Search

Faced with the problem of incompatibility between traditional information acquisition mode and spaceborne earth observation tasks, starting from the general mathematical model of compressed sensing, a theoretical model of block compressed sensing was established, and a full-process adaptive coding and decoding compressed sensing framework for remote sensing images was proposed, which includes five parts: mode selection, feature factor extraction, adaptive shape segmentation, adaptive sampling rate allocation and image reconstruction. Unlike previous semi-adaptive or local adaptive methods, the advantages of the adaptive encoding and decoding method proposed in this paper are mainly reflected in four aspects: (1) Ability to select encoding modes based on image content, and maximizing the use of the richness of the image to select appropriate sampling methods; (2) Capable of utilizing image texture details for adaptive segmentation, effectively separating complex and smooth regions; (3) Being able to detect the sparsity of encoding blocks and adaptively allocate sampling rates to fully explore the compressibility of images; (4) The reconstruction matrix can be adaptively selected based on the size of the encoding block to alleviate block artifacts caused by non-stationary characteristics of the image. Experimental results show that the method proposed in this article has good stability for remote sensing images with complex edge textures, with the peak signal-to-noise ratio and structural similarity remaining above 35 dB and 0.8. Moreover, especially for ocean images with relatively simple image content, when the sampling rate is 0.26, the peak signal-to-noise ratio reaches 50.8 dB, and the structural similarity is 0.99. In addition, the recovered images have the smallest BRISQUE value, with better clarity and less distortion. In the subjective aspect, the reconstructed image has clear edge details and good reconstruction effect, while the block effect is effectively suppressed. The framework designed in this paper is superior to similar algorithms in both subjective visual and objective evaluation indexes, which is of great significance for alleviating the incompatibility between traditional information acquisition methods and satellite-borne earth observation missions. [ABSTRACT FROM AUTHOR]

Image super-resolution (SR) technology can improve the resolution of images and provide clearer and more reliable remote sensing images of high quality to better serve the subsequent applications. However, when reconstructing high-frequency feature areas of remote sensing images, existing SR reconstruction methods are prone to artifacts that affect visual effects and make it difficult to generate real texture details. In order to address this issue, a texture-detail-enhanced dense generative adversarial network (TDEGAN) for remote sensing image SR is presented. The generator uses multi-level dense connections, residual connections, and Shuffle attention (SA) to improve the feature extraction ability. A PatchGAN-style discrimination network is designed to effectively perform local discrimination and helps the network generate rich, detailed features. To reduce the impact of artifacts, we introduce an artifact loss function, which is combined with the exponential moving average (EMA) technique to distinguish the artifacts generated from the actual texture details through local statistics, which can help the network reduce artifacts and generate more realistic texture details. Experiments show that TDEGAN can better restore the texture details of remote sensing images and achieves certain advantages in terms of evaluation indicators and visualization. [ABSTRACT FROM AUTHOR]

The present study proposes a novel deep-learning model for remote sensing image enhancement. It maintains image details while enhancing brightness in the feature extraction module. An improved hierarchical model named Global Spatial Attention Network (GSA-Net), based on U-Net for image enhancement, is proposed to improve the model's performance. To circumvent the issue of insufficient sample data, gamma correction is applied to create low-light images, which are then used as training examples. A loss function is constructed using the Structural Similarity (SSIM) and Peak Signal-to-Noise Ratio (PSNR) indices. The GSA-Net network and loss function are utilized to restore images obtained via low-light remote sensing. This proposed method was tested on the Northwestern Polytechnical University Very-High-Resolution 10 (NWPU VHR-10) dataset, and its overall superiority was demonstrated in comparison with other state-of-the-art algorithms using various objective assessment indicators, such as PSNR, SSIM, and Learned Perceptual Image Patch Similarity (LPIPS). Furthermore, in high-level visual tasks such as object detection, this novel method provides better remote sensing images with distinct details and higher contrast than the competing methods. [ABSTRACT FROM AUTHOR]

Multi-source image registration has often suffered from great radiation and geometric differences. Specifically, grayscale and texture from similar landforms in different source images often show significantly different visual features, and these differences disturb the corresponding point extraction in the following image registration process. Considering that edges between heterogeneous images can provide homogeneous information and more consistent features can be extracted based on image edges, an edge consistency radiation-change insensitive feature transform (EC-RIFT) method is proposed in this paper. Firstly, the noise and texture interference are reduced by preprocessing according to the image characteristics. Secondly, image edges are extracted based on phase congruency, and an orthogonal Log-Gabor filter is performed to replace the global algorithm. Finally, the descriptors are built with logarithmic partition of the feature point neighborhood, which improves the robustness of the descriptors. Comparative experiments on datasets containing multi-source remote sensing image pairs show that the proposed EC-RIFT method outperforms other registration methods in terms of precision and effectiveness. [ABSTRACT FROM AUTHOR]

Plant lodging is one of the most essential phenotypes for soybean breeding programs. Soybean lodging is conventionally evaluated visually by breeders, which is time-consuming and subject to human errors. This study aimed to investigate the potential of unmanned aerial vehicle (UAV)-based imagery and machine learning in assessing the lodging conditions of soybean breeding lines. A UAV imaging system equipped with an RGB (red-green-blue) camera was used to collect the imagery data of 1266 four-row plots in a soybean breeding field at the reproductive stage. Soybean lodging scores were visually assessed by experienced breeders, and the scores were grouped into four classes, i.e., non-lodging, moderate lodging, high lodging, and severe lodging. UAV images were stitched to build orthomosaics, and soybean plots were segmented using a grid method. Twelve image features were extracted from the collected images to assess the lodging scores of each breeding line. Four models, i.e., extreme gradient boosting (XGBoost), random forest (RF), K-nearest neighbor (KNN) and artificial neural network (ANN), were evaluated to classify soybean lodging classes. Five data preprocessing methods were used to treat the imbalanced dataset to improve classification accuracy. Results indicate that the preprocessing method SMOTE-ENN consistently performs well for all four (XGBoost, RF, KNN, and ANN) classifiers, achieving the highest overall accuracy (OA), lowest misclassification, higher F1-score, and higher Kappa coefficient. This suggests that Synthetic Minority Oversampling-Edited Nearest Neighbor (SMOTE-ENN) may be a good preprocessing method for using unbalanced datasets and the classification task. Furthermore, an overall accuracy of 96% was obtained using the SMOTE-ENN dataset and ANN classifier. The study indicated that an imagery-based classification model could be implemented in a breeding program to differentiate soybean lodging phenotype and classify lodging scores effectively. [ABSTRACT FROM AUTHOR]

Featured Application: Change detection for remote sensing images. Aiming at the problem of misdetection caused by the traditional texture characteristic extraction model, which does not describe the correlation among multiple bands, an object-oriented remote sensing image change detection method based on a color co-occurrence matrix is proposed. First, the image is divided into multi-scale objects by graph-based superpixel segmentation, and the optimal scale is determined by the overall goodness F-measure (OGF). Then, except for the extraction of the spectral features, the multi-channel texture features based on the color co-occurrence matrix (CCM) are extracted to consider the correlation among multiple bands. To accurately find the representative features to overcome the impact of feature redundancy, a cumulative backward search strategy (CBSS) is further designed. Finally, the change detection is completed by inputting the difference image of dual time points to the trained random forest model. Taking Shenzhen and Dapeng as the study areas, with Sentinel-2 and Skysat images under different spatial resolutions, and the forest–bareland change type as an example, the effectiveness of the proposed algorithm is verified by qualitative and quantitative analyses. They show that the proposed algorithm can obtain higher detection accuracy than the texture features without band correlation. [ABSTRACT FROM AUTHOR]

Deep learning approaches are gaining popularity in image feature analysis and in attaining state-of-the-art performances in scene classification of remote sensing imagery. This article presents a comprehensive review of the developments of various computer vision methods in remote sensing. There is currently an increase of remote sensing datasets with diverse scene semantics; this renders computer vision methods challenging to characterize the scene images for accurate scene classification effectively. This paper presents technology breakthroughs in deep learning and discusses their artificial intelligence open-source software implementation framework capabilities. Further, this paper discusses the open gaps/opportunities that need to be addressed by remote sensing communities. [ABSTRACT FROM AUTHOR]

High-quality remote sensing images play important roles in the development of ecological indicators' mapping, urban-rural management, urban planning, and other fields. Compared with natural images, remote sensing images have more abundant land cover along with lower spatial resolutions. Given the embedded longitude and latitude information of remote sensing images, reference (Ref) images with similar scenes could be more accessible. However, existing traditional super-resolution (SR) approaches always depend on increases in network depth to improve performance, which limits the acquisition and application of high-quality remote sensing images. In this paper, we proposed a novel, reference-image-based, super-resolution method with feature compression module (FCSR) for remote sensing images to alleviate the above issue while effectively utilizing high-resolution (HR) information from Ref images. Specifically, we exploited a feature compression branch (FCB) to extract relevant features in feature detail matching with large measurements. This branch employed a feature compression module (FCM) to extract features from low-resolution (LR) and Ref images, which enabled texture transfer from different perspectives. To decrease the impact of environmental factors such as resolution, brightness and ambiguity disparities between the LR and Ref images, we designed a feature extraction encoder (FEE) to ensure accuracy in feature extraction in the feature acquisition branch. The experimental results demonstrate that the proposed FCSR achieves significant performance and visual quality compared to state-of-the-art SR methods. Explicitly, when compared with the best method, the average peak signal-to-noise ratio (PSNR) index on the three test sets is improved by 1.0877%, 0.8161%, 1.0296%, respectively, and the structural similarity (SSIM) index on four test sets is improved by 1.4764%, 1.4467%, 0.0882%, and 1.8371%, respectively. Simultaneously, FCSR obtains satisfactory visual details following qualitative evaluation. [ABSTRACT FROM AUTHOR]

Registration between remote sensing images has been a research focus in the field of remote sensing image processing. Most of the existing image registration algorithms applied to feature point matching are derived from image feature extraction methods, such as scale-invariant feature transform (SIFT), speed-up robust features (SURF) and Siamese neural network. Such methods encounter difficulties in achieving accurate image registration where there is a large bias in the image features or no significant feature points. Aiming to solve this problem, this paper proposes an algorithm for multi-source image registration based on geographical location information (GLI). By calculating the geographic location information that corresponds to the pixel in the image, the ideal projected pixel position of the corresponding image is obtained using spatial coordinate transformation. Additionally, the corresponding relationship between the two images is calculated by combining multiple sets of registration points. The simulation experiment illustrates that, under selected common simulation parameters, the average value of the relative registration-point error between the two images is 12.64 pixels, and the registration accuracy of the corresponding ground registration point is higher than 6.5 m. In the registration experiment involving remote sensing images from different sources, the average registration pixel error of this algorithm is 20.92 pixels, and the registration error of the image center is 21.24 pixels. In comparison, the image center registration error given by the convolutional neural network (CNN) is 142.35 pixels after the registration error is manually eliminated. For the registration of homologous and featureless remote sensing images, the SIFT algorithm can only offer one set of registration points for the correct region, and the neural network cannot achieve accurate registration results. The registration accuracy of the presented algorithm is 7.2 pixels, corresponding to a ground registration accuracy of 4.32 m and achieving more accurate registration between featureless images. [ABSTRACT FROM AUTHOR]

Spectral-spatial classification of hyperspectral images (HSIs) has recently attracted great attention in the research domain of remote sensing. It is well-known that, in remote sensing applications, spectral features are the fundamental information and spatial patterns provide the complementary information. With both spectral features and spatial patterns, hyperspectral image (HSI) applications can be fully explored and the classification performance can be greatly improved. In reality, spatial patterns can be extracted to represent a line, a clustering of points or image texture, which denote the local or global spatial characteristic of HSIs. In this paper, we propose a spectral-spatial HSI classification model based on superpixel pattern (SP) and kernel based extreme learning machine (KELM), called SP-KELM, to identify the land covers of pixels in HSIs. In the proposed SP-KELM model, superpixel pattern features are extracted by an advanced principal component analysis (PCA), which is based on superpixel segmentation in HSIs and used to denote spatial information. The KELM method is then employed to be a classifier in the proposed spectral-spatial model with both the original spectral features and the extracted spatial pattern features. Experimental results on three publicly available HSI datasets verify the effectiveness of the proposed SP-KELM model, with the performance improvement of 10% over the spectral approaches. [ABSTRACT FROM AUTHOR]

Urban forests maintain the ecological balance of cities and are significant in promoting the sustainable development of cities. Therefore, using advanced remote sensing technology to accurately extract forest green space in the city and monitor its change in real-time is very important. Taking Nanjing as the study area, this research extracted 55 vegetation phenological features from Sentinel-2A time series images and formed a feature set containing 81 parameters together with 26 features, including polarimetric- and texture-related information extracted from dual-polarization Sentinel-1A data. On the basis of the improved ABC (ABC-LIBSVM) feature selection method, the optimal feature subset was selected, and the forest coverage areas in the study area were accurately described. To verify the feasibility of the improved feature selection method and explore the potential for the development of multi-source time series remote sensing for urban forest feature extraction, this paper also used the random forest classification model to classify four different feature sets. The results revealed that the classification accuracy based on the feature set obtained by the ABC-LIBSVM algorithm was the highest, with an overall accuracy of 86.80% and a kappa coefficient of 0.8145. The producer accuracy and user accuracy of the urban forest were 93.21% and 82.45%, respectively. Furthermore, by combining the multi-source time series Sentinel-2A optical images with Sentinel-1A dual-polarization SAR images, urban forests can be distinguished from the perspective of phenology, and polarimetric- and texture-related features can contribute to the accurate identification of forests. [ABSTRACT FROM AUTHOR]

Popular deep-learning-based spatiotemporal fusion methods for creating high-temporal–high-spatial-resolution images have certain limitations. The reconstructed images suffer from insufficient retention of high-frequency information and the model suffers from poor robustness, owing to the lack of training datasets. We propose a dual-branch remote sensing spatiotemporal fusion network based on a selection kernel mechanism. The network model comprises a super-resolution network module, a high-frequency feature extraction module, and a difference reconstruction module. Convolution kernel adaptive mechanisms are added to the high-frequency feature extraction module and difference reconstruction module to improve robustness. The super-resolution module upgrades the coarse image to a transition image matching the fine image; the high-frequency feature extraction module extracts the high-frequency features of the fine image to supplement the high-frequency features for the difference reconstruction module; the difference reconstruction module uses the structural similarity for fine-difference image reconstruction. The fusion result is obtained by combining the reconstructed fine-difference image with the known fine image. The compound loss function is used to help network training. Experiments are carried out on three datasets and five representative spatiotemporal fusion algorithms are used for comparison. Subjective and objective evaluations validate the superiority of our proposed method. [ABSTRACT FROM AUTHOR]

Forest above-ground biomass (AGB) is an important index to evaluate forest carbon sequestration capacity, which is very important to maintain the stability of forest ecosystems. At present, the wide use of remote sensing technology makes it possible to estimate the large-scale forest AGB accurately and efficiently. Airborne hyperspectral remote sensing data can obtain rich spectral information and spatial structure information on the forest canopy with the characteristics of high spatial and hyperspectral resolution. Airborne LiDAR data can describe the three-dimensional structure characteristics of a forest and provide vertical structure information related to biomass. Based on the characteristics of the two data sources, this study takes Gaofeng forest farm in Nanning, Guangxi, as the study area, Chinese fir, pine tree, eucalyptus and other broadleaved trees as the research object, and constructs the AGB estimation models of different tree species by fusing airborne LiDAR and hyperspectral features. Firstly, spectral features, texture features, vegetation index, wavelet transform features and edge features are extracted from hyperspectral data. Canopy structure features, point cloud structure features, point cloud density features and terrain features are extracted from airborne LiDAR data. Secondly, the random forest (RF) method is used to screen the features of the two sets of data, and the features with the highest importance are selected. Finally, based on the optimal features of the two data sources, the forest AGB model is constructed using the multiple stepwise regression method. The results show that the texture features extracted by wavelet transform can be used for AGB modeling. The AGB of eucalyptus has high correlation with height features derived from airborne LiDAR, the AGB of other broadleaved trees mostly depends on the wavelet transform texture features from airborne hyperspectral data, while the AGB of Chinese fir and pine tree has high correlation with both height features and spectral features. Feature-fusion-based LiDAR and hyperspectral data can greatly improve the accuracy of the AGB models. The accuracy of the optimal AGB models of Chinese fir, pine tree, eucalyptus and other broadleaved trees is 0.78, 0.95, 0.72 and 0.89, respectively. In conclusion, more accurate estimation results can be obtained by combining active and passive remote sensing data for forest AGB estimation, which provides a solution for carbon storage assessment and forest ecosystem assessment. [ABSTRACT FROM AUTHOR]

Resolution is a comprehensive reflection and evaluation index for the visual quality of remote sensing images. Super-resolution processing has been widely applied for extracting information from remote sensing images. Recently, deep learning methods have found increasing application in the super-resolution processing of remote sensing images. However, issues such as blurry object edges and existing artifacts persist. To overcome these issues, this study proposes an improved generative adversarial network with self-attention and texture enhancement (TE-SAGAN) for remote sensing super-resolution images. We first designed an improved generator based on the residual dense block with a self-attention mechanism and weight normalization. The generator gains the feature extraction capability and enhances the training model stability to improve edge contour and texture. Subsequently, a joint loss, which is a combination of L1-norm, perceptual, and texture losses, is designed to optimize the training process and remove artifacts. The L1-norm loss is designed to ensure the consistency of low-frequency pixels; perceptual loss is used to entrench medium- and high-frequency details; and texture loss provides the local features for the super-resolution process. The results of experiments using a publicly available dataset (UC Merced Land Use dataset) and our dataset show that the proposed TE-SAGAN yields clear edges and textures in the super-resolution reconstruction of remote sensing images. [ABSTRACT FROM AUTHOR]

Extracting buildings and roads from remote sensing images is very important in the area of land cover monitoring, which is of great help to urban planning. Currently, a deep learning method is used by the majority of building and road extraction algorithms. However, for existing semantic segmentation, it has a limitation on the receptive field of high-resolution remote sensing images, which means that it can not show the long-distance scene well during pixel classification, and the image features is compressed during down-sampling, meaning that the detailed information is lost. In order to address these issues, Hybrid Multi-resolution and Transformer semantic extraction Network (HMRT) is proposed in this paper, by which a global receptive field for each pixel can be provided, a small receptive field of convolutional neural networks (CNN) can be overcome, and the ability of scene understanding can be enhanced well. Firstly, we blend the features by branches of different resolutions to keep the high-resolution and multi-resolution during down-sampling and fully retain feature information. Secondly, we introduce the Transformer sequence feature extraction network and use encoding and decoding to realize that each pixel has the global receptive field. The recall, F1, OA and MIoU of HMPR obtain 85.32%, 84.88%, 85.99% and 74.19%, respectively, in the main experiment and reach 91.29%, 90.41%, 91.32% and 84.00%, respectively, in the generalization experiment, which prove that the method proposed is better than existing methods. [ABSTRACT FROM AUTHOR]

Using satellite remote sensing has become a mainstream approach for extracting crop spatial distribution. Making edges finer is a challenge, while simultaneously extracting crop spatial distribution information from high-resolution remote sensing images using a convolutional neural network (CNN). Based on the characteristics of the crop area in the Gaofen 2 (GF-2) images, this paper proposes an improved CNN to extract fine crop areas. The CNN comprises a feature extractor and a classifier. The feature extractor employs a spectral feature extraction unit to generate spectral features, and five coding-decoding-pair units to generate five level features. A linear model is used to fuse features of different levels, and the fusion results are up-sampled to obtain a feature map consistent with the structure of the input image. This feature map is used by the classifier to perform pixel-by-pixel classification. In this study, the SegNet and RefineNet models and 21 GF-2 images of Feicheng County, Shandong Province, China, were chosen for comparison experiment. Our approach had an accuracy of 93.26%, which is higher than those of the existing SegNet (78.12%) and RefineNet (86.54%) models. This demonstrates the superiority of the proposed method in extracting crop spatial distribution information from GF-2 remote sensing images. [ABSTRACT FROM AUTHOR]

Variation in the format and classification requirements for remote sensing data makes establishing a standard remote sensing sample dataset difficult. As a result, few remote sensing deep neural network models have been widely accepted. We propose a hybrid deep neural network model based on a convolutional auto-encoder and a complementary convolutional neural network to solve this problem. The convolutional auto-encoder supports feature extraction and data dimension reduction of remote sensing data. The extracted features are input into the convolutional neural network and subsequently classified. Experimental results show that in the proposed model, the classification accuracy increases from 0.916 to 0.944, compared to a traditional convolutional neural network model; furthermore, the number of training runs is reduced from 40,000 to 22,000, and the number of labelled samples can be reduced by more than half, all while ensuring a classification accuracy of no less than 0.9, which suggests the effectiveness and feasibility of the proposed model. [ABSTRACT FROM AUTHOR]

In this article, we develop a novel method for the detection of vineyard parcels in agricultural landscapes based on very high resolution (VHR) optical remote sensing images. Our objective is to perform texture-based image retrieval and supervised classification algorithms. To do that, the local textural and structural features inside each image are taken into account to measure its similarity to other images. In fact, VHR images usually involve a variety of local textures and structures that may verify a weak stationarity hypothesis. Hence, an approach only based on characteristic points, not on all pixels of the image, is supposed to be relevant. This work proposes to construct the local extrema-based descriptor (LED) by using the local maximum and local minimum pixels extracted from the image. The LED descriptor is formed based on the radiometric, geometric and gradient features from these local extrema. We first exploit the proposed LED descriptor for the retrieval task to evaluate its performance on texture discrimination. Then, it is embedded into a supervised classification framework to detect vine parcels using VHR satellite images. Experiments performed on VHR panchromatic PLEIADES image data prove the effectiveness of the proposed strategy. Compared to state-of-the-art methods, an enhancement of about 7% in retrieval rate is achieved. For the detection task, about 90% of vineyards are correctly detected. [ABSTRACT FROM AUTHOR]

Optical remote sensing images are widely used in the fields of feature recognition, scene semantic segmentation, and others. However, the quality of remote sensing images is degraded due to the influence of various noises, which seriously affects the practical use of remote sensing images. As remote sensing images have more complex texture features than ordinary images, this will lead to the previous denoising algorithm failing to achieve the desired result. Therefore, we propose a novel remote sensing image denoising network (RSIDNet) based on a deep learning approach, which mainly consists of a multi-scale feature extraction module (MFE), multiple local skip-connected enhanced attention blocks (ECA), a global feature fusion block (GFF), and a noisy image reconstruction block (NR). The combination of these modules greatly improves the model's use of the extracted features and increases the model's denoising capability. Extensive experiments on synthetic Gaussian noise datasets and real noise datasets have shown that RSIDNet achieves satisfactory results. RSIDNet can improve the loss of detail information in denoised images in traditional denoising methods, retaining more of the higher-frequency components, which can have performance improvements for subsequent image processing. [ABSTRACT FROM AUTHOR]

Large-scale optical sensing and precise, rapid assessment of seismic building damage in urban communities are increasingly demanded in disaster prevention and reduction. The common method is to train a convolutional neural network (CNN) in a pixel-level semantic segmentation approach and does not fully consider the characteristics of the assessment objectives. This study developed a machine-learning-derived two-stage method for post-earthquake building location and damage assessment considering the data characteristics of satellite remote sensing (SRS) optical images with dense distribution, small size, and imbalanced numbers. It included a modified You Only Look Once (YOLOv4) object detection module and a support vector machine (SVM) based classification module. In the primary step, the multiscale features were successfully extracted and fused from SRS images of densely distributed buildings by optimizing the YOLOv4 model toward the network structures, training hyperparameters, and anchor boxes. The fusion improved multi-channel features, optimization of network structure and hyperparameters have significantly enhanced the average location accuracy of post-earthquake buildings. Thereafter, three statistics (i.e., the angular second moment, dissimilarity, and inverse difference moment) were further discovered to effectively extract the characteristic value for earthquake damage from located buildings in SRS optical images based on the gray level co-occurrence matrix. They were used as the texture features to distinguish damage intensities of buildings, using the SVM model. The investigated dataset included 386 pre- and post-earthquake SRS optical images of the 2017 Mexico City earthquake, with a resolution of 1024 × 1024 pixels. Results show that the average location accuracy of post-earthquake buildings exceeds 95.7% and that the binary classification accuracy for damage assessment reaches 97.1%. The proposed two-stage method was validated by its extremely high precision in respect of densely distributed small buildings, indicating the promising potential of computer vision in large-scale disaster prevention and reduction using SRS datasets. [ABSTRACT FROM AUTHOR]

This study employed a data fusion method to extract the high-similarity time series feature index of a dataset through the integration of MS (Multi-Spectrum) and SAR (Synthetic Aperture Radar) images. The farmlands are divided into small pieces that consider the different behaviors of farmers for their planting contents in Taiwan. Hence, the conventional image classification process cannot produce good outcomes. The crop phenological information will be a core factor to multi-period image data. Accordingly, the study intends to resolve the previous problem by using three different SPOT6 satellite images and nine Sentinel-1A synthetic aperture radar images, which were used to calculate features such as texture and indicator information, in 2019. Considering that a Dynamic Time Warping (DTW) index (i) can integrate different image data sources, (ii) can integrate data of different lengths, and (iii) can generate information with time characteristics, this type of index can resolve certain classification problems with long-term crop classification and monitoring. More specifically, this study used the time series data analysis of DTW to produce "multi-scale time series feature similarity indicators". We used three approaches (Support Vector Machine, Neural Network, and Decision Tree) to classify paddy patches into two groups: (a) the first group did not apply a DTW index, and (b) the second group extracted conflict predicted data from (a) to apply a DTW index. The outcomes from the second group performed better than the first group in regard to overall accuracy (OA) and kappa. Among those classifiers, the Neural Network approach had the largest improvement of OA and kappa from 89.51, 0.66 to 92.63, 0.74, respectively. The rest of the two classifiers also showed progress. The best performance of classification results was obtained from the Decision Tree of 94.71, 0.81. Observing the outcomes, the interference effects of the image were resolved successfully by various image problems using the spectral image and radar image for paddy rice classification. The overall accuracy and kappa showed improvement, and the maximum kappa was enhanced by about 8%. The classification performance was improved by considering the DTW index. [ABSTRACT FROM AUTHOR]

Automatic weed detection and classification faces the challenges of large intraclass variation and high spectral similarity to other vegetation. With the availability of new high-resolution remote sensing data from various platforms and sensors, it is possible to capture both spectral and spatial characteristics of weed species at multiple scales. Effective multi-resolution feature learning is then desirable to extract distinctive intensity, texture and shape features of each category of weed to enhance the weed separability. We propose a feature extraction method using a Convolutional Neural Network (CNN) and superpixel based Local Binary Pattern (LBP). Both middle and high level spatial features are learned using the CNN. Local texture features from superpixel-based LBP are extracted, and are also used as input to Support Vector Machines (SVM) for weed classification. Experimental results on the hyperspectral and remote sensing datasets verify the effectiveness of the proposed method, and show that it outperforms several feature extraction approaches. [ABSTRACT FROM AUTHOR]

The efficient and accurate application of deep learning in the remote sensing field largely depends on the pre-processing technology of remote sensing images. Particularly, image fusion is the essential way to achieve the complementarity of the panchromatic band and multispectral bands in high spatial resolution remote sensing images. In this paper, we not only pay attention to the visual effect of fused images, but also focus on the subsequent application effectiveness of information extraction and feature recognition based on fused images. Based on the WorldView-3 images of Tongzhou District of Beijing, we apply the fusion results to conduct the experiments of object recognition of typical urban features based on deep learning. Furthermore, we perform a quantitative analysis for the existing pixel-based mainstream fusion methods of IHS (Intensity-Hue Saturation), PCS (Principal Component Substitution), GS (Gram Schmidt), ELS (Ehlers), HPF (High-Pass Filtering), and HCS (Hyper spherical Color Space) from the perspectives of spectrum, geometric features, and recognition accuracy. The results show that there are apparent differences in visual effect and quantitative index among different fusion methods, and the PCS fusion method has the most satisfying comprehensive effectiveness in the object recognition of land cover (features) based on deep learning. [ABSTRACT FROM AUTHOR]

Information, especially spatial distribution data, related to coastal raft aquaculture is critical to the sustainable development of marine resources and environmental protection. Commercial high spatial resolution satellite imagery can accurately locate raft aquaculture. However, this type of analysis using this expensive imagery requires a large number of images. In contrast, medium resolution satellite imagery, such as Landsat 8 images, are available at no cost, cover large areas with less data volume, and provide acceptable results. Therefore, we used Landsat 8 images to extract the presence of coastal raft aquaculture. Because the high chlorophyll concentration of coastal raft aquaculture areas cause the Normalized Difference Vegetation Index (NDVI) and the edge features to be salient for the water background, we integrated these features into the proposed method. Three sites from north to south in Eastern China were used to validate the method and compare it with our former proposed method using only object-based visually salient NDVI (OBVS-NDVI) features. The new proposed method not only maintains the true positive results of OBVS-NDVI, but also eliminates most false negative results of OBVS-NDVI. Thus, the new proposed method has potential for use in rapid monitoring of coastal raft aquaculture on a large scale. [ABSTRACT FROM AUTHOR]

Remote sensing has been widely used in vegetation cover research but is rarely used for intercropping area monitoring. To investigate the efficiency of Chinese Gaofen satellite imagery, in this study the GF-1 and GF-2 of Moyu County south of the Tarim Basin were studied. Based on Chinese GF-1 and GF-2 satellite imagery features, this study has developed a comprehensive feature extraction and intercropping classification scheme. Textural features derived from a Gray level co-occurrence matrix (GLCM) and vegetation features derived from multi-temporal GF-1 and GF-2 satellites were introduced and combined into three different groups. The rotation forest method was then adopted based on a Support Vector Machine (RoF-SVM), which offers the advantage of using an SVM algorithm and that boosts the diversity of individual base classifiers by a rotation forest. The combined spectral-textural-multitemporal features achieved the best classification result. The results were compared with those of the maximum likelihood classifier, support vector machine and random forest method. It is shown that the RoF-SVM algorithm for the combined spectral-textural-multitemporal features can effectively classify an intercropping area (overall accuracy of 86.87% and kappa coefficient of 0.78), and the classification result effectively eliminated salt and pepper noise. Furthermore, the GF-1 and GF-2 satellite images combined with spectral, textural, and multi-temporal features can provide sufficient information on vegetation cover located in an extremely complex and diverse intercropping area. [ABSTRACT FROM AUTHOR]

Coastal wetland vegetation is a vital component that plays an important role in environmental protection and the maintenance of the ecological balance. As such, the efficient classification of coastal wetland vegetation types is key to the preservation of wetlands. Based on its detailed spatial information, high spatial resolution imagery constitutes an important tool for extracting suitable texture features for improving the accuracy of classification. In this paper, a texture feature, Completed Local Binary Patterns (CLBP), which is highly suitable for face recognition, is presented and applied to vegetation classification using high spatial resolution Pléiades satellite imagery in the central zone of Yancheng National Natural Reservation (YNNR) in Jiangsu, China. To demonstrate the potential of CLBP texture features, Grey Level Co-occurrence Matrix (GLCM) texture features were used to compare the classification. Using spectral data alone and spectral data combined with texture features, the image was classified using a Support Vector Machine (SVM) based on vegetation types. The results show that CLBP and GLCM texture features yielded an accuracy 6.50% higher than that gained when using only spectral information for vegetation classification. However, CLBP showed greater improvement in terms of classification accuracy than GLCM for Spartina alterniflora. Furthermore, for the CLBP features, CLBP_magnitude (CLBP_m) was more effective than CLBP_sign (CLBP_s), CLBP_center (CLBP_c), and CLBP_s/m or CLBP_s/m/c. These findings suggest that the CLBP approach offers potential for vegetation classification in high spatial resolution images. [ABSTRACT FROM AUTHOR]

Recently, many researchers have been dedicated to using convolutional neural networks (CNNs) to extract global-context features (GCFs) for remote-sensing scene classification. Commonly, accurate classification of scenes requires knowledge about both the global context and local objects. However, unlike the natural images in which the objects cover most of the image, objects in remote-sensing images are generally small and decentralized. Thus, it is hard for vanilla CNNs to focus on both global context and small local objects. To address this issue, this paper proposes a novel end-to-end CNN by integrating the GCFs and local-object-level features (LOFs). The proposed network includes two branches, the local object branch (LOB) and global semantic branch (GSB), which are used to generate the LOFs and GCFs, respectively. Then, the concatenation of features extracted from the two branches allows our method to be more discriminative in scene classification. Three challenging benchmark remote-sensing datasets were extensively experimented on; the proposed approach outperformed the existing scene classification methods and achieved state-of-the-art results for all three datasets. [ABSTRACT FROM AUTHOR]