Semantic segmentation of remote sensing imagery plays an important role in applications such as environmental monitoring and disaster response. However, challenges such as complex spatial patterns of variable target objects, significant scale variations, and high inter-class similarity challenge accurate segmentation. Most existing methods based on convolutional neural networks (CNNs) and Transformers face limitations in modeling multi-scale global-local dependencies or often incur high computational costs. Therefore, we propose a multi-scale convolution and mamba fusion model (MF-Mamba) that integrates a CNN encoder with a Mamba-based decoder. The decoder incorporates a Global-Local State Space (GLSS) module with eight-directional selective scanning mechanisms and multi-kernel parallel convolutions to capture the rich global-local context. To enhance multi-scale feature representation, we developed a channel-spatial attention and dense multi-scale feature fusion (CSDF) module, which combines channel-spatial attention and atrous convolutions for multi-scale feature fusion. Additionally, a multi-scale lateral connection is developed to align encoder features for efficient integration. Experiments on the data sets of ISPRS Vaihingen, ISPRS Potsdam, and the Wuhan Dense Labeling Dataset (WHDLD) demonstrate the superior performance of MF-Mamba compared to existing state-of-the-art methods. It achieves Mean F1 scores of 86.71%, 90.70%, and 77.07%, respectively. The code is available at https://github.com/Mango-Mars/MF-Mamba.

Floods are one of the most frequent and disastrous natural hazards that affect millions of people and cause damage all around the world. Satellite-based flood mapping using optical or synthetic aperture radar (SAR) data has become an important component of disaster response. For extreme event monitoring such as floods, SAR and optical data have obvious complementary advantages in terms of data availability and information richness, thereby the combined use of the two data for flood monitoring has great application potential. However, there is a lack of efficient processing methods for both SAR and optical data, which limits the cross-application of these two complementary data in flood monitoring. In this study, we develop an unconditional image thresholding approach of optical and SAR data (UIT) to monitor large-scale floods. The UIT algorithm constructs a variable Gaussian mixture model with parameter prior to express the probability distribution of water and non-water pixels, which can be effectively compatible with optical and SAR data. To reduce the omission error of the flood extent, spatial context information and dual-polarization information are exploited for fast flood detection. The experiment used Sentinel-1 SAR data and Sentinel-2 optical data to monitor two severe flooding events in the history of the Gulf of Mexico. Compared with several state-of-the-art methods, the proposed algorithm can distinguish water from non-water pixels with higher accuracy (more than 5% improvement in OA).

Rapid and accurate monitoring of surface water is critical for water resource management, environmental protection, sustainable urban development, among other issues. Traditional threshold-based or classification-based surface water mapping methods often require adjusting thresholds or training samples for different regions or different sensors, which may hinder the generalization performance of the method in large-scale water body mapping. We propose an unsupervised deep learning water body mapping framework (UUCP) for unlabeled large-scale optical remote sensing images in this study. The UUCP framework adopts an unsupervised multi-segment thresholding strategy to achieve the transition from label-free learning to noisy label learning, and learns robust multi-scale features of water bodies by the developed channel attention multi-scale surface water extraction network and training strategies under noise labels. The results show that our proposed method performs well in the overall performance of water extraction and is applicable to different sensors.

The scientific research and engineering application of digital elevation models (DEM) is affected by data voids. Spatial interpolation methods and the generative adversarial network (GAN)-based methods are widely used in void filling. However, they suffer from accuracy degradation, artifacts, and the failure to reconstruct complex terrain features. To address these deficiencies, a terrain feature-guided diffusion model (TFDM) is proposed to fill the DEM data voids. To the best of our knowledge, this is the first work where a diffusion model has been applied to DEM void filling. The TFDM is characterized by the generation of seamless DEM surfaces and stable terrain contours in response to terrain conditions. The TFDM outperforms common methods in filling voids according to visual inspection and quantitative comparison and improves elevation accuracy with low bias in regions of varying gradients.

Small water bodies and their temporal changes are, especially in urban areas, closely related to the urban climate, people's daily life, among others. Mapping of small water bodies with optical remote sensing images in complex urban landscapes is challenging: that is to establish a balance between reducing incorrect water detection and increasing the integrity of water extraction. In this work we propose a spatial information-integrated small water bodies mapping (SWM) method to achieve a complete and accurate extraction and temporal change monitoring of small water bodies. The spatial contextual information is exploited by the proposed water index roughness feature to compensate for the indistinguishability of small water bodies in spectral information. Results using Landsat and Sentinel-2 data show that the proposed algorithm achieves better water extraction performance, i.e. higher completeness and less incorrect extractions. It proves the ability to observe the changes of surface water.

In deep learning (DL)-based hyperspectral imagery classification, “spatial patching” is primarily used as a preprocessing for incorporating local spatial information. This operation can help to promote classification accuracy but it is facing new challenges in the unmanned aerial vehicle (UAV)-borne hyperspectral imagery with high spatial and spectral resolutions (H2imagery). The ground objects’ various spatial scales result in it being challenging to determine the optimal size for the spatial patches. In addition, due to the severe spectral variability and spatial heterogeneity of the H2imagery, “spatial patching” only exploits the local spatial information and results in serious salt-and-pepper (SP) noise and isolated areas in the classification maps. In this article, to address these issues, a novel spectral patching network (SPNet) with an end-to-end DL architecture is proposed for UAV-borne H2imagery classification. The “spectral patching” approach is proposed to preserve the global spatial information and almost all the spectral information of the original hyperspectral imagery. An end-to-end deep encoder–decoder network is then constructed based on the spectral patching mechanism, which introduces the deep residual network (ResNet) and atrous spatial pyramid pooling (ASPP) modules to extract multiscale high-level semantic information for the H2imagery classification. The experimental results obtained with the Wuhan UAV-borne H2imagery (WHU-Hi) UAV-borne hyperspectral data set demonstrate that SPNet can achieve state-of-the-art accuracy and visualization performance in the classification of H2imagery.

High spatial resolution (HSR) remote sensing images can reflect more subtle changes and more specific types of land use and land cover (LULC) due to the abundant spatial geometric information. In this article, a class-prior object-oriented conditional random field (COCRF) framework consisting of a binary change detection (CD) task and a multiclass CD task is proposed to fill the application gap. In the proposed framework, the class-prior knowledge is used to improve the construction of the unary potential in both the binary and multiclass CD tasks, to reduce the influence of spectral variability. The binary CD result provides a constraint to the multiclass CD result. As a result, both parts have effective interaction. The class posterior probability images of two dates can be obtained automatically with the class-prior knowledge by sample migration. Furthermore, an object constraint described by the class dispersion within the objects is added to improve the smoothness in local objects, while the pairwise potential improves the smoothness of the whole area by using the eight-neighborhood spectral information of the center pixel. By integrating the above approaches, the problems of error accumulation and the manual intervention required in the traditional multiclass CD methods can be relieved. An adaptive parameter estimation strategy is also adopted in the proposed framework, to save the time required for manual parameter setting. The proposed COCRF framework was validated on two HSR remote sensing image data sets, where it achieved a better performance than the other state-of-the-art CD methods.

Deep learning (DL)-based hyperspectral classification primarily use "spatial patching" as preprocessing for incorporating local spatial information. This operation can help to promote classification accuracy but faces the following problems. First, it is difficult to determine the optimal size of spatial patches for different hyperspectral images (HSIs). Second, this operation only exploits spatial features locally but not globally. In this paper, we propose a novel spectral patching network (SPNet) with an end-to-end deep learning architecture for HSI classification. SPNet uses "spectral patching" and Atrous Spatial Pyramid Pooling (ASPP) module to fully preserve the local and global spatial contextual information of original HSIs. The experimental results with UAV-borne hyperspectral dataset demonstrate that the SPNet achieved state-of-the-art accuracy and visualization performance in.

Road detection is a hot research topic in the very high resolution (VHR) remote sensing field and has been applied in various practical applications. Many deep-learning based methods have been used to detect roads and achieved good performance. In this paper, a multi-scale enhanced road detection framework (DenseUNet) is proposed which based on the densely connected convolutional networks (DenseNet) and U-Net. The U-Net has strong capabilities of preserving spatial details due to its skip connections, and the DenseNet can better optimize the deep network. Meanwhile, atrous spatial pyramid pooling (ASPP) is employed to effectively capture multi-scale features for road detection. Finally, a public road dataset was used to verify the proposed approach, compared with other state-of-the-art methods. The proposed method achieve the best performance, which illustrates its superiority.

Road detection and centerline extraction from very high-resolution (VHR) remote sensing imagery are of great significance in various practical applications. Road detection and centerline extraction operations depend on each other, to a certain extent. The road detection constrains the appearance of the centerline, and the centerline enhances the linear features of the road detection. However, most of the previous works have addressed these two tasks separately and have not considered the symbiotic relationship between them, making it difficult to obtain smooth and complete roads. In this paper, a novel multi-scale and multi-task deep learning framework for automatic road extraction (MSMT-RE) is proposed to build the relationship between them and simultaneously complete the road detection and centerline extraction tasks. U-Net is selected as the basic network for multi-task learning due to its strong ability to preserve spatial details. Multi-scale feature integration is also applied in the framework to increase the robustness of the feature extraction. Meanwhile, an adaptive loss function is introduced to solve the problems of roads taking up a small percentage of the training samples, and the fact that the positive samples of the two tasks are unbalanced. Finally, experiments were conducted on two public road data sets and two large images from Google Earth, and the proposed framework was compared with other state-of-the-art deep learning-based road extraction methods, both quantitatively and qualitatively. The proposed approach outperformed all the compared methods, confirming its advantages in automatic road extraction.

High spatial resolution (HSR) remote sensing images provide detailed geometric information about land cover. As a result, it is possible to detect more subtle changes with the help of HSR images. However, due to the increased spatial resolution and the limited spectral information, it is difficult to identify the real changes only through the spectral feature of the image. To fully explore the spectral–spatial information and improve the change detection performance for HSR images, this paper proposes the hybrid conditional random field (HCRF) model, which combines the traditional random field method with an object-based technique. In the proposed method, the spectral discriminative information of a single pixel is extracted by the unary potential, which is modeled using a soft clustering method to make an initial separation of changed and unchanged pixels. The pairwise potential then considers the contextual information of adjacent pixels to favor spatial smoothing. An object term is also introduced in the HCRF model to keep the homogeneity of changed objects. By the use of these approaches, the oversmoothing problem of the random field-based methods and the detection error caused by the segmentation strategy in the object-based methods can be relieved. The proposed method was tested on three HSR image data sets and outperformed the compared state-of-the-art techniques.

Convolutional neural networks (CNNs) has been introduced into remote sensing scene classification, achieving outstanding performance. However, the scale change of objects contained in remote sensing scene image make it difficult to extract feature robust to scale, limiting the further improvement of classification accuracy. In this paper, a scene classification method named Scale Invariance Convolutional Neural Networks (SICNNs) is proposed for remote sensing scene classification. In the proposed method, two images with different scales generated by randomly stretching one image are fed into CNNs simultaneously for training at intervals of several iterations. Then a similarity measure layer was added in SICNN to make the distance of the two feature vectors extracted from the two images as close as possible, leading extracted feature to be robust to scale. Experimental results using two datasets, i.e. the UC Merced dataset, Google dataset of SIRI-WHU, demonstrated the effectiveness of the proposed method.

In this paper, a structural conditional random field framework (SCRF) is proposed to detect the detailed change information from high spatial resolution (HSR) remote sensing imagery. Traditional random field based methods encounter the over-smoothing problem when deal with HSR images and the boundary of changed objects cannot be preserved well. To solve this problem, in SCRF, fuzzy c means (FCM) is used to model the unary potential while avoiding the independent assumption. Pairwise potentials with different shapes are selected as the structural set to model the spatial features of land cover such as buildings and roads. Based on SCRF, a set of change belief maps are generated to describe the observed image from different aspects. An object based fusion strategy is then followed to combine the belief maps to get the refined result. The results of the proposed method on two HSR data sets outperform some state-of-art algorithms.

In this paper, an unsupervised change detection model based on hybrid conditional random field model (HCRF) is proposed for high spatial resolution (HSR) remote sensing imagery. Traditional random field based algorithms are mainly based on the analysis of the difference image which ignores the spatial-temporal change information of ground objects which is important in dealing with HSR imagery. Thus in HCRF, a new graph structure is designed to explore the correlation of corresponding ground objects from different times to get a better result. The unary potential is selected as the probabilistic result of change vector analysis (CVA), the pairwise potential is modeled to consider the contextual information of difference image and the similarity between objects from bi-temporal original images is considered using an object term. The proposed method is tested on two HSR data sets (IKONOS and QuickBird) and out performs some state-of-art algorithms.

In this paper, a novel feature extraction framework is proposed for hyperspectral image classification. Inspired by the role of discriminant function in classifier, which intends to learn a mapping from the input features to label information in class space, we develop a feature extraction framework to learn the new feature representation of original input features in class space, by establishing the relevance between feature extraction and discriminative classifier. The new learning features integrate the input features and the discrimination information of used classifier with available training samples, which reveal the cues of class in class space. Therefore, the new features are called as the features of class-in-class. Several experiments were conducted to illustrate the availability of the proposed features.

In this letter, a multifeature probabilistic ensemble conditional random field (MFPECRF) model is proposed to perform the task of change detection for high spatial resolution (HSR) remote sensing imagery. MFPECRF not only considers the spectral feature of single pixels but also the interaction between neighborhood pixels and the structural property of the ground objects in HSR imagery to give a higher detection accuracy than the traditional random field methods, which only utilize spectral and label information. In the unary potential, the spectral and morphological features of the difference image are combined using a probabilistic ensemble strategy, and the pairwise potential considers the contextual information of the observed field. The parameters of MFPECRF are estimated using a piecewise strategy, and the final result is obtained by the use of the loopy belief propagation algorithm. The experimental results of two groups of HSR multispectral images confirm the potential of the proposed method in improving the detection accuracy for HSR imagery.

With the increase in the availability of high-resolution remote sensing imagery, classification is becoming an increasingly useful technique for providing a large area of detailed land-cover information by the use of these high-resolution images. High-resolution images have the characteristics of abundant geometric and detail information, which are beneficial to detailed classification. In order to make full use of these characteristics, a classification algorithm based on conditional random fields (CRFs) is presented in this paper. The proposed algorithm integrates spectral, spatial contextual, and spatial location cues by modeling the probabilistic potentials. The spectral cues modeled by the unary potentials can provide basic information for discriminating the various land-cover classes. The pairwise potentials consider the spatial contextual information by establishing the neighboring interactions between pixels to favor spatial smoothing. The spatial location cues are explicitly encoded in the higher order potentials. The higher order potentials consider the nonlocal range of the spatial location interactions between the target pixel and its nearest training samples. This can provide useful information for the classes that are easily confused with other land-cover types in the spectral appearance. The proposed algorithm integrates spectral, spatial contextual, and spatial location cues within a CRF framework to provide complementary information from varying perspectives, so that it can address the common problem of spectral variability in remote sensing images, which is directly reflected in the accuracy of each class and the average accuracy. The experimental results with three high-resolution images show the validity of the algorithm, compared with the other state-of-the-art classification algorithms.

In this paper, we propose a novel spectral-spatial conditional random field classification algorithm with location cues (CRFSS) for high spatial resolution remote sensing imagery. In the CRFSS algorithm, the spectral and spatial location cues are integrated to provide the complementary information from spectral and spatial location perspectives. The spectral cues of different land-cover types are mainly provided by support vector machine (SVM), because of its excellent spectral classification performance. However, it is difficult to deal with the common spectral variability problem in remote sensing images. To alleviate this dilemma, considering the spectral similarity of the same land-cover in a local region, a point-to-point (P2P) classifier is designed to emphasize the spatial location cues. The P2P classifier considers the nonlocal range of the spatial location interactions between the target pixel and its nearest training samples for all the classes. In addition, the pairwise potential of CRFSS also considers the spatial contextual information to favor spatial smoothing. The experimental results showed that the algorithm has a competitive classification performance, in both the quantitative and qualitative evaluation.

In this letter, a change detection algorithm based on pulse-coupled neural networks (PCNN) and the normalized moment of inertia (NMI) feature is proposed for high spatial resolution (HSR) remote sensing imagery. To better analyze a large remote sensing image, the whole image is divided into blocks by the use of a deblocking mechanism. The PCNN model is utilized to obtain the initial binary image, and the NMI feature is calculated based on the binary image to detect the hot spot changed areas. Finally, the changed areas are processed by expectation–maximization to obtain the final change map. The experimental results using QuickBird and IKONOS images demonstrate that the proposed algorithm has the ability to provide better change detection results for HSR images than the traditional PCNN change detection algorithms.

In the field of high spatial resolution (HSR) remote sensing imagery classification, object-oriented classification and conditional random field (CRF) approaches are widely used due to their ability to incorporate the spatial contextual information. However, the selection of the optimal segmentation scale in object-oriented classification is not an easy task, and some pairwise CRF models always show an oversmooth performance. In this paper, a detail-preserving smoothing classifier based on conditional random fields (DPSCRF) for HSR imagery is proposed to apply the object-oriented strategy in the CRF classification framework, thus integrating the merits of both approaches to consider the spatial contextual information and preserve the detail information in the classification. The DPSCRF model defines suitable potential functions based on the CRF model for HSR image classification, which comprise the spatial smoothing and local class label cost terms. Both terms favor spatial smoothing in a local neighborhood to consider the spatial information. In addition, the local class label cost also considers the different label information of neighboring pixels at each iterative step in the classification to preserve the detail information. In order to deal with the spectral variability of HSR imagery, a segmentation prior is used by the object-oriented processing strategy. This models the probability of each pixel based on the segmentation regions obtained by the connected-component labeling algorithm. The experimental results with three HSR images demonstrate that the proposed classification algorithm shows a competitive performance in both the quantitative and the qualitative evaluation when compared to the other state-of-the-art classification algorithms.

High spatial resolution (HSR) remote sensing imagery provides abundant geometric and detailed information, which is important for classification. In order to make full use of the spatial contextual information, object-oriented classification and pairwise conditional random fields (CRFs) are widely used. However, the segmentation scale choice is a challenging problem in object-oriented classification, and the classification result of pairwise CRF always has an oversmooth appearance. In this paper, a hybrid object-oriented CRF classification framework for HSR imagery, namely, CRF$+$OO, is proposed to address these problems by integrating object-oriented classification and CRF classification. In CRF$+$OO, a probabilistic pixel classification is first performed, and then, the classification results of two CRF models with different potential functions are used to obtain the segmentation map by a connected-component labeling algorithm. As a result, an object-level classification fusion scheme can be used, which integrates the object-oriented classifications using a majority voting strategy at the object level to obtain the final classification result. The experimental results using two multispectral HSR images (QuickBird and IKONOS) and a hyperspectral HSR image (HYDICE) demonstrate that the proposed classification framework has a competitive quantitative and qualitative performance for HSR image classification when compared with other state-of-the-art classification algorithms.