Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing

Deep Convolutional Neural Network Ensembles Using ECOC

Deep neural networks have enhanced the performance of decision making systems in many applications, including image understanding, and further gains can be achieved by constructing ensembles. However, designing an ensemble of deep networks is often not very beneficial since the time needed to train the networks is generally very high or the performance gain obtained is not very significant. In this paper, we analyse an error correcting output coding (ECOC) framework for constructing ensembles of deep networks and propose different design strategies to address the accuracy-complexity trade-off. We carry out an extensive comparative study between the introduced ECOC designs and the state-of-the-art ensemble techniques such as ensemble averaging and gradient boosting decision trees. Furthermore, we propose a fusion technique, that is shown to achieve the highest classification performance

Machine Learning based Classification of Diseased Mango Leaves

The preponderance of population depends on agriculture to produce crops which would be their primary subsistence for their livelihood. So, agriculture is considered the backbone of any nation. Mango (Mangifera indica Linn), belonging to a family Anacardiaceous, is a conspicuous fruit that captivates all ages because of its meticulous taste, delicious flavor, ampleness variety, and highly lustiness. Mangoes are generally rich in minerals, vitamins, fibers, and negotiable fat. Mango plants are exposed to many micro-organisms. If these are not detected and treated in the initial developing stages, it would affect peculiar parts of the mango plant and result in loss of overall productivity. Several factors like biotic and abiotic always ensue in the decrease in the overall productivity of mango plants. Self-regulated Detection of mango plant disease is imperative, and it must be detected at the preliminary stages of the growing period of the mango plant. This paper discusses the existing methodology to classify diseases in mango plant leaves by implementing ensemble technique (Stack) which includes algorithms like Decision Tree (DT), Support vector machine (SVM), Neural Network (NN), and Logistic Regression (LR). The developmental results validate that the disease classification methodology can successfully classify a higher percentage in predicting whether mango plant leaf is healthy or diseased.&nbsp