the accuracy using dropout and data augmentation. Deep convolutional neural networks’ applications, namely, image classification, localization and detection, document analysis, and speech recognition, are discussed in detail. In-depth analysis of deep generative adversarial network applications, namely, image-to-image translation, image denoising, face aging, and facial attribute editing, is done. The deep generative adversarial network is unsupervised learning, but adding a certain number of labels in practical applications can improve its generating ability. However, it is challenging to acquire many data labels, but a small number of data labels can be acquired. Therefore, combining semisupervised learning and generative adversarial networks is one of the future directions. This article surveys the recent developments in this direction and provides a critical review of the related significant aspects, investigates the current opportunities and future challenges in all the emerging domains, and discusses the current opportunities in many emerging fields such as handwriting recognition, semantic mapping, webcam-based eye trackers, lumen center detection, query-by-string word, intermittently closed and open lakes and lagoons, and landslides.

show abstract

“…HSI has been used in remote sensing [26][27][28], and the medical and food industries [29,30]. HSI is also being used for minced and whole meat-type classification [14,17,18,31,32].…”

Section: Species Beef Buffalo Sheep Goat Chickenmentioning

confidence: 99%

Myoglobin-Based Classification of Minced Meat Using Hyperspectral Imaging

et al. 2020

Self Cite

View full text Add to dashboard Cite

Minced meat substitution is one of the most common frauds which not only affects consumer health but impacts their lifestyles and religious customs as well. A number of methods have been proposed to overcome these frauds; however, these mostly rely on laboratory measures and are often subject to human error. Therefore, this study proposes novel hyperspectral imaging (400–1000 nm) based non-destructive isos-bestic myoglobin (Mb) spectral features for minced meat classification. A total of 60 minced meat spectral cubes were pre-processed using true-color image formulation to extract regions of interest, which were further normalized using the Savitzky–Golay filtering technique. The proposed pipeline outperformed several state-of-the-art methods by achieving an average accuracy of 88.88%.

show abstract

Multiclass Non-Randomized Spectral–Spatial Active Learning for Hyperspectral Image Classification

Cited by 20 publications

References 41 publications

Spatiotemporal Analysis of Web News Archives for Crime Prediction

Spatiotemporal Analysis of Web News Archives for Crime Prediction

Deep CNN and Deep GAN in Computational Visual Perception‐Driven Image Analysis

Myoglobin-Based Classification of Minced Meat Using Hyperspectral Imaging

Contact Info

Product

Resources

About