Deep Ensembles for Hyperspectral Image Data Classification and Unmixing

Hyperspectral images capture very detailed information about scanned objects and, hence, can be used to uncover various characteristics of the materials present in the analyzed scene. However, such image data are difficult to transfer due to their large volume, and generating new ground-truth datasets that could be utilized to train supervised learners is costly, time-consuming, very user-dependent, and often infeasible in practice. The research efforts have been focusing on developing algorithms for hyperspectral data classification and unmixing, which are two main tasks in the analysis chain of such imagery. Although in both of them, the deep learning techniques have bloomed as an extremely effective tool, designing the deep models that generalize well over the unseen data is a serious practical challenge in emerging applications. In this paper, we introduce the deep ensembles benefiting from different architectural advances of convolutional base models and suggest a new approach towards aggregating the outputs of base learners using a supervised fuser. Furthermore, we propose a model augmentation technique that allows us to synthesize new deep networks based on the original one by injecting Gaussian noise into the model’s weights. The experiments, performed for both hyperspectral data classification and unmixing, show that our deep ensembles outperform base spectral and spectral-spatial deep models and classical ensembles employing voting and averaging as a fusing scheme in both hyperspectral image analysis tasks.

Diagnostic Breast Cancer Image Data Classification using CNN

Breast cancer has been dangerous form of cancer. In this report, we use a convolutional neural network to scan and separate infected cells.In this we diagnose if its benign or malignant cancer bulk using computer assisted detection(CAD). The productivity of open CAD has always been inadequate. Here, we use a deep CNN-based content detection method.We create narrower and broader images of histology patches with cell and tumour attributes. CNN constitutes unorganized data specifically for image data which has been said to be thriving in the area of image recognition .We use highly interconnected layer first cnn, in which those layers are incorporated before the first convolutional layer, since CNN does not support data sets.

Medical Image Data Classification Using Deep Learning Based Hybrid Model with CNN and Encoder

Healthcare sector is one of the prime and different from other trade. Society expects high priority and highest level of services and care irrespective of money. Presently medical field suffers from accurate diagnosis of diseases and it create huge loss to society. The prime factor for this is due to the nature of medical data, it is a combination of all varieties of data. Medical image analysis is a key method of Computer-Aided Diagnosis (CAD) frameworks. Customary strategies depend predominantly on the shape, shading, and additionally surface highlights just as their mixes, a large portion of which are issue explicit and have demonstrated to be integral in medical images, which prompts a framework that does not have the capacity to make portrayals of significant level issue area ideas and that has poor model speculation capacity. In this paper we are attempting a medical image data classification technique using hybrid deep learning technique based on Convolutional Neural Network (CNN) and encodes. What's more, we assess the proposed approach on two benchmark clinical picture datasets: HIS2828 and ISIC2017. The proposed algorithm is applied on the considered 2 datasets for performing data classification using deep learning based CNN and encoders. The proposed model is compared with the traditional methods and the results show that proposed model classification accuracy is better than the existing models.

IDENTIFIKASI CITRA DAUN DENGAN MENGGUNAKAN METODE DEEP LEARNING CONVOLUTIONAL NEURAL NETWORK (CNN)

In the world of programming, deep learning methods of convolutional neural networks (CNN) may be very rarely heard, because this method was only popular around 2015, and this time I will conduct an experiment using this method, where I will conduct experiments on identification systems leaf image whose application is almost similar to a face recognition system, the leaf image identification system itself consists of detection and classification stages. Both of these stages are done so quickly by humans but it takes a long time for the computer. The application also uses MATLAB 2018a software with the CNN method we can find out the image data classification and can do the process of identifying images properly.

MACHINE LEARNING-BASED MANGROVE LAND CLASSIFICATION ON WORLDVIEW-2 SATELLITE IMAGE IN NUSA LEMBONGAN ISLAND

Machine learning is an empirical approach for regressions, clustering and/or classifying (supervised or unsupervised) on a non-linear system. This method is mainly used to analyze a complex system for wide data observation. In remote sensing, machine learning method could be used for image data classification with software tools independence. This research aims to classify the distribution, type, and area of mangroves using Akaike Information Criterion approach for case study in Nusa Lembongan Island. This study is important because mangrove forests have an important role ecologically, economically, and socially. For example is as a green belt for protection of coastline from storm and tsunami wave. Using satellite images Worldview-2 with data resolution of 0.46 meters, this method could identify automatically land class, sea class/water, and mangroves class. Three types of mangrove have been identified namely: Rhizophora apiculata, Sonnetaria alba, and other mangrove species. The result showed that the accuracy of classification was about 68.32%.