Abstract
Effective feature representations play an important role in remote sensing image analysis tasks. With the rapid progress of deep learning techniques, deep features have been widely applied to remote sensing image understanding in recent years and shown powerful ability in image representation. The existing deep feature extraction approaches are usually carried out on the whole image directly. However, such deep feature representation strategies may not effectively capture the local geometric invariance of target regions in remote sensing images. In this paper, we propose a novel region-wise deep feature extraction framework for remote sensing images. First, regions that may contain the target information are extracted from one whole image. Then, these regions are fed into a pre-trained convolutional neural network (CNN) model to extract regional deep features. Finally, the regional deep features are encoded by an improved Vector of Locally Aggregated Descriptors (VLAD) algorithm to generate the feature representation for the image. We conducted extensive experiments on remote sensing image classification and retrieval tasks based on the proposed region-wise deep feature extraction framework. The comparison results show that the proposed approach is superior to the existing CNN feature extraction methods.
Highlights
With the developments of satellite imaging techniques, it is much easier to acquire a large collection of remote sensing images
In order to address the above problem, we propose a novel region-wise deep convolutional neural network (CNN) feature representation method for remote sensing image analysis, which extracts the CNN features from regions containing the targets instead of the whole image
CNN-W as well as state-of-the-art remote sensing image classification methods are used as benchmarks in the experiments
Summary
With the developments of satellite imaging techniques, it is much easier to acquire a large collection of remote sensing images. Feature extraction and representation is the foundation of many remote sensing image processing tasks [5,6,7,8,9]. A variety of feature learning methods for remote sensing images have been proposed. Remote sensing image analysis was mainly based on the hand-crafted features which include both global features and local features. In addition to the hand-crafted features, data-driven features are developed via unsupervised feature learning in terms of content-based remote sensing image retrieval and classification tasks [18,19,20,21,22]. A multiple feature-based remote sensing image retrieval approach was proposed in [21] by combining hand-crafted features and data-driven features via unsupervised feature learning. As the remote sensing image understanding task becomes more challenging, the description capabilities of the above low-level features are limited and may not be effective to capture the high-level semantics
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