Remote Sensing Image Retrieval Using Convolutional Neural Network Features and Weighted Distance

Remote sensing image retrieval (RSIR) may be a fundamental task in remote sensing. Most content-based image retrieval (CBRSIR) approaches take an easy distance as similarity criteria. A retrieval method supported weighted distance and basic features of Convolutional Neural Network (CNN) is proposed during this letter. the strategy contains two stages. First, in offline stage, the pretrained CNN will be fine-tuned by some labelled images from our target data set, then accustomed extract CNN features, and labelled the pictures within the retrieval data set. Second, in online stage, we extract features of the query image by using fine-tuned CNN model and calculate the load of every image class and apply them to calculate the space between the query image and also the retrieved images. Experiments and methods are conducted on two Remote Sensing Image Retrieval data sets. Compared with the state-of the-art methods, the proposed method significantly improves retrieval performance.

Download Full-text

Remote Sensing Image Retrieval Using Convolutional Neural Network Features and Weighted Distance

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2018.2847303 ◽

2018 ◽

Vol 15 (10) ◽

pp. 1535-1539 ◽

Cited By ~ 22

Author(s):

Famao Ye ◽

Hui Xiao ◽

Xuqing Zhao ◽

Meng Dong ◽

Wei Luo ◽

...

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Image Retrieval ◽

Convolutional Neural Network ◽

Remote Sensing Image ◽

Weighted Distance

Download Full-text

Unsupervised Representation High-Resolution Remote Sensing Image Scene Classification via Contrastive Learning Convolutional Neural Network

Photogrammetric Engineering & Remote Sensing ◽

10.14358/pers.87.8.577 ◽

2021 ◽

Vol 87 (8) ◽

pp. 577-591

Author(s):

Fengpeng Li ◽

Jiabao Li ◽

Wei Han ◽

Ruyi Feng ◽

Lizhe Wang

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Deep Learning ◽

High Resolution ◽

Convolutional Neural Network ◽

State Of The Art ◽

Remote Sensing Image ◽

Scene Classification ◽

Data Set ◽

Unsupervised Deep Learning

Inspired by the outstanding achievement of deep learning, supervised deep learning representation methods for high-spatial-resolution remote sensing image scene classification obtained state-of-the-art performance. However, supervised deep learning representation methods need a considerable amount of labeled data to capture class-specific features, limiting the application of deep learning-based methods while there are a few labeled training samples. An unsupervised deep learning representation, high-resolution remote sensing image scene classification method is proposed in this work to address this issue. The proposed method, called contrastive learning, narrows the distance between positive views: color channels belonging to the same images widens the gaps between negative view pairs consisting of color channels from different images to obtain class-specific data representations of the input data without any supervised information. The classifier uses extracted features by the convolutional neural network (CNN)-based feature extractor with labeled information of training data to set space of each category and then, using linear regression, makes predictions in the testing procedure. Comparing with existing unsupervised deep learning representation high-resolution remote sensing image scene classification methods, contrastive learning CNN achieves state-of-the-art performance on three different scale benchmark data sets: small scale RSSCN7 data set, midscale aerial image data set, and large-scale NWPU-RESISC45 data set.

Download Full-text