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In the field of object detection, small object detection in remote sensing images is an important and challenging task. Due to limitations in size and resolution, most existing methods often suffer from localization blurring. To address the above problem, this paper proposes a remote sensing small object detection network based on adaptive channel attention and large kernel adaptation. This approach aims to enhance multi-channel information mining and multi-scale feature extraction to alleviate the problem of localization blurring. To enhance the model's focus on the features of small objects in remote sensing at varying scales, this paper introduces an adaptive channel attention block. This block applies adaptive attention weighting based on the input feature dimensions, guiding the model to better focus on local information. To mitigate the loss of local information by large kernel convolutions, a large kernel adaptive block is designed. The block dynamically adjusts the surrounding spatial receptive field based on the context around the detection area, improving the model's ability to extract information around remote sensing small objects. To address the recognition confusion during the sample classification process, a layer batch normalization method is proposed. This method enhances the consistency analysis capabilities of adaptive learning, thereby reducing the decline in the model's classification accuracy caused by sample misclassification. Experiments on the DOTA-v2.0, SODA-A and VisDrone datasets show that the proposed method achieves state-of-the-art performance. [ABSTRACT FROM AUTHOR]

In recent years, classification and identification of Earth's surface materials has been a challenging research topic in the field of earth science and remote sensing (RS). Although deep learning techniques have achieved some results in remote sensing image classification, there are still some challenges for multimodal remote sensing data classification, such as information redundancy between multimodal remote sensing images. In this paper, we propose a multimodal remote sensing data classification method IFF-Net based on irregular feature fusion, called IFF-Net. The IFF-Net architecture utilizes weight-shared residual blocks for feature extraction while maintaining the independent batch normalization (BN) layer. During the training phase, the redundancy of the current channel is determined by evaluating the judgement factor of the BN layer. If this judgment factor falls below a predefined threshold, it indicates that the current channel information is redundant and should be substituted with another channel. Sparse constraints are imposed on some of the judgment factors in order to remove extra channels and enhance generalization. Furthermore, a module for feature normalization and calibration has been devised to leverage the spatial interdependence of multimodal features in order to achieve improved discrimination. Two standard datasets are used in the experiments to validate the effectiveness of the proposed method. The experimental results show that the IFF-NET method proposed in this paper exhibits significantly superior performance compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Target recognition and fine-grained ship classification in remote sensing face challenges of high inter-class similarity and sample scarcity. A transfer fusion-based ship image harmonization algorithm is proposed to overcome these challenges. This algorithm designs a feature transfer fusion strategy based on the combination of a region-aware instantiation and attention mechanism. Adversarial learning is implemented through an image harmony generator and discriminator module to generate realistic remote sensing ship harmony images. Furthermore, the domain encoder and domain discriminator modules are responsible for extracting feature representations of the foreground and background, and further align the ship foreground with remote sensing ocean background features through feature discrimination. Compared with other advanced image conversion techniques, our algorithm delivers more realistic visuals, improving classification accuracy for six ship types by 3% and twelve types by 2.94%, outperforming Sim2RealNet. Finally, a mixed dataset containing data augmentation and harmonizing samples and real data was proposed for the fine-grained classification task of remote sensing ships. Evaluation experiments were conducted on eight typical fine-grained classification algorithms, and the accuracy of the fine-grained classification for all categories of ships was analyzed. The experimental results show that the mixed dataset proposed in this paper effectively alleviates the long-tail problem in real datasets, and the proposed remote sensing ship data augmentation framework performs better than state-of-the-art data augmentation methods in fine-grained ship classification tasks. [ABSTRACT FROM AUTHOR]

Existing remote sensing images of ground objects are difficult to annotate, and building a hyperspectral dataset requires huge resources. To tackle these problems, this paper proposes a new method with low requirements for the scale of the dataset that involves correcting the inter-class differences of hyperspectral images and eliminating the redundant information of spatial–spectral features. Firstly, the algorithm introduces the spatial information of hyperspectral images into the classification task through the superpixel definition based on the entropy rate, which not only reduces the spatial redundancy information of hyperspectral images but also uses the similarity of spatial neighbors to increase the differences between classes and alleviate the differences within classes. Secondly, under the theoretical guidance of similar spectral fluctuation trends of similar objects, a feature extraction method based on fast Fourier transform is proposed to alleviate spectral feature redundancy and further eliminate the inter-class differences of the algorithm. Finally, to verify the improvement effect of the proposed idea on the traditional classification method, the idea was applied to the traditional SVM algorithm, and experiments were carried out based on the PaviaU and Indian Pines hyperspectral datasets. The simulation results show that the proposed (ERS–FFT–SVM) algorithm shows a significant improvement in classification accuracy when compared with the traditional classification algorithm and is able to perform the small sample classification of hyperspectral images. [ABSTRACT FROM AUTHOR]

For the multi-label classification task of remote sensing images (RSIs), it is difficult to accurately extract feature information from complex land covers, and it is easy to generate redundant features by ordinary convolution extraction features. This paper proposes a multi-label classification model for multi-source RSIs that combines dense convolution and an attention mechanism. This method adds fusion channel attention and a spatial attention mechanism to each dense block module of the DenseNet, and the sigmoid activation function replaces the softmax activation function in multi-label classification. The improved model retains the main features of RSIs to the greatest extent and enhances the feature extraction of the images. The model can integrate local features, capture global dependencies, and aggregate contextual information to improve the multi-label land cover classification accuracy of RSIs. We conducted comparative experiments on the SEN12-MS and UC-Merced land cover dataset and analyzed the evaluation indicators. The experimental results show that this method effectively improves the multi-label classification accuracy of RSIs. [ABSTRACT FROM AUTHOR]

With the continuous advancement of remote sensing technology, the semantic segmentation of different ground objects in remote sensing images has become an active research topic. For complex and diverse remote sensing imagery, deep learning methods have the ability to automatically discern features from image data and capture intricate spatial dependencies, thus outperforming traditional image segmentation methods. To address the problems of low segmentation accuracy in remote sensing image semantic segmentation, this paper proposes a new remote sensing image semantic segmentation network, RSLC-Deeplab, based on DeeplabV3+. Firstly, ResNet-50 is used as the backbone feature extraction network, which can extract deep semantic information more effectively and improve the segmentation accuracy. Secondly, the coordinate attention (CA) mechanism is introduced into the model to improve the feature representation generated by the network by embedding position information into the channel attention mechanism, effectively capturing the relationship between position information and channels. Finally, a multi-level feature fusion (MFF) module based on asymmetric convolution is proposed, which captures and refines low-level spatial features using asymmetric convolution and then fuses them with high-level abstract features to mitigate the influence of background noise and restore the lost detailed information in deep features. The experimental results on the WHDLD dataset show that the mean intersection over union (mIoU) of RSLC-Deeplab reached 72.63%, the pixel accuracy (PA) reached 83.49%, and the mean pixel accuracy (mPA) reached 83.72%. Compared to the original DeeplabV3+, the proposed method achieved a 4.13% improvement in mIoU and outperformed the PSP-NET, U-NET, MACU-NET, and DeeplabV3+ networks. [ABSTRACT FROM AUTHOR]

In the process of extracting tailings ponds from large scene remote sensing images, semantic segmentation models usually perform calculations on all small-size remote sensing images segmented by the sliding window method. However, some of these small-size remote sensing images do not have tailings ponds, and their calculations not only affect the model accuracy, but also affect the model speed. For this problem, we proposed a fast tailings pond extraction method (Scene-Classification-Sematic-Segmentation, SC-SS) that couples scene classification and semantic segmentation models. The method can map tailings ponds rapidly and accurately in large scene remote sensing images. There were two parts in the method: a scene classification model, and a semantic segmentation model. Among them, the scene classification model adopted the lightweight network MobileNetv2. With the help of this network, the scenes containing tailings ponds can be quickly screened out from the large scene remote sensing images, and the interference of scenes without tailings ponds can be reduced. The semantic segmentation model used the U-Net model to finely segment objects from the tailings pond scenes. In addition, the encoder of the U-Net model was replaced by the VGG16 network with stronger feature extraction ability, which improves the model's accuracy. In this paper, the Google Earth images of Luanping County were used to create the tailings pond scene classification dataset and tailings pond semantic segmentation dataset, and based on these datasets, the training and testing of models were completed. According to the experimental results, the extraction accuracy (Intersection Over Union, IOU) of the SC-SS model was 93.48%. The extraction accuracy of IOU was 15.12% higher than the U-Net model, while the extraction time was shortened by 35.72%. This research is of great importance to the remote sensing dynamic observation of tailings ponds on a large scale. [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]

Remote sensing scene classification aims to automatically assign proper labels to remote sensing images. Most of the existing deep learning based methods usually consider the interclass and intraclass relationships of the image content for classification. However, these methods rarely consider the hierarchical information of scene labels, as a scene label may belong to hierarchically multi-grained levels. For example, multi-grained level labels may indicate that a remote sensing scene image may belong to the coarse-grained label "transportation land" while also belonging to the fine-grained label "airport". In this paper, to exploit hierarchical label information, we propose an attention-embedding multi-task multi-grained network (AEMMN) for remote sensing scene classification. In the proposed AEMMN, we add a coarse-grained classifier as the first level and a fine-grained classifier as the second level to perform multi-task learning tasks. Additionally, a gradient control module is utilized to control the gradient propagation of two classifiers to suppress the negative transfer caused by the irrelevant features between tasks. In the feature extraction portion, the model uses an ECA module embedding Resnet50 to extract effective features with cross-channel interaction information. Furthermore, an external attention module is exploited to improve the discrimination of fine-grained and coarse-grained features. Experiments were conducted on the NWPU-RESISC45 and the Aerial Image Data Set (AID), and the overall accuracy of the proposed AEMMN is 92.07% on the NWPU-RESISC45 dataset and reached 94.96% on the AID. The results indicate that hierarchical label information can effectively improve the performance of scene classification tasks when categorizing remote sensing imagery. [ABSTRACT FROM AUTHOR]

The hyperspectral feature extraction technique is one of the most popular topics in the remote sensing community. However, most hyperspectral feature extraction methods are based on region-based local information descriptors while neglecting the correlation and dependencies of different homogeneous regions. To alleviate this issue, this paper proposes a multi-view structural feature extraction method to furnish a complete characterization for spectral–spatial structures of different objects, which mainly is made up of the following key steps. First, the spectral number of the original image is reduced with the minimum noise fraction (MNF) method, and a relative total variation is exploited to extract the local structural feature from the dimension reduced data. Then, with the help of a superpixel segmentation technique, the nonlocal structural features from intra-view and inter-view are constructed by considering the intra- and inter-similarities of superpixels. Finally, the local and nonlocal structural features are merged together to form the final image features for classification. Experiments on several real hyperspectral datasets indicate that the proposed method outperforms other state-of-the-art classification methods in terms of visual performance and objective results, especially when the number of training set is limited. [ABSTRACT FROM AUTHOR]

Big data analysis assumes a significant role in Earth observation using remote sensing images, since the explosion of data images from multiple sensors is used in several fields. The traditional data analysis techniques have different limitations on storing and processing massive volumes of data. Besides, big remote sensing data analytics demand sophisticated algorithms based on specific techniques to store to process the data in real-time or in near real-time with high accuracy, efficiency, and high speed. In this paper, we present a method for storing a huge number of heterogeneous satellite images based on Hadoop distributed file system (HDFS) and Apache Spark. We also present how deep learning algorithms such as VGGNet and UNet can be beneficial to big remote sensing data processing for feature extraction and classification. The obtained results prove that our approach outperforms other methods. [ABSTRACT FROM AUTHOR]

Historical maps classification has become an important application in today's scenario of everchanging land boundaries. Historical map changes include the change in boundaries of cities/states, vegetation regions, water bodies and so forth. Change detection in these regions are mainly carried out via satellite images. Hence, an extensive knowledge on satellite image processing is necessary for historical map classification applications. An exhaustive analysis on the merits and demerits of many satellite image processing methods are discussed in this paper. Though several computational methods are available, different methods perform differently for the various satellite image processing applications. Wrong selection of methods will lead to inferior results for a specific application. This work highlights the methods and the suitable satellite imaging methods associated with these applications. Several comparative analyses are also performed in this work to show the suitability of several methods. This work will help support the selection of innovative solutions for the different problems associated with satellite image processing applications. [ABSTRACT FROM AUTHOR]

Computational intelligence is a very active and fruitful research of artificial intelligence with a broad spectrum of applications. Remote sensing data has been a salient field of application of computational intelligence algorithms, both for the exploitation of the data and for the research/ development of new data analysis tools. In this editorial paper we provide the setting of the special issue "Computational Intelligence in Remote Sensing" and an overview of the published papers. The 11 accepted and published papers cover a wide spectrum of applications and computational tools that we try to summarize and put in perspective in this editorial paper. [ABSTRACT FROM AUTHOR]

High-precision landcover classification is a fundamental prerequisite for resource and environmental monitoring and land-use status surveys. Imbued with intricate spatial information and texture features, very high spatial resolution remote sensing images accentuate the divergence between features within the same category, thereby amplifying the complexity of landcover classification. Consequently, semantic segmentation models leveraging deep backbone networks have emerged as stalwarts in landcover classification tasks owing to their adeptness in feature representation. However, the classification efficacy of a solitary backbone network model fluctuates across diverse scenarios and datasets, posing a persistent challenge in the construction or selection of an appropriate backbone network for distinct classification tasks. To elevate the classification performance and bolster the generalization of semantic segmentation models, we propose a novel semantic segmentation network architecture, named the aggregated backbone network (ABNet), for the meticulous landcover classification. ABNet aggregates three prevailing backbone networks (ResNet, HRNet, and VoVNet), distinguished by significant structural disparities, using a same-stage fusion approach. Subsequently, it amalgamates these networks with the Deeplabv3+ head after integrating the convolutional block attention mechanism (CBAM). Notably, this amalgamation harmonizes distinct scale features extracted by the three backbone networks, thus enriching the model's spatial contextual comprehension and expanding its receptive field, thereby facilitating more effective semantic feature extraction across different stages. The convolutional block attention mechanism primarily orchestrates channel adjustments and curtails redundant information within the aggregated feature layers. Ablation experiments demonstrate an enhancement of no less than 3% in the mean intersection over union (mIoU) of ABNet on both the LoveDA and GID15 datasets when compared with a single backbone network model. Furthermore, in contrast to seven classical or state-of-the-art models (UNet, FPN, PSPNet, DANet, CBNet, CCNet, and UPerNet), ABNet evinces excellent segmentation performance across the aforementioned datasets, underscoring the efficiency and robust generalization capabilities of the proposed approach. [ABSTRACT FROM AUTHOR]

The special issue "Tree species diversity mapping" presents research focused on the remote assessment of tree species diversity, using different sensor modalities and platforms. The special issue thereby recognizes that the continued loss of biodiversity poses a great challenge to humanity. Precise and regularly updated baseline information is urgently needed, which is difficult, using field inventories, especially on a large scale. On such scales, remote sensing methods excel. The work presented in the special issue demonstrates the great potential of Earth Observation (EO) for addressing knowledge gaps, as EO provides rich (spectral) information at high revisit frequencies and spatial resolutions. Many tree species can be distinguished well using optical data, in particular, when simultaneously leveraging both the spectral and temporal dimensions. A combination with other sensor modalities can further improve performance. EO approaches are, however, limited by the availability of high-quality reference information. This complicates the task as the collection of field data is labor and time-consuming. To mitigate this limiting factor, resources should be better shared amongst the community. The reliance on in situ data also highlights the need to focus research on the extraction of more permanent (i.e., species-inherent) properties. In this respect, we identify and discuss some inherent limitations of current approaches regarding tree species discrimination. To this end, we offer a more fundamental view on tree species classification based on physical principles. To provide both a summary of the special issue and some stimulating thoughts about possible future research directions, we structured the present communication into four parts. We first introduce the need for biodiversity information, followed by a summary of all 19 articles published within the special issue. The articles are ordered by the number of species investigated. Next, we provide a short summary of the main outputs. To stimulate further research and discussion within the scientific community, we conclude this communication by offering a more fundamental view on tree species classification based on EO data and its biophysical foundations. In particular, we purport that species can possibly be more robustly identified if we classify/identify them in the biophysical feature space and not in the spectral-temporal feature space. This involves the creation and inversion of so-called physically-based radiative transfer models (RTM), which take hyper/multispectral observations together with their observation geometry (as well as other priors), and project these into biophysical variables such as chlorophyll content and LAI etc. The perceived advantage of such an approach is that the generalizability (and scalability) of EO based classifications will increase, as the temporal trajectory of species in the biophysical parameter space is probably more robust compared to the sole analysis of spectral data, which—amongst other perturbing factors—also depend on site/time specific illumination geometry. [ABSTRACT FROM AUTHOR]

The fusion of the hyperspectral image (HSI) and the light detecting and ranging (LiDAR) data has a wide range of applications. This paper proposes a novel feature fusion method for urban area classification, namely the relative total variation structure analysis (RTVSA), to combine various features derived from HSI and LiDAR data. In the feature extraction stage, a variety of high-performance methods including the extended multi-attribute profile, Gabor filter, and local binary pattern are used to extract the features of the input data. The relative total variation is then applied to remove useless texture information of the processed data. Finally, nonparametric weighted feature extraction is adopted to reduce the dimensions. Random forest and convolutional neural networks are utilized to evaluate the fusion images. Experiments conducted on two urban Houston University datasets (including Houston 2012 and the training portion of Houston 2017) demonstrate that the proposed method can extract the structural correlation from heterogeneous data, withstand a noise well, and improve the land cover classification accuracy. [ABSTRACT FROM AUTHOR]

High-resolution optical remote sensing image classification is an important research direction in the field of computer vision. It is difficult to extract the rich semantic information from remote sensing images with many objects. In this paper, a multiscale self-adaptive attention network (MSAA-Net) is proposed for the optical remote sensing image classification, which includes multiscale feature extraction, adaptive information fusion, and classification. In the first part, two parallel convolution blocks with different receptive fields are adopted to capture multiscale features. Then, the squeeze process is used to obtain global information and the excitation process is used to learn the weights in different channels, which can adaptively select useful information from multiscale features. Furthermore, the high-level features are classified by many residual blocks with an attention mechanism and a fully connected layer. Experiments were conducted using the UC Merced, NWPU, and the Google SIRI-WHU datasets. Compared to the state-of-the-art methods, the MSAA-Net has great effect and robustness, with average accuracies of 94.52%, 95.01%, and 95.21% on the three widely used remote sensing datasets. [ABSTRACT FROM AUTHOR]

Land-cover classification on very high resolution data (decimetre-level) is a well-studied yet challenging problem in remote sensing data processing. Most of the existing works focus on using images with orthographic view or orthophotos with the associated digital surface models (DSMs). However, the use of the nowadays widely-available oblique images to support such a task is not sufficiently investigated. In the effort of identifying different land-cover classes, it is intuitive that information of side-views obtained from the oblique can be of great help, yet how this can be technically achieved is challenging due to the complex geometric association between the side and top views. We aim to address these challenges in this paper by proposing a framework with enhanced classification results, leveraging the use of orthophoto, digital surface models and oblique images. The proposed method contains a classic two-step of (1) feature extraction and (2) a classification approach, in which the key contribution is a feature extraction algorithm that performs simplified geometric association between top-view segments (from orthophoto) and side-view planes (from projected oblique images), and joint statistical feature extraction. Our experiment on five test sites showed that the side-view information could steadily improve the classification accuracy with both kinds of training samples (1.1% and 5.6% for evenly distributed and non-evenly distributed samples, separately). Additionally, by testing the classifier at a large and untrained site, adding side-view information showed a total of 26.2% accuracy improvement of the above-ground objects, which demonstrates the strong generalization ability of the side-view features. [ABSTRACT FROM AUTHOR]

The current literature of remote sensing (RS) scene classification shows that state-of-the-art results are achieved using feature extraction methods, where convolutional neural networks (CNNs) (mostly VGG16 with 138.36 M parameters) are used as feature extractors and then simple to complex handcrafted modules are added for additional feature learning and classification, thus coming back to feature engineering. In this paper, we revisit the fine-tuning approach for deeper networks (GoogLeNet and Beyond) and show that it has not been well exploited due to the negative effect of the vanishing gradient problem encountered when transferring knowledge to small datasets. The aim of this work is two-fold. Firstly, we provide best practices for fine-tuning pre-trained CNNs using the root-mean-square propagation (RMSprop) method. Secondly, we propose a simple yet effective solution for tackling the vanishing gradient problem by injecting gradients at an earlier layer of the network using an auxiliary classification loss function. Then, we fine-tune the resulting regularized network by optimizing both the primary and auxiliary losses. As for pre-trained CNNs, we consider in this work inception-based networks and EfficientNets with small weights: GoogLeNet (7 M) and EfficientNet-B0 (5.3 M) and their deeper versions Inception-v3 (23.83 M) and EfficientNet-B3 (12 M), respectively. The former networks have been used previously in the context of RS and yielded low accuracies compared to VGG16, while the latter are new state-of-the-art models. Extensive experimental results on several benchmark datasets reveal clearly that if fine-tuning is done in an appropriate way, it can settle new state-of-the-art results with low computational cost. [ABSTRACT FROM AUTHOR]

There are normally three main steps to carrying out the labeling of airborne laser scanning (ALS) point clouds. The first step is to use appropriate primitives to represent the scanning scenes, the second is to calculate the discriminative features of each primitive, and the third is to introduce a classifier to label the point clouds. This paper investigates multiple primitives to effectively represent scenes and exploit their geometric relationships. Relationships are graded according to the properties of related primitives. Then, based on initial labeling results, a novel, hierarchical, and optimal strategy is developed to optimize semantic labeling results. The proposed approach was tested using two sets of representative ALS point clouds, namely the Vaihingen datasets and Hong Kong's Central District dataset. The results were compared with those generated by other typical methods in previous work. Quantitative assessments for the two experimental datasets showed that the performance of the proposed approach was superior to reference methods in both datasets. The scores for correctness attained over 98% in all cases of the Vaihingen datasets and up to 96% in the Hong Kong dataset. The results reveal that our approach of labeling different classes in terms of ALS point clouds is robust and bears significance for future applications, such as 3D modeling and change detection from point clouds. [ABSTRACT FROM AUTHOR]

In this paper, we develop a hyperspectral feature extraction method called sparse and smooth low-rank analysis (SSLRA). First, we propose a new low-rank model for hyperspectral images (HSIs) where we decompose the HSI into smooth and sparse components. Then, these components are simultaneously estimated using a nonconvex constrained penalized cost function (CPCF). The proposed CPCF exploits total variation penalty, ℓ 1 penalty, and an orthogonality constraint. The total variation penalty is used to promote piecewise smoothness, and, therefore, it extracts spatial (local neighborhood) information. The ℓ 1 penalty encourages sparse and spatial structures. Additionally, we show that this new type of decomposition improves the classification of the HSIs. In the experiments, SSLRA was applied on the Houston (urban) and the Trento (rural) datasets. The extracted features were used as an input into a classifier (either support vector machines (SVM) or random forest (RF)) to produce the final classification map. The results confirm improvement in classification accuracy compared to the state-of-the-art feature extraction approaches. [ABSTRACT FROM AUTHOR]

Landcover classification is an important application in remote sensing, but it is always a challenge to distinguish different features with similar characteristics or large-scale differences. Some deep learning networks, such as UperNet, PSPNet, and DANet, use pyramid pooling and attention mechanisms to improve their abilities in multi-scale features extraction. However, due to the neglect of low-level features contained in the underlying network and the information differences between feature maps, it is difficult to identify small-scale objects. Thus, we propose a novel image segmentation network, named HFENet, for mining multi-level semantic information. Like the UperNet, HFENet adopts a top-down horizontal connection architecture while includes two improved modules, the HFE and the MFF. According to the characteristics of different levels of semantic information, HFE module reconstructs the feature extraction part by introducing an attention mechanism and pyramid pooling module to fully mine semantic information. With the help of a channel attention mechanism, MFF module up-samples and re-weights the feature maps to fuse them and enhance the expression ability of multi-scale features. Ablation studies and comparative experiments between HFENet and seven state-of-the-art models (U-Net, DeepLabv3+, PSPNet, FCN, UperNet, DANet and SegNet) are conducted with a self-labeled GF-2 remote sensing image dataset (MZData) and two open datasets landcover.ai and WHU building dataset. The results show that HFENet on three datasets with six evaluation metrics (mIoU, FWIoU, PA, mP, mRecall and mF1) are better than the other models and the mIoU is improved 7.41–10.60% on MZData, 1.17–11.57% on WHU building dataset and 0.93–4.31% on landcover.ai. HFENet can perform better in the task of refining the semantic segmentation of remote sensing images. [ABSTRACT FROM AUTHOR]

Remote sensing image scene classification (RSISC) is an active task in the remote sensing community and has attracted great attention due to its wide applications. Recently, the deep convolutional neural networks (CNNs)-based methods have witnessed a remarkable breakthrough in performance of remote sensing image scene classification. However, the problem that the feature representation is not discriminative enough still exists, which is mainly caused by the characteristic of inter-class similarity and intra-class diversity. In this paper, we propose an efficient end-to-end local-global-fusion feature extraction (LGFFE) network for a more discriminative feature representation. Specifically, global and local features are extracted from channel and spatial dimensions respectively, based on a high-level feature map from deep CNNs. For the local features, a novel recurrent neural network (RNN)-based attention module is first proposed to capture the spatial layout information and context information across different regions. Gated recurrent units (GRUs) is then exploited to generate the important weight of each region by taking a sequence of features from image patches as input. A reweighed regional feature representation can be obtained by focusing on the key region. Then, the final feature representation can be acquired by fusing the local and global features. The whole process of feature extraction and feature fusion can be trained in an end-to-end manner. Finally, extensive experiments have been conducted on four public and widely used datasets and experimental results show that our method LGFFE outperforms baseline methods and achieves state-of-the-art results. [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]

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]

Traditional co-word networks do not discriminate keywords of researcher interest from general keywords. Co-word networks are therefore often too general to provide knowledge if interest to domain experts. Inspired by the recent work that uses an automatic method to identify the questions of interest to researchers like "problems" and "solutions", we try to answer a similar question "what sensors can be used for what kind of applications", which is great interest in sensor- related fields. By generalizing the specific questions as "questions of interest", we built a knowledge network considering researcher interest, called bipartite network of interest (BNOI). Different from a co-word approaches using accurate keywords from a list, BNOI uses classification models to find possible entities of interest. A total of nine feature extraction methods including N-grams, Word2Vec, BERT, etc. were used to extract features to train the classification models, including naïve Bayes (NB), support vector machines (SVM) and logistic regression (LR). In addition, a multi-feature fusion strategy and a voting principle (VP) method are applied to assemble the capability of the features and the classification models. Using the abstract text data of 350 remote sensing articles, features are extracted and the models trained. The experiment results show that after removing the biased words and using the ten-fold cross-validation method, the F-measure of "sensors" and "applications" are 93.2% and 85.5%, respectively. It is thus demonstrated that researcher questions of interest can be better answered by the constructed BNOI based on classification results, comparedwith the traditional co-word network approach. [ABSTRACT FROM AUTHOR]

In recent years, hyperspectral images (HSIs) have attained considerable attention in computer vision (CV) due to their wide utility in remote sensing. Unlike images with three or lesser channels, HSIs have a large number of spectral bands. Recent works demonstrate the use of modern deep learning based CV techniques like convolutional neural networks (CNNs) for analyzing HSI. CNNs have receptive fields (RFs) fueled by learnable weights, which are trained to extract useful features from images. In this work, a novel multi-receptive CNN module called GhoMR is proposed for HSI classification. GhoMR utilizes blocks containing several RFs, extracting features in a residual fashion. Each RF extracts features which are used by other RFs to extract more complex features in a hierarchical manner. However, the higher the number of RFs, the greater the associated weights, thus heavier is the network. Most complex architectures suffer from this shortcoming. To tackle this, the recently found Ghost module is used as the basic building unit. Ghost modules address the feature redundancy in CNNs by extracting only limited features and performing cheap transformations on them, thus reducing the overall parameters in the network. To test the discriminative potential of GhoMR, a simple network called GhoMR-Net is constructed using GhoMR modules, and experiments are performed on three public HSI data sets—Indian Pines, University of Pavia, and Salinas Scene. The classification performance is measured using three metrics—overall accuracy (OA), Kappa coefficient (Kappa), and average accuracy (AA). Comparisons with ten state-of-the-art architectures are shown to demonstrate the effectiveness of the method further. Although lightweight, the proposed GhoMR-Net provides comparable or better performance than other networks. The PyTorch code for this study is made available at the iamarijit/GhoMR GitHub repository. [ABSTRACT FROM AUTHOR]

Recently, extended multi-attribute profiles (EMAPs) have attracted much attention due to its good performance while applied to remote sensing images feature extraction and classification. Since the EMAPs connect multiple attribute features without considering the pixel-based Hyperspectral Image (HSI) classification, homogeneous regions may become unsmooth due to the noise to be introduced. To tackle this problem, we propose the weighted EMAPs (WEMAPs) to reduce the noise and smoothen the homogeneous regions based on weighted mean filter (WMF). Then, we construct multiscale WEMAPs to product multiscale feature in order to extract different spatial structures of the HSI and produce better classification results. Finally, a new joint decision fusion and feature fusion (JDFFF) framework is proposed based on the decision fusion (DF) and the multiscale WEMAPs (MWEMAPs) based on extreme learning machine (ELM) classifier. That is, the classification results from various scales are combined into a final one with ELM to perform the HSI classification. Experiment results show that the proposed algorithm significantly outperforms many state-of-the-art HSI classification algorithms. [ABSTRACT FROM AUTHOR]

Point cloud classification is an important task in point cloud data analysis. Traditional point cloud classification is conducted primarily on the basis of specific handcrafted features with a specific classifier and is often capable of producing satisfactory results. However, the extraction of crucial handcrafted features hinges on sufficient knowledge of the field and substantial experience. In contrast, while powerful deep learning algorithms possess the ability to learn features automatically, it normally requires complex network architecture and a considerable amount of calculation time to attain better accuracy of classification. In order to combine the advantages of both the methods, in this study, we integrated the handcrafted features, whose benefits were confirmed by previous studies, into a deep learning network, in the hopes of solving the problem of insufficient extraction of specific features and enabling the network to recognise other effective features through automatic learning. This was done to achieve the performance of a complex model by using a simple model and fulfil the application requirements of the remote sensing domain. As indicated by the experimental results, the integration of handcrafted features into the simple and fast-calculating PointNet model could generate a classification result that bore comparison with that generated by a complex network model such as PointNet++ or KPConv. [ABSTRACT FROM AUTHOR]

In recent years, the development of convolutional neural networks (CNNs) has promoted continuous progress in scene classification of remote sensing images. Compared with natural image datasets, however, the acquisition of remote sensing scene images is more difficult, and consequently the scale of remote sensing image datasets is generally small. In addition, many problems related to small objects and complex backgrounds arise in remote sensing image scenes, presenting great challenges for CNN-based recognition methods. In this article, to improve the feature extraction ability and generalization ability of such models and to enable better use of the information contained in the original remote sensing images, we introduce a multitask learning framework which combines the tasks of self-supervised learning and scene classification. Unlike previous multitask methods, we adopt a new mixup loss strategy to combine the two tasks with dynamic weight. The proposed multitask learning framework empowers a deep neural network to learn more discriminative features without increasing the amounts of parameters. Comprehensive experiments were conducted on four representative remote sensing scene classification datasets. We achieved state-of-the-art performance, with average accuracies of 94.21%, 96.89%, 99.11%, and 98.98% on the NWPU, AID, UC Merced, and WHU-RS19 datasets, respectively. The experimental results and visualizations show that our proposed method can learn more discriminative features and simultaneously encode orientation information while effectively improving the accuracy of remote sensing scene classification. [ABSTRACT FROM AUTHOR]

Remote sensing images classification is the key technology for monitoring forest changes. Texture features have been demonstrated to have better effectiveness than spectral features in the improvement of the classification accuracy. The accuracy of extracting texture information by window-based method depends on the choice of the window size. Moreover, the size should ideally match the spatial scale of the object or class under consideration. However, most of the existing texture feature extraction methods are all based on a single window and do not adequately consider the scale of different objects. Our first proposition is to use a composite window for extracting texture features, which is a small window surrounded by a larger window. Our second proposition is to reinforce the performance of the trained ensemble classifier by training it using only the most important features. Considering the advantages of random forest classifier, such as fast training speed and few parameters, these features feed this classifier. Measures of feature importance are estimated along with the growth of the base classifiers, here decision trees. We aim to classify each pixel of the forest images disturbed by hurricanes and fires in three classes, damaged, not damaged, or unknown, as this could be used to compute time-dependent aggregates. In this study, two research areas—Nezer Forest in France and Blue Mountain Forest in Australia—are utilized to validating the effectiveness of the proposed method. Numerical simulations show increased performance and improved monitoring ability of forest disturbance when using these two propositions. When compared with the reference methods, the best increase of the overall accuracy obtained by the proposed algorithm is 4.77% and 2.96% on the Nezer forest data and Blue Mountain forest data, respectively. [ABSTRACT FROM AUTHOR]

Scene classification relying on images is essential in many systems and applications related to remote sensing. The scientific interest in scene classification from remotely collected images is increasing, and many datasets and algorithms are being developed. The introduction of convolutional neural networks (CNN) and other deep learning techniques contributed to vast improvements in the accuracy of image scene classification in such systems. To classify the scene from areal images, we used a two-stream deep architecture. We performed the first part of the classification, the feature extraction, using pre-trained CNN that extracts deep features of aerial images from different network layers: the average pooling layer or some of the previous convolutional layers. Next, we applied feature concatenation on extracted features from various neural networks, after dimensionality reduction was performed on enormous feature vectors. We experimented extensively with different CNN architectures, to get optimal results. Finally, we used the Support Vector Machine (SVM) for the classification of the concatenated features. The competitiveness of the examined technique was evaluated on two real-world datasets: UC Merced and WHU-RS. The obtained classification accuracies demonstrate that the considered method has competitive results compared to other cutting-edge techniques. [ABSTRACT FROM AUTHOR]

The scene classification of a remote sensing image has been widely used in various fields as an important task of understanding the content of a remote sensing image. Specially, a high-resolution remote sensing scene contains rich information and complex content. Considering that the scene content in a remote sensing image is very tight to the spatial relationship characteristics, how to design an effective feature extraction network directly decides the quality of classification by fully mining the spatial information in a high-resolution remote sensing image. In recent years, convolutional neural networks (CNNs) have achieved excellent performance in remote sensing image classification, especially the residual dense network (RDN) as one of the representative networks of CNN, which shows a stronger feature learning ability as it fully utilizes all the convolutional layer information. Therefore, we design an RDN based on channel-spatial attention for scene classification of a high-resolution remote sensing image. First, multi-layer convolutional features are fused with residual dense blocks. Then, a channel-spatial attention module is added to obtain more effective feature representation. Finally, softmax classifier is applied to classify the scene after adopting data augmentation strategy for meeting the training requirements of the network parameters. Five experiments are conducted on the UC Merced Land-Use Dataset (UCM) and Aerial Image Dataset (AID), and the competitive results demonstrate that our method can extract more effective features and is more conducive to classifying a scene. [ABSTRACT FROM AUTHOR]

The use of Convolutional Neural Networks (CNNs) to solve Domain Adaptation (DA) image classification problems in the context of remote sensing has proven to provide good results but at high computational cost. To avoid this problem, a deep learning network for DA in remote sensing hyperspectral images called TCANet is proposed. As a standard CNN, TCANet consists of several stages built based on convolutional filters that operate on patches of the hyperspectral image. Unlike the former, the coefficients of the filter are obtained through Transfer Component Analysis (TCA). This approach has two advantages: firstly, TCANet does not require training based on backpropagation, since TCA is itself a learning method that obtains the filter coefficients directly from the input data. Second, DA is performed on the fly since TCA, in addition to performing dimensional reduction, obtains components that minimize the difference in distributions of data in the different domains corresponding to the source and target images. To build an operating scheme, TCANet includes an initial stage that exploits the spatial information by providing patches around each sample as input data to the network. An output stage performing feature extraction that introduces sufficient invariance and robustness in the final features is also included. Since TCA is sensitive to normalization, to reduce the difference between source and target domains, a previous unsupervised domain shift minimization algorithm consisting of applying conditional correlation alignment (CCA) is conditionally applied. The results of a classification scheme based on CCA and TCANet show that the DA technique proposed outperforms other more complex DA techniques. [ABSTRACT FROM AUTHOR]

In recent years, remote sensing researchers have investigated the use of different modalities (or combinations of modalities) for classification tasks. Such modalities can be extracted via a diverse range of sensors and images. Currently, there are no (or only a few) studies that have been done to increase the land cover classification accuracy via unmanned aerial vehicle (UAV)–digital surface model (DSM) fused datasets. Therefore, this study looks at improving the accuracy of these datasets by exploiting convolutional neural networks (CNNs). In this work, we focus on the fusion of DSM and UAV images for land use/land cover mapping via classification into seven classes: bare land, buildings, dense vegetation/trees, grassland, paved roads, shadows, and water bodies. Specifically, we investigated the effectiveness of the two datasets with the aim of inspecting whether the fused DSM yields remarkable outcomes for land cover classification. The datasets were: (i) only orthomosaic image data (Red, Green and Blue channel data), and (ii) a fusion of the orthomosaic image and DSM data, where the final classification was performed using a CNN. CNN, as a classification method, is promising due to hierarchical learning structure, regulating and weight sharing with respect to training data, generalization, optimization and parameters reduction, automatic feature extraction and robust discrimination ability with high performance. The experimental results show that a CNN trained on the fused dataset obtains better results with Kappa index of ~0.98, an average accuracy of 0.97 and final overall accuracy of 0.98. Comparing accuracies between the CNN with DSM result and the CNN without DSM result for the overall accuracy, average accuracy and Kappa index revealed an improvement of 1.2%, 1.8% and 1.5%, respectively. Accordingly, adding the heights of features such as buildings and trees improved the differentiation between vegetation specifically where plants were dense. [ABSTRACT FROM AUTHOR]

With the development of high-resolution optical sensors, the classification of ground objects combined with multivariate optical sensors is a hot topic at present. Deep learning methods, such as convolutional neural networks, are applied to feature extraction and classification. In this work, a novel deep belief network (DBN) hyperspectral image classification method based on multivariate optical sensors and stacked by restricted Boltzmann machines is proposed. We introduced the DBN framework to classify spatial hyperspectral sensor data on the basis of DBN. Then, the improved method (combination of spectral and spatial information) was verified. After unsupervised pretraining and supervised fine-tuning, the DBN model could successfully learn features. Additionally, we added a logistic regression layer that could classify the hyperspectral images. Moreover, the proposed training method, which fuses spectral and spatial information, was tested over the Indian Pines and Pavia University datasets. The advantages of this method over traditional methods are as follows: (1) the network has deep structure and the ability of feature extraction is stronger than traditional classifiers; (2) experimental results indicate that our method outperforms traditional classification and other deep learning approaches. [ABSTRACT FROM AUTHOR]