CS6005 - DEEP LEARNING TECHNIQUES COURSE PROJECT
In the classification of remote sensing scenes, Convolutional Neural Networks (CNNs) have out-standing advantages. Deep CNN models with better classification performance typically have high complexity, whereas shallow CNN models with low complexity rarely achieve good classification performance for remote sensing images with complex spatial structures.
A new lightweight CNN classification method based on branch feature fusion (LCNN-BFF) for remote sensing scene classification can be used for attaining these results. In contrast to a conventional single linear convolution structure, this model has a bilinear feature extraction structure. The BFF method is used to fuse the feature information extracted from the two branches, which improved the classification accuracy. In addition, combining depth wise separable convolution and conventional convolution to extract image features greatly reduced the complexity of the model on the premise of ensuring the accuracy of classification.
The experimental results showed that, compared with recent classification methods, the number of weight parameters of the proposed method only accounted for less than 5% of the other methods; however, the classification accuracy was equivalent to or even superior to certain high-performance classification methods.
Dataset
NWPU-RESISC45 dataset is a dataset for Remote Sensing Image Scene Classification (RESISC). It contains 31,500 RGB images of size 256×256 divided into 45 scene classes, each class containing 700 images. Among its notable features, RESISC45 contains varying spatial resolution ranging from 20cm to more than 30m/px.
Presented by
Ajitesh M 2019103503
Vishnupriya N 2019103599
Reference (Base paper)
C. Shi, T. Wang and L. Wang, "Branch Feature Fusion Convolution Network for Remote Sensing Scene Classification," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 5194-5210, 2020, doi: 10.1109/JSTARS.2020.3018307.