Query by Example in Remote Sensing Image Archive Using Enhanced Deep Support Vector Data Description

Coastline Extraction Using Support Vector Machine from Remote Sensing Image

In recent years, support vector machine (SVM) has been widely applied in remote sensing image classification, since its experience can also minimize errors and maximize the geometric characteristics of the edge area. In this article SVM classification algorithm will be introduced the remote sensing extraction coastline. Fujian Province Landsat7 ETM + image will be a test region to be classified...

Adaptive Single Image Superresolution Approach Using Support Vector Data Description

An adaptive single image superresolution (SR) method using a support vector data description (SVDD) is presented. The proposed method represents the prior on high-resolution (HR) images by hyperspheres of the SVDD obtained from training examples and reconstructs HR images from low-resolution (LR) observations based on the following schemes. First, in order to perform accurate reconstruction of ...

Integration of remote sensing and meteorological data to predict flooding time using deep learning algorithm

Accurate flood forecasting is a vital need to reduce its risks. Due to the complicated structure of flood and river flow, it is somehow difficult to solve this problem. Artificial neural networks, such as frequent neural networks, offer good performance in time series data. In recent years, the use of Long Short Term Memory networks hase attracted much attention due to the faults of frequent ne...

Support Vector Machines for Remote-Sensing Image Classification

In the last decade, the application of statistical and neural network classifiers to remote-sensing images has been deeply investigated. Therefore, performances, characteristics, and pros and cons of such classifiers are quite well known, even from remote-sensing practitioners. In this paper, we present the application to remote-sensing image classification of a new pattern recognition techniqu...

Topic Distillation using Support Vector Data Description