Method For Remote Sensing Images Research Articles

Lossless and lossy remote sensing image encryption-compression algorithm based on DeepLabv3+ and 2D CS

With the advancement of remote sensing technology, the amount of remote sensing image (RSI) has increased sharply. The explosion in data volume requires higher standards of image compression and encryption technology. This paper proposes a lossless and lossy encryption-compression method for RSI. Because RSIs contain important and unimportant information, using a normal algorithm could ignore their important information or reduce the computational efficiency, so we use the trained model by DeepLabv3+ to segment the region of interest (ROI) and the region of non-interest (RONI), and implement lossless and lossy algorithms respectively. The whole algorithm is based on the newly proposed hyperchaotic system 2D Tent coupled Infinite collapse map (2D TICM). According to the 2D TICM, the dynamic 3D Latin cube is generated. This cube is then used for dynamic scrambling algorithms between multiple planes (3D LMBS), within planes (3D LMIS), and for the multiple dynamic S boxes substitution algorithm. Due to the large size of RSI, the image is divided into blocks, and MATLAB parallel mechanism is used to encrypt each block at the same time. First, the lossy part is scrambled by 3D LMIS, then compressed with 2D compressive sensing (2D CS). Finally, each block is substituted with a different S box. A CS reconstruction algorithm combining decryption and reconstruction, 2D projection gradient chaotic decryption algorithm (2D PG-CD), is proposed. The lossless part embeds the encryption into JPEG-LS, encrypts while compressing, and then encrypts according to 3D LMBS and S boxes. The algorithm in this paper considers the characteristics of RSI, experiments show that it has good security and compression performance.

YOLO-RS: A More Accurate and Faster Object Detection Method for Remote Sensing Images

In recent years, object detection based on deep learning has been widely applied and developed. When using object detection methods to process remote sensing images, the trade-off between the speed and accuracy of models is necessary, because remote sensing images pose additional difficulties such as complex backgrounds, small objects, and dense distribution to the detection task. This paper proposes YOLO-RS, an optimized object detection algorithm based on YOLOv4 to address the challenges. The Adaptively Spatial Feature Fusion (ASFF) structure is introduced after the feature enhancement network of YOLOv4. It assigns adaptive weight parameters to fuse multi-scale feature information, improving detection accuracy. Furthermore, optimizations are applied to the Spatial Pyramid Pooling (SPP) structure in YOLOv4. By incorporating residual connections and employing 1 × 1 convolutions after maximum pooling, both computation complexity and detection accuracy are improved. To enhance detection speed, Lightnet is introduced, inspired by Depthwise Separable Convolution for reducing model complexity. Additionally, the loss function in YOLOv4 is optimized by introducing the Intersection over Union loss function. This change replaces the aspect ratio loss term with the edge length loss, enhancing sensitivity to width and height, accelerating model convergence, and improving regression accuracy for detected frames. The mean Average Precision (mAP) values of the YOLO-RS model are 87.73% and 92.81% under the TGRS-HRRSD dataset and RSOD dataset, respectively, which are experimentally verified to be 2.15% and 1.66% higher compared to the original YOLOv4 algorithm. The detection speed reached 43.45 FPS and 43.68 FPS, respectively, with 5.29 Frames Per Second (FPS) and 5.30 FPS improvement.

A Lightweight Detection Method for Remote Sensing Images and Its Energy-Efficient Accelerator on Edge Devices.

Convolutional neural networks (CNNs) have been extensively employed in remote sensing image detection and have exhibited impressive performance over the past few years. However, the abovementioned networks are generally limited by their complex structures, which make them difficult to deploy with power-sensitive and resource-constrained remote sensing edge devices. To tackle this problem, this study proposes a lightweight remote sensing detection network suitable for edge devices and an energy-efficient CNN accelerator based on field-programmable gate arrays (FPGAs). First, a series of network weight reduction and optimization methods are proposed to reduce the size of the network and the difficulty of hardware deployment. Second, a high-energy-efficiency CNN accelerator is developed. The accelerator employs a reconfigurable and efficient convolutional processing engine to perform CNN computations, and hardware optimization was performed for the proposed network structure. The experimental results obtained with the Xilinx ZYNQ Z7020 show that the network achieved higher accuracy with a smaller size, and the CNN accelerator for the proposed network exhibited a throughput of 29.53 GOPS and power consumption of only 2.98 W while consuming only 113 DSPs. In comparison with relevant work, DSP efficiency at an identical level of energy consumption was increased by 1.1-2.5 times, confirming the superiority of the proposed solution and its potential for deployment with remote sensing edge devices.

A Leading but Simple Classification Method for Remote Sensing Images

Recently, researchers have proposed a lot of deep convolutional neural network (CNN) approaches with obvious flaws to tackle the difficult semantic classification (SC) task of remote sensing images (RSI). In this paper, the author proposes a simple method that aims to provide a leading but efficient solution by using a lightweight EfficientNet-B0. First, this paper concluded the drawbacks with an analysis of mathematical theory and then proposed a qualitative conclusion on the previous methods’ theoretical performance based on theoretical derivation and experiments. Following that, the paper designs a novel method named LS-EfficientNet, consisting only of a single CNN and a concise training algorithm called SC-CNN. Far different from previous complex and hardware-extensive ones, the proposed method mainly focuses on tackling the long-neglected problems, including overfitting, data distribution shift by DA, improper use of training tricks, and other incorrect operations on a pre-trained CNN. Compared to previous studies, the proposed method is easy to reproduce because all the models, training tricks, and hyperparameter settings are open-sourced. Extensive experiments on two benchmark datasets show that the proposed method can easily surpass all the previous state-of-the-art ones, with an outstanding accuracy lead of 0.5% to 1.2% and a remarkable parameter decrease of 78% if compared to the best prior one in 2022. In addition, ablation test results also prove that the proposed effective combination of training tricks, including OLS and CutMix, can clearly boost a CNN's performance for RSI-SC, with an increase in accuracy of 1.0%. All the results reveal that a single lightweight CNN can well tackle the routine task of classifying RSI.

A Dehazing Method for Remote Sensing Image Under Nonuniform Hazy Weather Based on Deep Learning Network

Different from the ground image with uniform haze, the haze in remote sensing (RS) image has the characteristics of irregular shape and uneven concentration in hazy weather. It brings a great challenge to the application of RS image data in advanced image processing tasks. A novel dehazing network for non-uniform hazy remote sensing image, named as KFA-Net, is proposed to solve the aforementioned issues. The designed asymmetric size feature cascade (ASFC), k-means pixel attention (KPA) and FFT channel attention (FCA) in KFA-Net all show excellent effects. Compared with symmetrically linked typical Unet, ASFC can more easily extract shallow features for feature reconstruction. Furthermore, different from the commonly used pixel attention that compresses feature maps directly, KPA introduces k-means clustering algorithm in machine learning into the attention mechanism, which facilitates network training to focus on the thick hazy region. Compared to typical squeeze-and-excitation block, FCA uses the low-frequency region feature of spectrogram to obtain the attention weight coefficient in the frequency domain, making network training pay more attention to the feature of image low-frequency region. Extensive comparison experiments verify that the proposed KFA-Net has the great superiority. PSNR/SSIM of KFA-Net are 31.0952% and 6.6401% higher than DCP with the highest citation in traditional dehazing methods, respectively. PSNR/SSIM of KFA-Net are 2.2049% and 0.4966% higher than the recently proposed 4KDehazing with the best performance among all comparison dehazing methods, respectively. The KFA-Net proposed in this research can greatly enhance the temporal and spatial scope of RS image application in hazy weather conditions.

SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing Imagery

Accurately and timely detecting multiscale small objects that contain tens of pixels from remote sensing images (RSI) remains challenging. Most of the existing solutions primarily design complex deep neural networks to learn strong feature representations for objects separated from the background, which often results in a heavy computation burden. In this article, we propose an accurate yet fast object detection method for RSI, named SuperYOLO, which fuses multimodal data and performs high-resolution (HR) object detection on multiscale objects by utilizing the assisted super resolution (SR) learning and considering both the detection accuracy and computation cost. First, we utilize a symmetric compact multimodal fusion (MF) to extract supplementary information from various data for improving small object detection in RSI. Furthermore, we design a simple and flexible SR branch to learn HR feature representations that can discriminate small objects from vast backgrounds with low-resolution (LR) input, thus further improving the detection accuracy. Moreover, to avoid introducing additional computation, the SR branch is discarded in the inference stage, and the computation of the network model is reduced due to the LR input. Experimental results show that, on the widely used VEDAI RS dataset, SuperYOLO achieves an accuracy of 75.09% (in terms of mAP50 ), which is more than 10% higher than the SOTA large models, such as YOLOv5l, YOLOv5x, and RS designed YOLOrs. Meanwhile, the parameter size and GFLOPs of SuperYOLO are about 18 times and 3.8 times less than YOLOv5x. Our proposed model shows a favorable accuracy and speed tradeoff compared to the state-of-the-art models. The code will be open-sourced at https://github.com/icey-zhang/SuperYOLO.

Land Use Classification Method of Remote Sensing Images for Urban and Rural Planning Monitoring Using Deep Learning

Aiming at the problems that most existing segmentation methods are difficult to deal with the imbalance of remote sensing image distribution and the overlap of segmentation target edges, a land use classification method of remote sensing image for urban and rural planning monitoring based on deep learning is proposed. Firstly, the U-Net is improved by pooling index upsampling and dimension superposition. The improvement can not only extract high-level abstract features but also extract low-level detail features, so as to reduce the loss of image edge information in the process of deconvolution. Then, the batch normalization and scaling exponential linear unit (SeLU) are used to improve the U-Net model. Finally, the improved U-Net model is applied to the classification of remote sensing images of land use types to realize dynamic monitoring. The experimental analysis of the proposed method based on TensorFlow deep learning framework shows that its total accuracy exceeds 94%. The segmentation effect of land use types in remote sensing images is good.

TransRoadNet: A Novel Road Extraction Method for Remote Sensing Images via Combining High-Level Semantic Feature and Context

Road extraction is a significant research hotspot in the area of remote sensing images. Extracting an accurate road network from remote sensing images is still challenging because some objects in the images are similar to the road, and some results are discontinuous due to the occlusion. Recently, convolutional neural networks (CNNs) have shown their power in road extraction process. However, the contextual information can not be captured effectively by those CNNs. Based on CNNs, combining with high-level semantic features and foreground contextual information, a novel road extraction method for remote sensing images is proposed in this paper. Firstly, the position attention (PA) mechanism is designed to enhance the expression ability for the road feature. Then the contextual information extraction module (CIEM) is constructed to capture the road contextual information in the images. At last, foreground contextual information supplement module (FCISM) is proposed to provide foreground context information at different stages of the decoder, which can improve the inference ability for the occluded area. Extensive experiments on the DeepGlobal road dataset showed the proposed method outperforms the existing methods in accuracy, IoU, Precision, F1-score, and yields competitive recall results, which demonstrated the efficiency of the new model.

Avoiding Negative Transfer for Semantic Segmentation of Remote Sensing Images

Reducing the feature distribution shift caused by the factor of visual-environment changes, namely as VE-changes, is a hot issue in domain adaptation learning. However, in the semantic segmentation task of remote sensing imageries, besides VE-changes, the change of semantic-scenes (SS-changes) is another factor raising domain gap, which brings the label distribution shift. For example, although urban and rural share the same landcover label, there is still a gap in label distribution. If there is little relation that can be found in neither feature nor label space, forcibly adapting to a new domain could have a high risk of negative transfer. Hence, we propose a new Transitive Domain Adaptation method for Remote Sensing images (TDARS). Firstly, we introduce an intermediate domain to enlarge the relation between the given source and target domains. Secondly, we learn from primary and non-primary confident classes to increase the likelihood of transferring valuable information. As a result, TDARS enables the given source and target domains to be connected through the selected intermediate domain and performs effective knowledge transfer among all domains. The proposed method is evaluated on three domain adaptation datasets of remote sensing images. Extensive experiments show the approach can effectively handle the domain shift problem from remote sensing images compared to other state-of-the-art domain adaptation methods.

Remote-Sensing Image Tile-Level Annotation Based on Discriminative Features and Expressive Visual Word Descriptors

Remote-sensing images play a crucial role in a wide range of applications and have been receiving significant attention. In recent years, great efforts have been made in developing various methods for intelligent interpretation of remote-sensing images. Generally speaking, machine learning-based methods of remote-sensing image interpretation require a large number of labeled samples and there are still not enough annotated datasets in the field of remote sensing. However, manual annotation of remote-sensing images is usually labor-intensive and requires expert knowledge and the accuracy of annotation results is relatively low. The goal of this paper is to propose a novel tile-level annotation method of remote-sensing images to obtain remote-sensing datasets which are well-labeled and contain accurate semantic concepts. Firstly, we use a set of images with defined semantic concepts to represent the training set and divide them into several nonoverlapping regions. Secondly, the color features, texture features, and spatial features of each region are extracted, and discriminative features are obtained by the weight optimization feature fusion method. Then, the features are quantized into visual words by applying a density-based clustering center selection method and an isolated feature point elimination method. And the remote-sensing images can be represented by a series of visual words. Finally, the LDA model is used to calculate the probabilities of semantic categories for each region. The experiments are conducted on remote-sensing images which demonstrate that our proposed method can achieve good performance on remote-sensing image tile-level annotation. The implications of our research can obtain annotated datasets with accurate semantic concepts for intelligent interpretation of remote-sensing images.

An Improved Adaptive Spatial Preprocessing Method for Remote Sensing Images.

Since remote sensing images are one of the main sources for people to obtain required information, the quality of the image becomes particularly important. Nevertheless, noise often inevitably exists in the image, and the targets are usually blurred by the acquisition of the imaging system, resulting in the degradation of quality of the images. In this paper, a novel preprocessing algorithm is proposed to simultaneously smooth noise and to enhance the edges, which can improve the visual quality of remote sensing images. It consists of an improved adaptive spatial filter, which is a weighted filter integrating functions of both noise removal and edge sharpness. Its processing parameters are flexible and adjustable relative to different images. The experimental results confirm that the proposed method outperforms the existing spatial algorithms both visually and quantitatively. It can play an important role in the remote sensing field in order to achieve more information of interested targets.

Ecological Remote Sensing Image Monitoring Method for Vegetation Destruction in Waterfront Greenway Based on Genetic Algorithm

<p>Because the traditional remote sensing image monitoring method has the problem of poor ability of capturing vegetation destruction, an ecological remote sensing image method for vegetation destruction in waterfront greenway based on genetic algorithm is proposed. The vegetation index data of waterfront greenway were acquired by remote sensing image, and the pseudo color image was synthesized. The on orbit radiometric calibration and geometric precision correction are carried out for the collected data, and the relationship between vegetation index and vegetation damage of waterfront greenway is analyzed, including meteorological factors, geographical factors and vegetation damage of waterfront greenway. Select the independent variables of the model, establish the ecological remote sensing image monitoring model of the vegetation damage of the waterfront greenway, and realize the ecological remote sensing image monitoring of the vegetation damage of the waterfront greenway. The experimental results show that the acquisition ability of the method based on genetic algorithm is better than that of the traditional method, and it is more suitable for the ecological remote sensing monitoring of the vegetation damage of waterfront greenway.</p>

GAN-Based Semisupervised Scene Classification of Remote Sensing Image

With the advent of a large number of remote sensing images (RSIs), scene classification of RSI is widely applied to many fields such as urban planning, natural disaster detection, and environmental monitoring. Compared with the natural image field, the lack of labeled RSI is a bottleneck of supervised scene classification methods based on deep learning. Meanwhile, unsupervised scene classification is difficult to meet actual needs. To this end, we propose a novel semisupervised scene classification method for RSI using generative adversarial nets (GANs), in which a gating unit, a self-attention gating (SAG) module, and a pretrained Inception V3 branch are introduced into discriminative network to enhance the feature representation capability for facilitating semisupervised classification. To be specific, the gating unit aims to learn the weights of each feature map and capture the dependence relationship between features. The SAG module aims to capture a long-range dependence for adaptively focusing on important scene regions. The Inception V3 branch aims to extract the high-level semantic representation of input images and further enhance the discriminant ability by gating unit and SAG module. Furthermore, a new optimization term is incorporated into the generator loss to indirectly drive discriminator to correctly classify scene images. To verify the effectiveness of the proposed method, extensive experimental results on UC Merced and EuroSAT data sets demonstrate that the method surpasses most of the state-of-the-art semisupervised image classification methods significantly, especially when only few samples are tagged.

A Local Distinctive Features Matching Method for Remote Sensing Images with Repetitive Patterns

A Local Distinctive Features Matching Method for Remote Sensing Images with Repetitive Patterns

A novel remote-sensing image fusion method based on hybrid visual saliency analysis

ABSTRACTIn practical applications, different regions in images may practically have different demands for the spatial and spectral resolution. However, most existing methods execute a unified fusion processing of the whole image with no consideration of such diverse demands. In this article, a new fusion method for remote-sensing images based on saliency analysis is proposed for addressing the issue. By introducing the hybrid visual saliency analysis, regions in the panchromatic (Pan) image and multispectral image would be automatically partitioned into two parts: salient and non-salient regions. Then, a subregion fusion strategy is applied to fuse the non-salient and salient regions, respectively. As for salient regions such as residential areas and roads, the adaptive intensity–hue–saturation (adaptive IHS) method is implemented for its merit of effective improvement in spatial quality. For non-salient regions such as farmland, forest, and grassland, the dual-tree complex wavelet transform is used in the process of spatial detail extraction, and the combination coefficients yielded by the adaptive IHS method are integrated to suppress the spectral distortion. Experimental results demonstrate that our proposal provides state-of-the-art performance as well as achieves a better balance between spatial injection and spectral maintenance in different regions.

An interval number distance- and ranking-based method for remotely sensed image fuzzy clustering

ABSTRACTFuzzy c-means clustering is an important non-supervised classification method for remote-sensing images and is based on type-1 fuzzy set theory. Type-1 fuzzy sets use singleton values to express the membership grade; therefore, such sets cannot describe the uncertainty of the membership grade. Interval type-2 fuzzy c-means (IT2FCM) clustering and relevant methods are based on interval type-2 fuzzy sets. Real vectors are used to describe the clustering centres, and the average values of the upper and lower membership grades are used to determine the classification of each pixel. Thus, the width information for interval clustering centres and interval membership grades are ignored. The main contribution of this article is to propose an improved IT2FCM* algorithm by adopting interval number distance (IND) and ranking methods, which use the width information of interval clustering centres and interval membership grades, thus distinguishing this method from existing fuzzy clustering methods. Three different IND definitions are tested, and the distance definition proposed by Li shows the best performance. The second contribution of this work is that two fuzzy cluster validity indices, FS- and XB-, are improved using the IND. Three types of multi/hyperspectral remote-sensing data sets are used to test this algorithm, and the experimental results show that the IT2FCM* algorithm based on the IND proposed by Li performs better than the IT2FCM algorithm using four cluster validity indices, the confusion matrix, and the kappa coefficient (κ). Additionally, the improved FS- index has more indicative ability than the original FS- index.

A fast marine sewage detection method for remote-sensing image

This paper presents an effective method for marine sewage detection from a remote-sensing image. It is inspired by the Grab-Cut mechanism that iterative estimation and incomplete labeling allow a considerably reduced degree of user interaction for a given quality of result. By establishing the relationship between the color feature and the object seeds, we first model object and background with Gaussian mixture model, respectively, followed by iteratively updating the parameter of model to decline the energy function. To improve the computation efficiency, we propose to extend the region of interest as background. The proposed method accounts for not only the effect of color feature, but also the geographical information. The experimental results demonstrate that the proposed method is more reliable in marine sewage detection compared to other state-of-the-art methods.

AN IMPROVED DESTRIPING METHOD FOR REMOTE SENSING IMAGES

AN IMPROVED DESTRIPING METHOD FOR REMOTE SENSING IMAGES

A Novel Unsupervised Segmentation Quality Evaluation Method for Remote Sensing Images.

The segmentation of a high spatial resolution remote sensing image is a critical step in geographic object-based image analysis (GEOBIA). Evaluating the performance of segmentation without ground truth data, i.e., unsupervised evaluation, is important for the comparison of segmentation algorithms and the automatic selection of optimal parameters. This unsupervised strategy currently faces several challenges in practice, such as difficulties in designing effective indicators and limitations of the spectral values in the feature representation. This study proposes a novel unsupervised evaluation method to quantitatively measure the quality of segmentation results to overcome these problems. In this method, multiple spectral and spatial features of images are first extracted simultaneously and then integrated into a feature set to improve the quality of the feature representation of ground objects. The indicators designed for spatial stratified heterogeneity and spatial autocorrelation are included to estimate the properties of the segments in this integrated feature set. These two indicators are then combined into a global assessment metric as the final quality score. The trade-offs of the combined indicators are accounted for using a strategy based on the Mahalanobis distance, which can be exhibited geometrically. The method is tested on two segmentation algorithms and three testing images. The proposed method is compared with two existing unsupervised methods and a supervised method to confirm its capabilities. Through comparison and visual analysis, the results verified the effectiveness of the proposed method and demonstrated the reliability and improvements of this method with respect to other methods.

Object-oriented change detection method based on adaptive multi-method combination for remote-sensing images

ABSTRACTIn this study, we propose a novel object-oriented change detection method for remote-sensing images. First, the Gabor texture and Markov random field texture are extracted based on the remote-sensing images, and an initial pixel-level change detection result is produced. Second, in order to reduce the influence of feature uncertainty on the change detection results, the weights of different features are calculated by the Relief algorithm based on the initial pixel-level change detection result, and several difference images are fused to obtain a single comprehensive difference image. Third, different pixel-level change detection results are obtained using diverse change detection methods. The two-temporal images are then stacked and segmented, and to ensure change detection method separability, the weighted object change probability is obtained by fusing five different object change probabilities, which are calculated from the pixel-level change detection results. Finally, the objects are labelled as the class with a higher weighted object change probability. Our experimental results showed that the accuracy of change detection results obtained using the weighted object change probability was higher than that of the change detection results produced using the independent object change probability.