Land Use Classification in Remote Sensing Images by Convolutional Neural Networks
This work addresses land use classification for remote sensing applications, but it is incremental as it adapts existing architectures to this domain.
The paper tackled land use classification in remote sensing images by applying convolutional neural networks (CaffeNet and GoogLeNet) with different training modalities, achieving significant performance improvements over state-of-the-art methods on two datasets.
We explore the use of convolutional neural networks for the semantic classification of remote sensing scenes. Two recently proposed architectures, CaffeNet and GoogLeNet, are adopted, with three different learning modalities. Besides conventional training from scratch, we resort to pre-trained networks that are only fine-tuned on the target data, so as to avoid overfitting problems and reduce design time. Experiments on two remote sensing datasets, with markedly different characteristics, testify on the effectiveness and wide applicability of the proposed solution, which guarantees a significant performance improvement over all state-of-the-art references.