With the rapid growth of remote sensing image data, it has become necessary to effectively and efficiently retrieve images from a big image database for managing and exploiting such data. This paper presents a novel method for content-based remote sensing image retrieval (CBRSIR) that re-ranks the initial retrieval result using two image-to-class distances, which are the similarity between an image and an image class. One is the image-to-training-class distance between an image and each image class of the training dataset, which uses the information included in the train samples with known-label. It is used to calculate the weight of the image classes and estimating the class of an image. The other is the image-to-query-class distance between an image and the query class that is obtained using the k-nearest neighbor (kNN) method. The proposed method first obtains the initial retrieval results via sorting the distances of the CNN feature between the query image and each retrieved image in ascending order. Then, the image-to-query-class distance is calculated according to the initial result, and the weight of each class is calculated according to the class probability of images obtained by CNN and the image-to-training-class distances. Finally, the initial result is re-ranked to get the final retrieval result via the weighted image-to-class distances. The performance of the proposed method is tested on UCMD and PatternNet databases. Experimental results show the effectiveness of the proposed re-ranking scheme in image retrieval in comparison to other state-of-the-art techniques.

Water body segmentation utilizing Synthetic Aperture Radar (SAR) images plays a crucial role in water resource and flood monitoring. However, existing SAR water segmentation methods often underutilize the information present in the multiple bands of SAR images. Moreover, U-shaped semantic segmentation algorithms based on convolutional neural networks (CNN) often suffer from the loss of valuable feature information and the neglect of global and local correlations caused by the inclusion of pooling layers. To address these challenges, this letter first generates two novel bands from the VV and VH bands of the SAR images and combines them with the VV band to create a new SAR image as the input for the model. Then, to enhance the model’s capacity for feature learning and minimize the risk of overlooking small target water bodies, the letter proposed a water body segmentation model for SAR images that improves upon the UNet model by integrating coordinate attention mechanisms, CBAM and PVTv2. Finally, to tackle the issue of high computational complexity associated with the model, traditional convolutions are substituted with depthwise separable convolutions. The experimental results demonstrate that the proposed method achieves a 1.68% increase in accuracy, a 4.21% increase in MIOU, and a 3.51% increase in F-score when compared to the widely adopted UNet algorithm. Consequently, the proposed model surpasses other algorithms in the domain of water segmentation.

Many image features have been proposed for image retrieval; hence, effectively fusing these features to alleviate the large variation in performance among image queries when using single image features has become a major challenge in remote sensing (RS) image retrieval. Because high-resolution remote sensing images have abundant and complex visual contents, accurately measuring the similarity between two images is another important problem. To address these challenges, we propose a novel RS image retrieval method that uses query-adaptive feature weights to fuse features and utilizes two image similarities to improve retrieval performance. First, we use the image rank similarity, which measures the similarity between two images according to their corresponding top-m image lists from a reference image collection, to calculate the similarity of each feature between a query image and each retrieved image. Then, we assign a weight to each feature to fuse these features via our query-adaptive weighting method. Finally, we take the query image and its neighborhood set selected from the retrieval dataset as the query class and utilize the image-to-query class similarity to re-rank the retrieval results. Extensive experiments are conducted on two publicly available RS image databases. Compared with the state-of-the-art methods, the proposed method can significantly enhance the retrieval precision.

For high-resolution remote sensing image retrieval tasks, single-scale features cannot fully express the complexity of the image information. Due to the large volume of remote sensing images, retrieval requires extensive memory and time. Hence, the problem of how to organically fuse multi-scale features and enhance retrieval efficiency is yet to be resolved. We propose an end-to-end deep hash remote sensing image retrieval model (PVTA_MSF) by fusing multi-scale features based on the Pyramid Vision Transformer network (PVTv2). We construct the multi-scale feature fusion module (MSF) by using a global attention mechanism and a multi-head self-attention mechanism to reduce background interference and enhance the representation capability of image features. Deformable convolution is introduced to address the challenge posed by varying target orientations. Moreover, an intra-class similarity (ICS) loss is proposed to enhance the discriminative capability of the hash feature by minimizing the distance among images of the same category. The experimental results show that, compared with other state-of-the-art methods, the proposed hash feature could yield an excellent representation of remote sensing images and improve remote sensing image retrieval accuracy. The proposed hash feature can gain an increase of 4.2% and 1.6% in terms of mAP on the UC Merced and NWPU-RESISC45 datasets, respectively, in comparison with other methods.

High-resolution remote sensing (HRRS) images contain complex visual contents and rich detailed information. This paper proposes a multi-level feature fusion method to improve the feature representation for HRRS image retrieval. Firstly, in order to obtain the multi-scale information of HRRS images, mid-level features and high-level features of VGG16 and GoogLeNet are extracted with different input sizes. Then a feature transformation method is proposed to adjust the size and the number of different feature maps, so that the distinct mid-level features and distinct high-level features can be fused separately using element-wise addition. There is a large amount of redundancy in the fusion features, thus small-region max-pooling method is adopted to aggregate the mid-level fusion feature, and global max-pooling method is used to aggregate the high-level fusion feature. Finally, an adaptive weight allocation method based on similarity is proposed to combine mid-level feature and high-level feature. Experimental results show that the multi-level feature fusion is an effective method to enhance the feature representation, thereby improving the retrieval performance.

The methods in remote sensing image retrieval (RSIR) usually search the whole retrieval data set in the retrieval process, which takes much time and is unnecessary. To reduce the overall search time, this letter proposes a new retrieval scheme based on fuzzy rules. The proposed method calculates the fuzzy class membership of images using two ways. The first way predicts the fuzzy class membership by convolutional neural network (CNN). The other uses the image-to-class distance that is a distance between an image and each class on the training data set. The two fuzzy class memberships are used to measure the classification confidence, and a query image is classified into three fuzzy sets, namely, ``low classification confidence,'' ``medium classification confidence,'' and ``high classification confidence,'' based on the classification confidence. The fuzzy rules are built according to fuzzy classification to choose the search space for each fuzzy set. The final search space is determined by the two search spaces obtained by fuzzy rules. Moreover, the fuzzy distance between a query image and a retrieved image is used to improve the retrieval performance, which is calculated according to their fuzzy class memberships and the Euclidean distance between the two images. The experimental results on University of California, Merced data set (UCMD) and PatternNet databases show that our proposed method can not only enhance the retrieval performance but also reduce the search time in comparison to other state-of-the-art techniques.