Autonomous building detection using region properties and PCA

This paper proposes an algorithm for autonomous building detection in remote sensing images. The basis of the algorithm relies on the fact that each channel in RGB color space conveys different information. Furthermore, region properties and Principal Component Analysis (PCA) are used to distinguish between buildings and other regions in order to reduce false positive cases. The images used to test the proposed algorithm were obtained from DubaiSat-2, which offers multispectral images with 1-m accuracy

Skin cancer classification model based on VGG 19 and transfer learning

Skin cancer is a concerning health issue with yearly increasing numbers. Detecting and classifying cancer type is problematic, especially since patients have to undergo several diagnosis over lengthy periods of time, which hinders early treatment and survival chances. With the aid of digital image processing, features can be extracted to identify skin cancer and its different types. Convolutional Neural Networks (CNNs) recently emerged as powerful autonomous feature extractors, and they have high potential to achieve high accuracy with skin cancer diagnosis. In this paper, two cancer types in addition to one non-cancer type taken from Human Against Machine (HAM10000) dataset are classified using CNN model based on VGG 19 and Transfer Learning technique. The training strategy is explained, tested, and evaluated by calculating the network's overall accuracy and loss

Protection and authentication of Dubai digital elevation model using hybrid watermarking technique

Nowadays, with the availability of digital images and models at no cost on the World Wide Web, the need to provide copyright protection of multimedia data arises. Hence, digital watermarking products have been in high demand. Digital watermarking essentially embeds information into data in such a way that data usage is not affected, and it simultaneously protects and authenticates the data. This research paper deals with the development and evaluation of a watermarking technique for protection and authentication of Dubai Digital Elevation Model (DEM) provided by United States Geological Survey (USGS). The technique uses a hybrid combination of Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT), and it is implemented for the protection of DEM by embedding the ownership information in hybrid DCT-DWT domain and for checking the integrity of the elevation model by embedding hash-key information in the spatial domain. The proposed watermarking technique causes minimal distortion to the DEM and the performance is assessed by using Peak Singal-to-Noise Ratio (PSNR), Wavelet Signal-to-Noise Ratio (WSNR), and Structural Similarity Index Measurement (SSIM). The results show promising performance with strong robustness of watermark information ownership for many intentional and non-intentional attacks, in addition to precise detection of localized modified areas on tampered DEM

A hybrid rexception network for COVID-19 classification from chest X-ray images

Nowadays, with the rapid spread of Coronavirus disease (COVID-19) across the globe, the necessity to develop an intelligent system for early diagnosis and detection the COVID-19 infectious disease increases. In recent researches, Chest Xray (CXR) of individual lungs became a common method to identify COVID-19 virus. Manual interpretation of the CXR images can be a lengthy process and subjective to human errors. In this paper, a hybrid Deep Learning model called ReXception is implemented, trained, and evaluated using two types of datasets; Mutliclass and Binary. The network is evaluated based on its overall accuracy, loss, precision, and recall, in addition to the running time and network size. The results show positive indications of the network's performance, especially when compared to other state-of-the-art networks

Explainable AI for Soil Fertility Prediction

Soil fertility refers to the ability of soil in a particular area to provide favorable chemical, physical and biological characteristics that help the plant in its growth. It is affected by multiple parameters, from the available concentration of Nitrogen in the soil to the concentration of Organic Carbon in the soil. This paper discusses the implementation of an explainable AI (XAI) model based on a Random Forest classifier. The developed model reliably predicts the relative soil fertility of a given soil using its various physiochemical properties, and explain the reasons behind the model’s soil fertility indicator prediction using user friendly graphs. The model shows 97.02% accuracy in comparison with state-of-the-art machine learning models. The paper also discusses applications of developed model in providing possible solutions to further improve upon soil fertility in the short term and long term

Airbus ship detection from satellite imagery using frequency domain learning

Ship detection from remote sensing images has been a topic of interest that gradually gained attention over the years due to the wide variety of its applications in the field of maritime surveillance, such as oil discharge control, sea pollution monitoring, and harbour management. Even though there is an extensive amount of methods developed for ship detection, there are still several challenges that remain unsolved, especially in complex environments. These challenges include occlusions due to shadows, clouds, and fog. Nowadays, deep learning algorithms, especially Deep Convolutional Neural Networks (DCNNs), are considered as a powerful approach for automatically detecting ships in satellite imagery. In this paper, enhanced Faster R-CNN (FRCNN) model will be used to overcome the aforementioned unsolved challenges. The enhanced FRCNN, which combines high level features with low level features, will be trained and tested in the frequency domain using the publicly available satellite imagery dataset, Airbus Ship Detection, provided by Kaggle. The performance will be compared to the original FRCNN based on their Overall Accuracy (OA) and Mean Average Precision (mAP) metrics