Remote Sensing Image Scene Classification with Noisy Label Distillation

Remote Sensing Image Classification

Remote Sensing Image Classification using Back Propogation

The resolution of remote sensing images increase every day . Most of the existing methods is used the same method for years. The existing method does not provide satisfactory result. The aim is to develop an artificial neural network based on classification method consists of segmentation and classification . Segmentation followed by K-Means method and then classification performed with back pr...

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 ...

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...

Remote Sensing Image Scene Classification Using Multi-Scale Completed Local Binary Patterns and Fisher Vectors

An effective remote sensing image scene classification approach using patch-based multi-scale completed local binary pattern (MS-CLBP) features and a Fisher vector (FV) is proposed. The approach extracts a set of local patch descriptors by partitioning an image and its multi-scale versions into dense patches and using the CLBP descriptor to characterize local rotation invariant texture informat...