Deep Object-Centric Pooling in Convolutional Neural Network for Remote Sensing Scene Classification

Abstract

Remote sensing imagery typically comprises successive background contexts and complex objects. Global average pooling is a popular choice to connect the convolutional and fully connected (FC) layers for the deep convolution network. This article equips the networks with another pooling strategy, namely the deep object-centric pooling (DOCP), to pool convolutional features considering the location of an object within the scene image. The proposed DOCP network structure consists of the following two steps: inferring object's location and separately pooling the foreground and background features to generate an object-level representation. Specifically, a spatial context module is presented to learn the location of the object of interest in the scene image. Then, the convolutional feature maps are pooled separately in the foreground and background of the object. Finally, the FC layer concatenates these pooled features and is followed by a batch normalization layer, a dropout layer, and a softmax layer. Two challenging datasets are employed to validate our approach. The experimental results demonstrate that the proposed DOCP-net can outperform the corresponding pooling methods and achieve a better classification performance than other pretrained convolutional neural network-based scene classification methods.