scholarly journals Cohesion Intensive Deep Hashing for Remote Sensing Image Retrieval

2019 ◽  
Vol 12 (1) ◽  
pp. 101 ◽  
Author(s):  
Lirong Han ◽  
Peng Li ◽  
Xiao Bai ◽  
Christos Grecos ◽  
Xiaoyu Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Retrieval ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Optimization Method ◽  
Remote Sensing Image ◽  
Model Parameters ◽  
Remote Sensing Images ◽  
Deep Hashing ◽  
Deep Model

Recently, the demand for remote sensing image retrieval is growing and attracting the interest of many researchers because of the increasing number of remote sensing images. Hashing, as a method of retrieving images, has been widely applied to remote sensing image retrieval. In order to improve hashing performance, we develop a cohesion intensive deep hashing model for remote sensing image retrieval. The underlying architecture of our deep model is motivated by the state-of-the-art residual net. Residual nets aim at avoiding gradient vanishing and gradient explosion when the net reaches a certain depth. However, different from the residual net which outputs multiple class-labels, we present a residual hash net that is terminated by a Heaviside-like function for binarizing remote sensing images. In this scenario, the representational power of the residual net architecture is exploited to establish an end-to-end deep hashing model. The residual hash net is trained subject to a weighted loss strategy that intensifies the cohesiveness of image hash codes within one class. This effectively addresses the data imbalance problem normally arising in remote sensing image retrieval tasks. Furthermore, we adopted a gradualness optimization method for obtaining optimal model parameters in order to favor accurate binary codes with little quantization error. We conduct comparative experiments on large-scale remote sensing data sets such as UCMerced and AID. The experimental results validate the hypothesis that our method improves the performance of current remote sensing image retrieval.

scholarly journals Distribution Consistency Loss for Large-Scale Remote Sensing Image Retrieval

2020 ◽  
Vol 12 (1) ◽  
pp. 175 ◽  
Author(s):  
Lili Fan ◽  
Hongwei Zhao ◽  
Haoyu Zhao
Keyword(s):  
Remote Sensing ◽  
Image Retrieval ◽  
Large Scale ◽  
Metric Learning ◽  
Remote Sensing Image ◽  
Positive Sample ◽  
Fine Tuning ◽  
Remote Sensing Images ◽  
Hard Samples ◽  
Image Datasets

Remote sensing images are featured by massiveness, diversity and complexity. These features put forward higher requirements for the speed and accuracy of remote sensing image retrieval. The extraction method plays a key role in retrieving remote sensing images. Deep metric learning (DML) captures the semantic similarity information between data points by learning embedding in vector space. However, due to the uneven distribution of sample data in remote sensing image datasets, the pair-based loss currently used in DML is not suitable. To improve this, we propose a novel distribution consistency loss to solve this problem. First, we define a new way to mine samples by selecting five in-class hard samples and five inter-class hard samples to form an informative set. This method can make the network extract more useful information in a short time. Secondly, in order to avoid inaccurate feature extraction due to sample imbalance, we assign dynamic weight to the positive samples according to the ratio of the number of hard samples and easy samples in the class, and name the loss caused by the positive sample as the sample balance loss. We combine the sample balance of the positive samples with the ranking consistency of the negative samples to form our distribution consistency loss. Finally, we built an end-to-end fine-tuning network suitable for remote sensing image retrieval. We display comprehensive experimental results drawing on three remote sensing image datasets that are publicly available and show that our method achieves the state-of-the-art performance.

scholarly journals A Semantic-Preserving Deep Hashing Model for Multi-Label Remote Sensing Image Retrieval

2021 ◽  
Vol 13 (24) ◽  
pp. 4965
Author(s):  
Qimin Cheng ◽  
Haiyan Huang ◽  
Lan Ye ◽  
Peng Fu ◽  
Deqiao Gan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Retrieval ◽  
Semantic Information ◽  
Nearest Neighbor ◽  
Remote Sensing Data ◽  
Semantic Content ◽  
Remote Sensing Image ◽  
Image Features ◽  
Practical Applications ◽  
Deep Hashing

Conventional remote sensing image retrieval (RSIR) systems perform single-label retrieval with a single label to represent the most dominant semantic content for an image. Improved spatial resolution dramatically boosts the remote sensing image scene complexity, as a remote sensing image always contains multiple categories of surface features. In this case, a single label cannot comprehensively describe the semantic content of a complex remote sensing image scene and therefore results in poor retrieval performance in practical applications. As a result, researchers have begun to pay attention to multi-label image retrieval. However, in the era of massive remote sensing data, how to increase retrieval efficiency and reduce feature storage while preserving semantic information remains unsolved. Considering the powerful capability of hashing learning in overcoming the curse of dimensionality caused by high-dimensional image representation in Approximate Nearest Neighbor (ANN) search problems, we propose a new semantic-preserving deep hashing model for multi-label remote sensing image retrieval. Our model consists of three main components: (1) a convolutional neural network to extract image features; (2) a hash layer to generate binary codes; (3) a new loss function to better maintain the multi-label semantic information of hash learning contained in context remote sensing image scene. As far as we know, this is the first attempt to apply deep hashing into the multi-label remote sensing image retrieval. Experimental results indicate the effectiveness and promising of the introduction of hashing methods in the multi-label remote sensing image retrieval.

Large-Scale Remote Sensing Image Retrieval by Deep Hashing Neural Networks

2018 ◽  
Vol 56 (2) ◽  
pp. 950-965 ◽  
Author(s):  
Yansheng Li ◽  
Yongjun Zhang ◽  
Xin Huang ◽  
Hu Zhu ◽  
Jiayi Ma
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Image Retrieval ◽  
Large Scale ◽  
Remote Sensing Image ◽  
Deep Hashing

MILL: Channel Attention–based Deep Multiple Instance Learning for Landslide Recognition

2021 ◽  
Vol 17 (2s) ◽  
pp. 1-11
Author(s):  
Xiaochuan Tang ◽  
Mingzhe Liu ◽  
Hao Zhong ◽  
Yuanzhen Ju ◽  
Weile Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Large Scale ◽  
Remote Sensing Image ◽  
Disaster Risk ◽  
Multiple Instance Learning ◽  
Remote Sensing Images ◽  
Loess Area ◽  
Remote Sensing Image Classification ◽  
Natural Disaster Risk

Landslide recognition is widely used in natural disaster risk management. Traditional landslide recognition is mainly conducted by geologists, which is accurate but inefficient. This article introduces multiple instance learning (MIL) to perform automatic landslide recognition. An end-to-end deep convolutional neural network is proposed, referred to as Multiple Instance Learning–based Landslide classification (MILL). First, MILL uses a large-scale remote sensing image classification dataset to build pre-train networks for landslide feature extraction. Second, MILL extracts instances and assign instance labels without pixel-level annotations. Third, MILL uses a new channel attention–based MIL pooling function to map instance-level labels to bag-level label. We apply MIL to detect landslides in a loess area. Experimental results demonstrate that MILL is effective in identifying landslides in remote sensing images.

scholarly journals Remote Sensing Image Retrieval with Gabor-CA-ResNet and Split-Based Deep Feature Transform Network

2021 ◽  
Vol 13 (5) ◽  
pp. 869
Author(s):  
Zheng Zhuo ◽  
Zhong Zhou
Keyword(s):  
Remote Sensing ◽  
Image Retrieval ◽  
State Of The Art ◽  
Remote Sensing Image ◽  
Storage Space ◽  
Remote Sensing Images ◽  
Retrieval Method ◽  
Organization Management ◽  
Deep Feature ◽  
Feature Transform

In recent years, the amount of remote sensing imagery data has increased exponentially. The ability to quickly and effectively find the required images from massive remote sensing archives is the key to the organization, management, and sharing of remote sensing image information. This paper proposes a high-resolution remote sensing image retrieval method with Gabor-CA-ResNet and a split-based deep feature transform network. The main contributions include two points. (1) For the complex texture, diverse scales, and special viewing angles of remote sensing images, A Gabor-CA-ResNet network taking ResNet as the backbone network is proposed by using Gabor to represent the spatial-frequency structure of images, channel attention (CA) mechanism to obtain stronger representative and discriminative deep features. (2) A split-based deep feature transform network is designed to divide the features extracted by the Gabor-CA-ResNet network into several segments and transform them separately for reducing the dimensionality and the storage space of deep features significantly. The experimental results on UCM, WHU-RS, RSSCN7, and AID datasets show that, compared with the state-of-the-art methods, our method can obtain competitive performance, especially for remote sensing images with rare targets and complex textures.

scholarly journals Efficient Patch-Wise Semantic Segmentation for Large-Scale Remote Sensing Images

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3232 ◽  
Author(s):  
Yan Liu ◽  
Qirui Ren ◽  
Jiahui Geng ◽  
Meng Ding ◽  
Jiangyun Li
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Large Scale ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Training Data ◽  
Land Resources ◽  
Remote Sensing Images ◽  
Training Strategy ◽  
The Impact

Efficient and accurate semantic segmentation is the key technique for automatic remote sensing image analysis. While there have been many segmentation methods based on traditional hand-craft feature extractors, it is still challenging to process high-resolution and large-scale remote sensing images. In this work, a novel patch-wise semantic segmentation method with a new training strategy based on fully convolutional networks is presented to segment common land resources. First, to handle the high-resolution image, the images are split as local patches and then a patch-wise network is built. Second, training data is preprocessed in several ways to meet the specific characteristics of remote sensing images, i.e., color imbalance, object rotation variations and lens distortion. Third, a multi-scale training strategy is developed to solve the severe scale variation problem. In addition, the impact of conditional random field (CRF) is studied to improve the precision. The proposed method was evaluated on a dataset collected from a capital city in West China with the Gaofen-2 satellite. The dataset contains ten common land resources (Grassland, Road, etc.). The experimental results show that the proposed algorithm achieves 54.96% in terms of mean intersection over union (MIoU) and outperforms other state-of-the-art methods in remote sensing image segmentation.

scholarly journals Performance Evaluation of Single-Label and Multi-Label Remote Sensing Image Retrieval Using a Dense Labeling Dataset

2018 ◽  
Vol 10 (6) ◽  
pp. 964 ◽  
Author(s):  
Zhenfeng Shao ◽  
Ke Yang ◽  
Weixun Zhou
Keyword(s):  
Remote Sensing ◽  
Performance Evaluation ◽  
Deep Learning ◽  
Image Retrieval ◽  
Semantic Segmentation ◽  
Semantic Content ◽  
Remote Sensing Image ◽  
Remote Sensing Images ◽  
Benchmark Datasets ◽  
Feature Based

Benchmark datasets are essential for developing and evaluating remote sensing image retrieval (RSIR) approaches. However, most of the existing datasets are single-labeled, with each image in these datasets being annotated by a single label representing the most significant semantic content of the image. This is sufficient for simple problems, such as distinguishing between a building and a beach, but multiple labels and sometimes even dense (pixel) labels are required for more complex problems, such as RSIR and semantic segmentation.We therefore extended the existing multi-labeled dataset collected for multi-label RSIR and presented a dense labeling remote sensing dataset termed "DLRSD". DLRSD contained a total of 17 classes, and the pixels of each image were assigned with 17 pre-defined labels. We used DLRSD to evaluate the performance of RSIR methods ranging from traditional handcrafted feature-based methods to deep learning-based ones. More specifically, we evaluated the performances of RSIR methods from both single-label and multi-label perspectives. These results demonstrated the advantages of multiple labels over single labels for interpreting complex remote sensing images. DLRSD provided the literature a benchmark for RSIR and other pixel-based problems such as semantic segmentation.

scholarly journals Superpixel-Guided Layer-Wise Embedding CNN for Remote Sensing Image Classification

2019 ◽  
Vol 11 (2) ◽  
pp. 174 ◽  
Author(s):  
Han Liu ◽  
Jun Li ◽  
Lin He ◽  
Yu Wang
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Remote Sensing Data ◽  
Sampling Strategy ◽  
Remote Sensing Image ◽  
Fine Tuning ◽  
Spatial Dependency ◽  
Remote Sensing Images ◽  
Training Set ◽  
Remote Sensing Image Classification

Irregular spatial dependency is one of the major characteristics of remote sensing images, which brings about challenges for classification tasks. Deep supervised models such as convolutional neural networks (CNNs) have shown great capacity for remote sensing image classification. However, they generally require a huge labeled training set for the fine tuning of a deep neural network. To handle the irregular spatial dependency of remote sensing images and mitigate the conflict between limited labeled samples and training demand, we design a superpixel-guided layer-wise embedding CNN (SLE-CNN) for remote sensing image classification, which can efficiently exploit the information from both labeled and unlabeled samples. With the superpixel-guided sampling strategy for unlabeled samples, we can achieve an automatic determination of the neighborhood covering for a spatial dependency system and thus adapting to real scenes of remote sensing images. In the designed network, two types of loss costs are combined for the training of CNN, i.e., supervised cross entropy and unsupervised reconstruction cost on both labeled and unlabeled samples, respectively. Our experimental results are conducted with three types of remote sensing data, including hyperspectral, multispectral, and synthetic aperture radar (SAR) images. The designed SLE-CNN achieves excellent classification performance in all cases with a limited labeled training set, suggesting its good potential for remote sensing image classification.

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