Therefore, a combination of the most effective features was used to achieve a recall accuracy of 98.272%. The results of the study showed that the statistical moments of the color spaces contribute positively to the classification accuracy while some of the gray level co-occurrence matrix (GLCM) features contribute negatively to the classification accuracy. This method aims to distinguish between high- and low-density vegetation regions along the power line corridor right-of-way (ROW). In this paper, we describe the statistical moments of the color spaces and the textural features of the satellite imagery to identify the most effective features that can increase the vegetation density classification accuracy of the support vector machine (SVM) algorithm. Alternatively, satellite imagery can cover a wide area at a relatively lower cost. However, they are expensive with regard to the coverage area. These methods are very effective in detecting vegetation encroachment. Many technologies have been used to detect vegetation encroachment, such as light detection and ranging (LiDAR), synthetic aperture radar (SAR), and airborne photogrammetry. Vegetation encroachment along electric power transmission lines is one of the major environmental challenges that can cause power interruption. The additional information is that training using Theano framework is faster than Tensorflow for both in GTX-980 and Tesla K40c According to the study, Deep Neural Network outperforms other classifiers with the highest accuracy and deviation standard 96.72☐.48 for four cross-validations. For training, we use two hardware: NVIDIA GPU GTX-980 and TESLA K40c. The proposed GLCM method is then trained using Deep Neural Networks (DNN) and compared to other classification techniques for benchmarking. The mean-shift filter is a low-pass filter technique that considers the surrounding pixels of the images. We use texture feature Gray Level Co-Occurrence Matrix (GLCM) with a meanshift filter as the data pre-processing of the images.
MULTIPATCH ROM WINDOWS
This study proposed advance texture extraction by multi-patch images pixel method with sliding windows that minimize loss of information in each pixel patch.
MULTIPATCH ROM FULL
The use of full high-resolution histopathology images will take a longer time for the extraction of all information due to the huge amount of data.
The status of cancer with histopathology images can be classified based on the shape, morphology, intensity, and texture of the image. Hematoxylin and Eosin (H&E) images are the most common modalities used by the pathologist for cancer detection. Not only for the pathologist but also from the view of a computer scientist. The main reason why research in this field becomes challenging. Cancer is one of the leading causes of death in the world.
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