Pairwise Comparison Network for Remote Sensing Scene Classification

Adaptive Deep Pyramid Matching for Remote Sensing Scene Classification

Convolutional neural networks (CNNs) have attracted increasing attention in the remote sensing community. Most CNNs only take the last fully-connected layers as features for the classification of remotely sensed images, discarding the other convolutional layer features which may also be helpful for classification purposes. In this paper, we propose a new adaptive deep pyramid matching (ADPM) mo...

A Survey on Remote Sensing Scene Classification Algorithms

Scene classification has been widely utilized in various remote sensing applications. Successful image classification depends on several factors, such as availability of data, complexity of available data, availability of ancillary data, expertise of an analyst, availability of suitable classification algorithms, etc. There is no single best classification method that would be suitable for all ...

A Knowledge-integrated Rbf Network for Remote Sensing Classification

Most Artificial neural networks (ANN) models used in the remote sensing classification are based on the multilayer perceptron (MLP) with back-propagation (BP) training algorithm. Compared to conventional statistical classifiers, MLP classifiers are non-parametric and distribution-free and is thus less restrictive in approximation, especially when distributions of features are strongly non-Gauss...

Comparison and Combination of Statistical and Neural Network Algorithms for Remote-sensing Image Classification

In recent years, the remote-sensing community has became very interested in applying neural networks to image classi cation and in comparing neural networks performances with the ones of classical statistical methods. These experimental comparisons pointed out that no single classi cation algorithm can be regarded as a \panacea". The superiority of one algorithm over the other strongly depends ...

Remote Sensing Image Classification