Telecommunication

Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/3080

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Author: search.filters.author.Daamouche, AbdelhamidStart date: 2020

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    Hyperspectral image classification using principle component analysis and convelutional neural networks
    (2021) Akochiah, Sara; Ameur, Sara; Daamouche, Abdelhamid
    Hyperspectral image (HSI) classification is a hot topic in the field of remote sensing data analysis due to the vast amount of information comprised by this type of images.The highest dimensionality of HSI enhances the computational complexity which affects the overall performance. Hence, the dimensionality reduction plays a vital role to enhance the performance while processing the Hyperspectral images. A Dimensionality reduction technique is proposed by this work as a first approach. This technique is applied using Principle Component analysis (PCA), which extract informative features suitable for data representation and classification. This work was condacted to determine and evaluate the performance of two different methods used for classification of three datasets: Indian Pines dataset, Pavia University dataset and the Salinas dataset. These methods are separated as a traditional Machine Learning based on: SVM with two kernels and a Deep Learning based on: U-Net and a pretrained Tansfer Learning with U-Net. The proposed approach is tested on the three datasetsts, The performance analysis results on the Deep Learning techniques have improved accuracy and performance compared to the Machine Learning techniques. Keywords : Hyperspectral Images, Dimensionality Reduction Technique, PCA, Traditional Machine Learning, Deep Learning, Transfer Learning, SVM, U-Net.
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    Hyperspectral image classification using deep neural networks
    (2021) Affoun, Nedjm Eddine; Daamouche, Abdelhamid
    Hyperspectral imaging technology has become of high importance nowadays because of the valuable information it provides. Its ability to acquire images with hundreds of adjacent narrow bands in addition to the spatial information made it possible for experts to analyze the target objects more efficiently, and led to an enormous development especially in the remote sensing field which is the focus of this work. Because of the enormous demand on the hyperspectral imaging in various fields, it became a necessity to provide useful tools in order to completely exploit this technology. The aim of this work is to propose some methods that would help reaching this goal by extracting the features from the hyperspectral data needed to identify and classify all the pixels contained in the image. The proposed methods are based on the spectral and spatial information contained in the hyperspectral image. The first method is concerned with the spectral information alone. The second exploits the spatial information alone. Whereas, the last combines both the spectral and spatial information. The technique used to implement the proposed methods is deep learning, which is a powerful system of artificial neural networks that mimics the human brain. Deep neural networks can pass the input data through a bunch of complicated operations in order to extract the most important features and classify the data accordingly.
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    Building detection from high resolution remote sensing imagery
    (2020) Bentaala, Ali; Boulebnane, Lokman; Daamouche, Abdelhamid
    Building detection is an important task in very high-resolution remote sensing image analysis. In recent years, availability of very high-resolution images raised new challenges to building detection algorithms. In this report, we use a supervised method to detect buildings from remotely sensed images using spectral-spatial features. The morphological operations (MO), gray level co-occurrence matrix (GLCM) and Variogram techniques are used to extract the spatial features. We concatenated spatial features and spectral features, and then we fed the Support Vector Machines (SVM) classifier with the resulting vector of features. We classified the image data into two classes (Building and Non-Building) using different combinations of features. The simulation results obtained on three different images showed that our approach achieved an acceptable performance in terms of accuracy.