PEMISAHAN ANOMALI GAYABERAT DAERAH LAMPUNG MENGGUNAKAN BIDIMENSIONAL EMPIRICAL MODE DECOMPOSITION (BEMD)

Metode gayaberat adalah salah satu metode geofisika yang digunakan dalam eksplorasi mineral dan migas. Metode ini memanfaatkan percepatan gravitasi untuk memodelkan struktur densitas batuan di dalam bumi, mendeliniasi struktur maupun satuan geologi. Pada tahap pengolahan data gayaberat diperlukan beberapa koreksi untuk menghasilkan anomali Bouguer lengkap (CBA). Nilai CBA merupakan hasil resultan dari seluruh kontribusi massa di bawah permukaan dan di sekitar titik datum. Pemisahan anomali CBA menjadi regional dan residual menjadi tahap penting dalam interpretasi dan pemodelan gayaberat. Beberapa metode pemisahan anomali yang ada saat ini sudah menunjukkan hasil yang baik. Pada prinsipnya, metode tersebut menggunakan teknik fitting permukaan, pemfilteran frekuensi, smoothing pada domain spasial, atau kontinyuasi medan. Namun, metode tersebut memiliki aspek subjektif yang tinggi dalam menentukan parameter yang bekerja. Pada paper ini, saya mengaplikasikan metode alternatif yaitu Bidimensional Empirical Mode Decomposition (BEMD) pada daerah Lampung. BEMD menganalisis data secara algoritmik dan mampu mendekomposisi data secara empirik yang dapat diasosiasikan dengan pemisahan anomali pada metode gayaberat. Keuntungan utama metode ini adalah kemampuannya untuk memberikan pemisahan yang hampir sempurna antar anomali yang terdapat dalam data gayaberat. Metode ini mampu secara langsung menunjukkan anomali yang dicari sehingga pemilihan anomali target dapat dilakukan dengan mudah karena jumlah dekomposisinya yang tidak banyak.

Target Recognition of SAR Images Based on Complex Bidimensional Empirical Mode Decomposition

A target recognition method for synthetic aperture radar (SAR) image based on complex bidimensional empirical mode decomposition (C-BEMD) is proposed. C-BEMD is used to decompose the original SAR image to obtain multilevel complex bidimensional intrinsic mode functions (BIMF), which reflect the two-dimensional time-frequency characteristics of the target. In the classification stage, the decomposed multilevel BIMFs are represented using the multitask sparse representation. Finally, the target category of the test sample is determined according to the reconstruction errors related to different training classes. In the experiment, the standard operating condition (SOC) and extended operating conditions (EOC) are designed based on the MSTAR dataset to test and verify the proposed method. The results confirm the effectiveness and robustness of the method.

Hyperspectral image classification using support vector machines

In this paper, a novel approach for hyperspectral image classification technique is presented using principal component analysis (PCA), bidimensional empirical mode decomposition (BEMD) and support vector machines (SVM). In this process, using PCA feature extraction technique on Hyperspectral Dataset, the first principal component is extracted. This component is supplied as input to BEMD algorithm, which divides the component into four parts, the first three parts represents intrensic mode functions (IMF) and last part shows the residue. These BIMFs and residue image is further taken as input to the SVM for classification. The results of experiments on two popular datasets of hyperspectral remote sensing scenes represent that the proposed-model offers a competitive analyticalperformance in comparison to some established methods.

Pipeline for Advanced Contrast Enhancement (PACE) of Chest X-ray in Evaluating COVID-19 Patients by Combining Bidimensional Empirical Mode Decomposition and Contrast Limited Adaptive Histogram Equalization (CLAHE)

COVID-19 is a new pulmonary disease which is driving stress to the hospitals due to the large number of cases worldwide. Imaging of lungs can play a key role in the monitoring of health status. Non-contrast chest computed tomography (CT) has been used for this purpose, mainly in China, with significant success. However, this approach cannot be massively used, mainly for both high risk and cost, also in some countries, this tool is not extensively available. Alternatively, chest X-ray, although less sensitive than CT-scan, can provide important information about the evolution of pulmonary involvement during the disease; this aspect is very important to verify the response of a patient to treatments. Here, we show how to improve the sensitivity of chest X-ray via a nonlinear post-processing tool, named PACE (Pipeline for Advanced Contrast Enhancement), combining properly Fast and Adaptive Bidimensional Empirical Mode Decomposition (FABEMD) and Contrast Limited Adaptive Histogram Equalization (CLAHE). The results show an enhancement of the image contrast as confirmed by three widely used metrics: (i) contrast improvement index, (ii) entropy, and (iii) measure of enhancement. This improvement gives rise to a detectability of more lung lesions as identified by two radiologists, who evaluated the images separately, and confirmed by CT-scans. The results show this method is a flexible and an effective approach for medical image enhancement and can be used as a post-processing tool for medical image understanding and analysis.