Forest tree species classification has great significance for sustainable development of forest resource. Multi-source remote sensing data provides abundant temporal, spatial, and spectral information for tree species classification. However, there lacks tree species classification methods which comprehensively capture and fuse spatio-temporal-spectral information. Therefore, a tree species classification method based on deep ensemble learning of multi-source spatio-temporal-spectral remote sensing data is proposed. Firstly, multi-temporal, high-resolution and hyperspectral data are utilized for training temporal, spatial, and spectral deep networks. Furtherly, deep ensemble learning is developed for fusion of spatio-temporal-spectral network outputs, where weighted fusion is implemented via dynamic weight optimization based on the spatio-temporal-spatial features. Experimental results indicate that the importance of temporal features is higher than that of spatial information, and spectral networks perform best among all network structures. After the spatio-temporal-spectral ensemble learning, the performance of tree species classification is further improved, and the overall accuracy of the proposed method reaches above 90%. The proposed algorithm realizes precise and fine-scale tree species classification, and provides technique support for the monitoring and conservation of forest resource.

Semantic change detection (SCD) endeavors to identify land-cover changes from multitemporal remote sensing images, providing essential information for various applications. Nevertheless, conventional supervised SCD methods necessitate extensive pixel-level annotations, limiting their applicability. The capability of self-supervised methods to learn feature representations with large amounts of unlabeled data and minimal annotation, and to achieve superior performance, has made them one of the hot topics in remote sensing. However, most self-supervised methods in remote sensing are primarily designed to learn general semantic representations of images, which limits their effectiveness for tasks like SCD that require the analysis of complex semantic transformations. To address this, we propose a multistage, multitask, and multilevel self-supervised network, named S3CD, that learns semantic changes from bi-temporal remote sensing images across scene, pixel, and prototype levels in two stages. In particular, in Stage 2, the network enhances the robustness of SCD by learning semantic consistency within the semantic stable categories across different temporal and capturing the temporal patterns of semantic change categories. We evaluate S3CD on two widely used remote sensing change detection (CD) datasets, where it outperformed state-of-the-art self-supervised and supervised SCD methods. Notably, in the binary CD (BCD) task (i.e., detecting the locations of changes), S3CD also outperforms most supervised learning methods. Therefore, this approach facilitates the application of self-supervised learning in the field of remote sensing CD.

Semantic segmentation techniques for extracting building footprints from high-resolution remote sensing images have been widely used in many fields such as urban planning. However, large-scale building extraction demands a higher diversity in training samples. In this paper, we construct a Global Building Semantic Segmentation (GBSS) dataset (The dataset will be released), which comprises 116.9k pairs of samples (about 742k buildings) from six continents. There are significant variations in building samples in terms of size and style, making the dataset a more challenging benchmark for evaluating the generalization and robustness of building semantic segmentation models. We validated through quantitative and qualitative comparisons between different datasets, and further confirmed the potential application in the field of transfer learning by conducting experiments on subsets.

Recent advancements in fully-supervised change detection (CD) have been notable, yet applying CD effectively in unlabeled scenarios remains a challenge. Our study focuses on domain adaptation of change detection (DACD), transferring knowledge from a labeled dataset (source domain) to an unlabeled dataset (target domain). However, we’ve found that this transfer often leads to reduced discrimination between change and non-change classes because of cross-domain discrepancy, adversely affecting CD performance in the target domain. Aiming at it, we propose a source discriminative learning (SDL) method to learn more discriminative interclass feature representations in the source domain, which can improve the transferability of change knowledge across different CD domains. To evaluate its effectiveness, we established two cross-domain CD scenarios using well-known CD datasets and applied our SDL method to them. The experimental results affirm that our method effectively boosts DACD performance.

Exposed surface for buildings (ESB), which refers to exposed surfaces with traces of building construction, often leads to urban dust. Accurate ESB detection is important for planning urban development and improving urban environment. Fine-grained monitoring of ESB typically needs massive high-quality pixel-level labels, which are demanding and expensive. In contrast, obtaining cost-efficient image-level labels is more promising. Most image-level weakly supervised methods can extract pixel-level pseudo labels using the class activation map (CAM) generated by the classification network. Subsequently, these labels are applied to train the semantic segmentation network. However, the CAM is easy to miss fine-grained information, which leads to label noise. Moreover, the downsampling in the segmentation networks will further loss the spatial information. Furthermore, the sparse distribution and irregular shape of ESB pose additional challenges. Given these problems, we propose a stepwise refining image-level weakly supervised semantic segmentation method (SRIWS): 1) we introduce a new data augmentation method called SRMix to oversample the classification dataset; 2) we propose a two-branch network with a superpixel pooling layer (SPNet) as the semantic segmentation network to capture both global semantic information and spatial details; and 3) to alleviate the impact of potential noise in the initial labels, we design the high-confidence sample filtering operation (HSF) during the SPNet training. The evaluation experiments for the SRIWS were performed on three datasets. The results confirm that our proposed SRIWS presents a superior performance in recognizing ESB compared with existing state-of-the-art methods. In addition, numerous ablation experimental results indicate the effectiveness and robustness of our SRIWS.

We propose a novel network for Multi-View Stereo reconstruction in the field of remote sensing, which considers Clustering-based Semantic Consistency into depth estimation optimization, referred to as CSC-MVS. In this approach, high-level semantic information acquired from multiple views is utilized to construct semantic consistency and assist in guiding the optimization of the MVS network. Specifically, the Non-negative Matrix Factorization (NMF) branch and the Deep Spectral Decomposition (DSD) branch, are designed to generate local and global semantic guidance, respectively. We then propose an uncertainty multi-task optimization method to adaptively combine matching and semantic metrics. The performance of CSC-MVS is evaluated on representative benchmarks, including the WHU TLC dataset and LuoJia-MVS dataset, demonstrating its effectiveness and generality across diverse remote sensing scenarios. Comprehensive experimental results show that our CSC-MVS significantly improves the performance of various MVS baseline networks and achieves notable accuracy in depth reconstruction. We also conduct ablation studies to validate the rationality of each component, and sensitivity analysis to confirm the robustness and adaptability of our proposed method. The code is available at https://github.com/zsl-whu/csc-mvs.

Most of the existing deep learning-based hyperspectral image (HSI) classification algorithms are based on supervised learning, where large number of annotated labels with high acquisition cost are required. Self-supervised learning (SSL) methods can learn abundant representations using large amount of unlabeled data, thereby reducing the reliability of labels. Particularly, SSL based on Masked Image Modeling (MIM) can extract fine-grained features, which is well-suited for HSI classification as a pixel-level interpretation task. However, MIM has scarcely been investigated in HSI classification. Current algorithms lack a comprehensive consideration of the multiscale spectral-spatial characteristics of HSI when constructing the pre-training task, and there exists high computational cost and redundancy when applied to large-scale HSI. Therefore, this paper develops an SSL framework based on Spectral-Spatial Hierarchical Masked Modeling (S2HM2) for large-scale HSI classification. Considering the spectral-spatial characteristics of HSI, 3D masking strategy and spectral-spatial consistency loss are proposed to construct MIM task. To fully exploit features at each scale, hierarchical 3D Feature Pyramid Network (3D-FPN) is designed as decoder for both pre-text and downstream tasks in a “pixel-to-pixel” manner. In addition, Multi-Scale Masked Feature Modeling (MS-MFM) task is proposed to further facilitate the multiscale feature learning. The SSL pre-training is guided by both MIM and MS-MFM. Experimental results on two large-scale hyperspectral datasets, i.e., WHU-OHS and WHU-H2SR, demonstrate the superiority of the proposed method. Furthermore, transfer learning experiments are conducted on a variety of hyperspectral datasets, where classification accuracies are boosted in most of the scenarios. Source code will be made available at https://github.com/tulilin/S2HM2.

Obtaining timely and reliable built-up area (BUA) information across extensive geographical zones holds crucial significance for understanding environmental change and human activities. BUAs often exhibit detailed textures and structures in high-resolution imagery but also present strong heterogeneity. Current methods for BUA extraction primarily relied on planar information from single-view imagery, struggling to effectively capture the 3-D attributes of urban landscapes. Therefore, to address this challenge, this article proposes a cross-angle propagation network (CAPNet) based on multiview remote sensing stereo observation imagery. Our contributions are threefold: 1) we propose the cross-angle fusion module (CAFM) to exploit BUA’s complementary spatial-spectral-angular context across different viewing angles. This module leverages attention mechanisms for the automated acquisition of multiangle feature representation learning from diverse angle combinations. 2) We propose a multiangular propagation decoder (MAPD) that pioneers the exploration of gradually propagating multiangle disparity information through bidirectional-adjacent feature fusion across hierarchical levels. 3) We construct a large-scale, high-resolution multiview BUA (MVBA) dataset over China’s 41 major cities based on the ZY-3 satellites. Extensive experiment results on MVBA and the public WV-3 multiview semantic stereo datasets verify CAPNet’s superiority to existing state-of-the-art (SOTA) models, on preserving overall BUA shape, edge, and internal structures. The dataset and the source code of CAPNet will be publicly available athttps://github.com/zuo-ux/Cross-Angle-Propagation-Network.

Multiview stereo (MVS) aerial image depth estimation is a research frontier in the remote sensing field. Recent deep learning-based advances in close-range object reconstruction have suggested the great potential of this approach. Meanwhile, the deformation problem and the scale variation issue are also worthy of attention. These characteristics of aerial images limit the applicability of the current methods for aerial image depth estimation. Moreover, there are few available benchmark datasets for aerial image depth estimation. In this regard, this article describes a new benchmark dataset called the LuoJia-MVS dataset (https://irsip.whu.edu.cn/resources/resources_en_v2.php), as well as a new deep neural network known as the hierarchical deformable cascade MVS network (HDC-MVSNet). The LuoJia-MVS dataset contains 7972 five-view images with a spatial resolution of 10 cm, pixel-wise depths, and precise camera parameters, and was generated from an accurate digital surface model (DSM) built from thousands of stereo aerial images. In the HDC-MVSNet network, a new full-scale feature pyramid extraction module, a hierarchical set of 3-D convolutional blocks, and “true 3-D” deformable 3-D convolutional layers are specifically designed by considering the aforementioned characteristics of aerial images. Overall and ablation experiments on the WHU and LuoJia-MVS datasets validated the superiority of HDC-MVSNet over the current state-of-the-art MVS depth estimation methods and confirmed that the newly built dataset can provide an effective benchmark.

Change detection of remote sensing images has a wide range of applications in many fields. In recent years, deep learning has become one of the most powerful tools for remote sensing change detection, thanks to its excellent feature learning capability. However, most deep learning methods require a lot of labeled data for the training, which is time-consuming and labor-intensive. Recently, a new learning paradigm—self-supervised learning—has become one of the hot topics in the field of change detection due to its ability to learn feature representations by training with a large amount of unlabeled data and without a large number of sample annotations. However, the existing methods for self-supervised learning are usually designed for natural image processing and are less considered for change detection in more complex scenes (e.g., remote sensing imagery). Therefore, in this paper, we propose a novel network by fusing temporal and differential information for self-supervised contrastive learning change detection, namely TD-SSCD. Specifically, TD-SSCD aims to mine information from the bi-temporal images and their differential images in a self-supervised learning framework, and it gradually learns the potential correlations between them through an alternating iteration learning strategy. The experimental results based on the OSCD and SZTAKI datasets show that the proposed method outperforms the current state-of-the-art unsupervised and self-supervised change detection methods. Benefiting from pre-training on unlabeled samples, the method closes the gap between unsupervised and supervised change detection.

In this article, a deep neural network for semantic segmentation of high-resolution remote sensing images is proposed for urban construction land classification. The network follows a high-resolution network (HRNet) architecture. Specifically, a directional self-attention on the paths of different resolutions is proposed, aiming to correct the directional bias caused by the attention of strip windows during the model learning, while also reducing the computational complexity, and allowing the model to improve both the accuracy and the speed. At the end of the network, a distributed alignment module with spatial information is constructed to train additional learnable parameters, to adjust the biased decision boundaries through a two-stage learning strategy, and alleviate the problem of accuracy degradation due to the unbalanced training data. We tested the proposed method and compared it with the current state-of-the-art (SOTA) semantic segmentation methods on the Luojia-fine-grained land cover (FGLC) dataset and the Wuhan Dense Labeling Dataset (WHDLD), and the proposed one obtained the best performance. We also verified the effectiveness of each component of the network through ablation experiments. The code and model will be available athttps://github.com/Zhzhyd/DWin-HRFormer.

Change detection (CD) has received raising attention for its broad application value. However, traditional fully supervised CD methods have a huge demand for pixel-level annotations, which are laborious and even impossible in some few-shot scenarios. Recently, several semisupervised CD (SSCD) methods have been proposed to utilize numerous unlabeled remote sensing image (RSI) pairs, which can largely reduce the annotation dependence. These methods are mainly based on: 1) adversarial learning, whose optimization direction is difficult to control as a black-box method, or 2) feature-consistency learning, which has no explicit physical meaning. To deal with these difficulties, we propose a novel progressive SSCD framework in this article, termed feature-prediction alignment (FPA). FPA can efficiently utilize unlabeled RSI pairs for training by two alignment strategies. First, a class-aware feature alignment (FA) strategy is designed to align the area-level change/no-change feature extracted from different unlabeled RSI pairs (i.e., across regions) with the awareness of their locations, in order to reduce the feature difference within the same classes. Second, a pixelwise prediction alignment (PA) is devised to align the pixel-level change prediction of strongly augmented unlabeled RSI pairs to the pseudo-labels calculated from the corresponding weakly augmented counterparts, in order to reduce the prediction uncertainty of various RSI transformations with physical meaning. Experiments are carried out on four widely used CD benchmarks, including Learning, Vision and Remote Sensing Laboratory (LEVIR-CD), Wuhan University building CD (WHU-CD), CDD, and GZ-CD, and our FPA achieves the state-of-the-art performance. The experimental results demonstrate the superiority of our method in both effectiveness and generalization. Our code is available athttps://github.com/zxt9/FPA-SSCD.

Change detection (CD) is an important Earth observation task that can monitor change areas at two times from the view of space. However, fully-supervised CD has a heavy dependence on numerous manually-labeled data, limiting their applications in practice. Beyond the fully-supervised setting, semi-supervised change detection (SSCD), which uses a few labeled data to guide the unsupervised learning of dominant unlabeled data, has attracted increasing attention for its significant advantage in alleviating the demand for annotations. To this end, in this paper we propose a joint self-training and rebalanced consistency learning (ST-RCL) framework for SSCD, which consists of a basic supervised branch for the labeled data and a novel unsupervised branch for the unlabeled data. To make full use of the unlabeled data, the unsupervised branch generates pseudo-labels from weakly-augmented unlabeled remote sensing image (RSI) pairs to supervise the CD of two strongly-augmented counterparts, including an unrotated version and a rotated version. On one hand, the unrotated unlabeled RSI pairs are pseudo-supervised with the pseudo-labels by confidence-based self-training. On the other hand, to further enhance model robustness to rotation non-equivariance and imbalanced distribution, the predictions of rotated unlabeled RSI pairs are aligned to the pseudo-labels by a well-designed rebalanced consistency learning strategy based on uncertainty-based class weighting. Extensive experiments are performed on four widely-used CD datasets, and the proposed ST-RCL yields new state-of-the-art results on all these datasets in comparison with some other SSCD methods, demonstrating its effectiveness and generalization. Our code will be available at https://github.com/zxt9/STRCL-SSCD.

The accurate extraction of residential area is of great significance to disaster assessment, urban management, and climate change research. Deep learning-based methods are limited by annotations acquisition and single data source. In this paper, a dual-branch encoder is proposed to extract the features of different inputs, and a multi-attention fusion module is proposed to effectively fuse dual-branch features. Furthermore, we propose a novel encoder-decoder architecture, called the dual-branch fusion network (DBNet). In addition, we propose a large-scale residential area extraction data set (ZRA) containing 43 Ziyuan-3 (ZY3) multi-spectral (MS) and multi-view (MV) images. Experiments on ZRA, our DBNet achieve state-of-the-art performance with a F1 score of 84.6% and an IoU of 73.3%.

Deep convolutional neural networks have been dominating in the field of hyperspectral image (HSI) classification. However, single convolutional kernels can limit the receptive field and fail to capture the sequential properties of data. Self-Attention-based Transformer can build global sequence information, among which, the Swin Transformer (SwinT) integrates sequence modeling capability and priori information of the visual signals (e.g., locality and translation invariance). Based on SwinT, we propose a 3D Swin Transformer (3DSwinT) to accommodate the 3D properties of HSI and capture the rich spatial-spectral information of HSI. Currently, supervised learning is still the most commonly used method for remote sensing image interpretation. However, pixel-by-pixel HSI classification demands a large number of high-quality labeled samples, which are time-consuming and costly to collect. As an unsupervised learning, self-supervised learning (SSL), especially contrastive learning, can learn semantic representations from unlabeled data, and hence, is becoming a potential alternative to supervised learning. On the other hand, current contrastive learning methods are all single-level or single-scale, which do not consider complex and variable multi-scale features of objects. Therefore, this paper proposes a novel 3DSwinT-based hierarchical contrastive learning method (3DSwinT-HCL), which can fully exploit multi-scale semantic representations of images. Besides, we propose a multi-scale local contrastive learning (MS-LCL) module to mine the pixel-level representations in order to adapt to downstream dense prediction tasks. A series of experiments verify the great potential and superiority of 3DSwinT-HCL.

Classification and change detection of high -resolution remote sensing images using low-resolution labels is a challenging issue in remote sensing community. In this paper, we propose a semi-supervised method based on deep learning to generate high-resolution change maps of Maryland using low-resolution NLCD labels in the Multitemporal Semantic change Detection challenge track (Track MSD) of the 2021 IEEE Data Fusion Contest. Firstly, we refined the NLCD labels using five global land cover products. Subsequently, Fully Convolutional Network (FCN) was trained for the classification of NAIP images with the refined NLCD labels and then the network training was continued with the pseudo-labels from the previous classification results and Modified Normalized Difference Water Index (MNDWI) extracted from Landsat-8 images as the new features. Finally, after the decision-level fusion of the two training periods, change detection results were generated from the bi-temporal classification maps with a post-processing to improve the performance. This algorithm achieved an average Intersection-over-Union (IoU) of 0.6657 on the test dataset and won the 2ndplace of the contest.

The recent availability of high-resolution multiview ZY-3 satellite images, with angular information, can provide an opportunity to capture 3-D structural features for classification. In high-resolution image classification over urban areas, objects with diverse vertical structures make urban landscape more heterogeneous in 3-D space and consequently can make the classification challenging. In this article, a novel multiangle gray-level cooccurrence tensor feature is proposed based on the multiview bands of the ZY-3 imagery, namely, GLCMMA–T. The GLCMMA–Tfeature captures the distributions of the gray-level spatial variation under different viewing angles, which can depict the 3-D textures and structures of urban objects. The spectral and GLCMMA–Ttensor features are interpreted by two 3-D convolutional neural network (CNN) streams and then concatenated as the input to the fully connected layer. This novel multispectral and multiangle 3-D convolutional neural network (M2-3-DCNN) combines the spectral and angular information, and the fused feature has the potential to provide a comprehensive description of urban objects with complex vertical structures. The experimental results on ZY-3 multiview images from four test areas indicate that the proposed method can significantly improve the classification accuracy when compared with several state-of-the-art multiangle features and deep-learning-based image classification methods.

This paper presents a new semi-supervised method for land cover classification using Sentinel-2 time-series images, which can deal with the problem of unclear observations. First, the MCCR method, which is constituted by the matrix completion (MC) of unclear observations and feature-adaptive collaborative representation (CR) based classifier, is adopted to handle the data quality problem. Second, by fusing RF, AdaBoost, and MCCR, a tri-training process is proposed to iteratively select the semi-labeled samples, considering the difference of classification certainty in different classifiers and classes. Experiments on two sets of Sentinel-2 images are conducted to validate the effectiveness of the proposed semi-supervised method.

Land-cover mapping over urban areas using Landsat imagery has attracted considerable attention in recent years as it can promptly and accurately reflect the biophysical composition status of the urban landscape and allow further applications such as urban planning and risk management. However, due to the large diversity across different urban landscapes, adequate training sample collection for urban area mapping is both challenging and time-consuming. In this paper, we propose a novel unsupervised sample collection method for mapping urban areas using Landsat imagery. Specifically, the idea is to select reliable, representative, and diverse training samples from the images in a two-stage and iterative manner, based on a set of spectral indices (vegetation, impervious surface, soil, water). To validate the effectiveness and robustness of the proposed method, a synthetic data set was designed and a series of Landsat images over 39 representative cities from different biomes across the world was employed. The effectiveness of the proposed algorithm was quantitatively validated by assessing the quality of the automatically collected samples and the accuracy of the mapping results. In terms of the mapping performance, the proposed automatic approach can achieve a comparable mapping accuracy to supervised classification with manually collected samples. On the basis of the freely accessed Landsat data, the proposed approach demonstrates a promising potential for automatic large-scale (i.e., global) mapping over urban areas.

This paper presents a new building indicator based on the multi-angle images, the angular difference feature (ADF), which characterizes angular properties from high-resolution ZY-3 multi-view images. The method for detecting buildings based on ADF consists of two main steps: ADF feature extraction and a post-processing step to refine the results by simultaneously incorporating the spectral and geometrical information. Experiments are conducted with three ZY-3 images acquired over Chinese cities. The proposed ADF achieves promising building detection performance over both highly dense urban areas and suburban areas, with an overall accuracy of better than 89% for all the three data sets.

Hyperspectral remote sensing could acquire hundreds of bands to cover a complete spectral interval, which deliver more information and allow a whole range of new and more precise applications. But vast data volume can cause trouble in computer processing and data transmission. Too many bands may cause interference for image processing and endmember variability is inevitable in hyperspectral data, which will affect the accuracy of interpretation. Band selection for hyperspectral image data is an effective way to mitigate the curse of dimensionality. In this paper, one hyperspectral band selection method based on endmember dissimilarity is proposed. This method used Mahalanobis distance as class separability criterion, and the spectral signature for each class is proposed by endmember extraction method automatically. Experiments on both synthetic and real hyperspectral data sets indicate that the proposed method outperformed the Minimum Estimated Abundance Covariance (MEAC) and Uniform Spectral Spacing (USS) method.

The mapping of urban areas at regional to global scales is a crucial task due to its value for environmental monitoring, habitat and biodiversity conservation, and decision-making. In most current applications, two techniques (i.e., supervised classification and data fusion) are widely applied in large-scale urban mapping. However, the costly training sample collection, inadequate data-source descriptions, and diverse urban characteristics (e.g., shape, size, socioeconomic status, and physical environment) are challenging problems for the urban mapping approaches. In this context, aiming at effectively deriving accurate urban areas at a large scale, we propose a novel ensemble support vector machine (SVM) method which consists of three steps: 1) the automatic generation of training data to reduce labor costs; 2) the construction of an ensemble SVM model to effectively combine the multisource data (including remote sensing and socioeconomic data); and 3) an adaptive patch-based thresholding technique to tackle the diverse urban characteristics. The proposed method is employed to map urban areas of China in 2005 and 2010, and the resulting maps are compared with the existing urban maps for 287 prefecture-level cities. It is found that our results present a satisfactory superiority, especially in challenging small cities, with a significant improvement in median Kappa (0.174 for 2005 and 0.203 for 2010). When incorporating moderate-resolution imaging spectroradiometer multispectral data as an additional source, the Kappa coefficient can be further raised by 0.028 for 2010. In general, the proposed method shows great potential for accurately mapping urban areas at regional, continental, or even global scales in a cost-effective manner.

In this article, we proposed a novel semi-supervised sparse representation classification for high resolution remote sensing image. First, collaborative representation mechanism that exploits the help from whole training information rather than from only the potential associated class can enhance the class recognition ability. Second, by taking advantage of spatial occurrence and alignment of class label, the adoption of the relearning can gradually learn the flexible class-oriented spatial pattern from the label space with alleviated computational complexity to enhance the original spectral characteristics. Third, inspired by the spatial smoothing phenomenon when spatial feature stacked, a novel stable self-learning method can be designed to automatically select informative unlabeled sample to help the limited supervised set. Experiments on two hyperspectral and high-spatial resolution images validated the effectiveness and robustness of the proposed algorithm.

In the last few years, integrating spatial information into active learning framework has been gaining growing interest in the remote sensing community to optimize the collection of training sample set for supervised image classification. We address this problem from two directions. One of the directions focus on improving the classifier's performance to reduce the need for training samples. For this purpose, relearning model is introduced to combine the active learning framework to form mutually reinforcing process. In the meantime, another direction focus on the way to select most informative samples. For this purpose, new uncertainty criterion is proposed to favor the selection of samples not only with most spectral uncertainty, but also located in most uncertain spatial regions. Experiments on hyperspectral image show the effectiveness of proposed active learning framework.

For a long time, the available global products of the urban area extent were limited to a coarse spatial resolution, e.g., Moderate Resolution Imaging Spectroradiometer (MODIS) global land cover (GLC) 500-m data and European Space Agency GlobCover 300-m data. This limitation was broken by the GlobeLand30 data, which is the world's first 30-m resolution GLC data set. However, detection accuracies of urban areas for the GlobeLand30 data (i.e., artificial surfaces) are not satisfactory. Therefore, in order to refine the detection accuracy of urban areas on the basis of the GlobeLand30 data, we propose a novel framework for urban area delineation by combining a set of remote sensing images and a geographical information system database, including the GlobeLand30 data, the National Land Cover Database (NLCD), the Land Use Interpretation Map (LUIM) of China, and Landsat images. First, the GlobeLand30 and land use/land cover products (e.g., NLCD or LUIM) are overlapped, and the study area is then separated into reliable and unreliable areas with a majority voting rule. Finally, the unreliable areas are confirmed by use of the Landsat data with a multiclassifier system. Experiments were conducted over two study areas that, respectively, represent typical patterns of American and Chinese urban areas: 1) the states of Utah, Mississippi, and Pennsylvania in the U.S. and 2) the provinces of Ningxia, Fujian, and Jilin in China. The results show that the accuracy of the GlobeLand30 data for urban area delineation can be significantly improved by integrating the multisource data and using the multiclassifier system.

With regard to the specific role of each pixel within a spatial parcel of a hyperspectral image (HSI), we propose a novel superpixel-oriented sparse representation classification method with a multi-task learning approach. The proposed algorithm exploits the class-level sparsity prior for multiple-feature fusion, and also the correlation and distinctiveness of pixels in a spatial local region. Compared with the state-of-the-art hyperspectral classifiers, the superiority of the spatial prior utilization, the multiple-feature fusion, and the computational efficiency are maintained at the same time in the proposed method. The proposed classification framework was tested on two HSIs. The experimental results suggest that the proposed algorithm performs better than the other representation-based classification algorithms and some popular hyperspectral multiple-feature classifiers.

In this paper, we propose a superpixel-level sparse representation classification framework with multitask learning for hyperspectral imagery. The proposed algorithm exploits the class-level sparsity prior for multiple-feature fusion, and the correlation and distinctiveness of pixels in a spatial local region. Compared with some of the state-of-the-art hyperspectral classifiers, the superiority of the multiple-feature combination, the spatial prior utilization, and the computational complexity are maintained at the same time in the proposed method. The proposed classification algorithm was tested on three hyperspectral images. The experimental results suggest that the proposed algorithm performs better than the other sparse (collaborative) representation-based algorithms and some popular hyperspectral multiple-feature classifiers.

A novel sparsity-based sub-pixel anomaly detection framework is proposed for hyperspectral imagery. The proposed approach consists of the following steps. First, a joint sparsity model is utilized to simultaneously represent the surrounding local background pixels and to automatically prune the rough overcomplete dictionary as a reliable, compact base for the following center test pixel representation. An unconstrained linear unmixing approach based on the compact dictionary is then utilized to decompose the abundance of the center test pixel. The unmixing result is finally compared to the former background joint sparse representation step, and the energy disparity is utilized to reflect the anomaly test result. The experimental results confirm that the proposed algorithm outperforms the classical RX-based anomaly detector and the orthogonal subspace projection based detector, and gives a desirable and stable performance.

In this letter, a novel supervised segmentation technique based on sparsely representing the stacked extended morphological attribute profiles (EAPs) and maximum a posteriori probability (MAP) is presented for very high resolution (VHR) images. Attribute profiles (APs), which are extracted by using several attributes, are applied to the multispectral VHR image, leading to a set of extended EAPs. Using the sparse prior of representing the pixel with all training samples, the extended multi-AP (EMAP) feature stacked by the EAP features is transformed into a class-dependent residual feature, which can be normalized as a posterior probability distribution of the pixel. A graph-cut approach is utilized to segment the image scene and obtain the final classification result. Experiments were conducted on IKONOS and WorldView-2 data sets. Compared with SVM, object-oriented SVM with majority voting, and some other state-of-the-art methods, the proposed method shows stable and effective results.

Sparse representation has been widely used in image classification. Sparsity-based algorithms are, however, known to be time consuming. Meanwhile, recent work has shown that it is the collaborative representation (CR) rather than the sparsity constraint that determines the performance of the algorithm. We therefore propose a nonlocal joint CR classification method with a locally adaptive dictionary (NJCRC-LAD) for hyperspectral image (HSI) classification. This paper focuses on the working mechanism of CR and builds the joint collaboration model (JCM). The joint-signal matrix is constructed with the nonlocal pixels of the test pixel. A subdictionary is utilized, which is adaptive to the nonlocal signal matrix instead of the entire dictionary. The proposed NJCRC-LAD method is tested on three HSIs, and the experimental results suggest that the proposed algorithm outperforms the corresponding sparsity-based algorithms and the classical support vector machine hyperspectral classifier.

A column generation kernel technology based nonlinear regression classification method for hyperspectral image is proposed in this paper. The nonlinear extension for the collaborative representation regression is utilized in the joint collaboration model framework. The proposed algorithm is tested on two hyperspectral images. Experimental results suggest that the proposed nonlinear algorithm shows superior performance over other linear regression-based algorithms and the classical hyperspectral classifier SVM.