Very High Spatial Resolution Images Research Articles

TCNet: A Transformer–CNN Hybrid Network for Marine Aquaculture Mapping from VHSR Images

Precise delineation of marine aquaculture areas is vital for the monitoring and protection of marine resources. However, due to the coexistence of diverse marine aquaculture areas and complex marine environments, it is still difficult to accurately delineate mariculture areas from very high spatial resolution (VHSR) images. To solve such a problem, we built a novel Transformer–CNN hybrid Network, named TCNet, which combined the advantages of CNN for modeling local features and Transformer for capturing long-range dependencies. Specifically, the proposed TCNet first employed a CNN-based encoder to extract high-dimensional feature maps from input images. Then, a hierarchical lightweight Transformer module was proposed to extract the global semantic information. Finally, it employed a coarser-to-finer strategy to progressively recover and refine the classification results. The results demonstrate the effectiveness of TCNet in accurately delineating different types of mariculture areas, with an IoU value of 90.9%. Compared with other state-of-the-art CNN or Transformer-based methods, TCNet showed significant improvement both visually and quantitatively. Our methods make a significant contribution to the development of precision agricultural in coastal regions.

Consensus Techniques for Unsupervised Binary Change Detection Using Multi-Scale Segmentation Detectors for Land Cover Vegetation Images

Change detection in very-high-spatial-resolution (VHR) remote sensing images is a very challenging area with applicability in many problems ranging from damage assessment to land management and environmental monitoring. In this study, we investigated the change detection problem associated with analysing the vegetation corresponding to crops and natural ecosystems over VHR multispectral and hyperspectral images obtained by sensors onboard drones or satellites. The challenge of applying change detection methods to these images is the similar spectral signatures of the vegetation elements in the image. To solve this issue, a consensus multi-scale binary change detection technique based on the extraction of object-based features was developed. With the objective of capturing changes at different granularity levels taking advantage of the high spatial resolution of the VHR images and, as the segmentation operation is not well defined, we propose to use several detectors based on different segmentation algorithms, each applied at different scales. As the changes in vegetation also present high variability depending on capture conditions such as illumination, the use of the CVA-SAM applied at the segment level instead of at the pixel level is also proposed. The results revealed the effectiveness of the proposed approach for identifying changes over land cover vegetation images with different types of changes and different spatial and spectral resolutions.

Large-Scale Date Palm Tree Segmentation from Multiscale UAV-Based and Aerial Images Using Deep Vision Transformers

The reliable and efficient large-scale mapping of date palm trees from remotely sensed data is crucial for developing palm tree inventories, continuous monitoring, vulnerability assessments, environmental control, and long-term management. Given the increasing availability of UAV images with limited spectral information, the high intra-class variance of date palm trees, the variations in the spatial resolutions of the data, and the differences in image contexts and backgrounds, accurate mapping of date palm trees from very-high spatial resolution (VHSR) images can be challenging. This study aimed to investigate the reliability and the efficiency of various deep vision transformers in extracting date palm trees from multiscale and multisource VHSR images. Numerous vision transformers, including the Segformer, the Segmenter, the UperNet-Swin transformer, and the dense prediction transformer, with various levels of model complexity, were evaluated. The models were developed and evaluated using a set of comprehensive UAV-based and aerial images. The generalizability and the transferability of the deep vision transformers were evaluated and compared with various convolutional neural network-based (CNN) semantic segmentation models (including DeepLabV3+, PSPNet, FCN-ResNet-50, and DANet). The results of the examined deep vision transformers were generally comparable to several CNN-based models. The investigated deep vision transformers achieved satisfactory results in mapping date palm trees from the UAV images, with an mIoU ranging from 85% to 86.3% and an mF-score ranging from 91.62% to 92.44%. Among the evaluated models, the Segformer generated the highest segmentation results on the UAV-based and the multiscale testing datasets. The Segformer model, followed by the UperNet-Swin transformer, outperformed all of the evaluated CNN-based models in the multiscale testing dataset and in the additional unseen UAV testing dataset. In addition to delivering remarkable results in mapping date palm trees from versatile VHSR images, the Segformer model was among those with a small number of parameters and relatively low computing costs. Collectively, deep vision transformers could be used efficiently in developing and updating inventories of date palms and other tree species.

A Dual Neighborhood Hypergraph Neural Network for Change Detection in VHR Remote Sensing Images

The very high spatial resolution (VHR) remote sensing images have been an extremely valuable source for monitoring changes occurring on the Earth’s surface. However, precisely detecting relevant changes in VHR images still remains a challenge, due to the complexity of the relationships among ground objects. To address this limitation, a dual neighborhood hypergraph neural network is proposed in this article, which combines multiscale superpixel segmentation and hypergraph convolution to model and exploit the complex relationships. First, the bi-temporal image pairs are segmented under two scales and fed to a pre-trained U-net to obtain node features by treating each object under the fine scale as a node. The dual neighborhood is then defined using the father-child and adjacent relationships of the segmented objects to construct the hypergraph, which permits models to represent higher-order structured information far more complex than the conventional pairwise relationships. The hypergraph convolutions are conducted on the constructed hypergraph to propagate the label information from a small amount of labeled nodes to the other unlabeled ones by the node-edge-node transformation. Moreover, to alleviate the problem of imbalanced sampling, the focal loss function is adopted to train the hypergraph neural network. The experimental results on optical, SAR and heterogeneous optical/SAR data sets demonstrate that the proposed method offersbetter effectiveness and robustness compared to many state-of-the-art methods.

Parcel-level mapping of apple orchard in smallholder agriculture areas based on feature-level fusion of VHR image and time-series images

ABSTRACT Accurate and reliable parcel-level apple orchard mapping is required for many precise agriculture application models, including planting suitability evaluation, standardized production, and personal agricultural operation loan approval. However, in hilly areas where smallholder management predominates, the highly fragmented and heterogeneous agricultural landscape means that fine parcel-level apple orchard mapping remains challenging. This paper proposes a parcel-level apple orchard mapping method based on feature-level spatiotemporal data fusion, which is suitable for hilly areas where smallholder management predominates. First, a hierarchical strategy that simulates human image cognition processing was used to extract redundant candidate parcels from a very high spatial resolution (VHR) image (Google Earth image with a spatial resolution of 0.6 m). Second, deep learning models, including a Depth-wise Asymmetric Bottleneck Network (DABNet) and long short-term memory (LSTM), were used to extract implicit spatial and time series features of the parcels. Third, the implicit features extracted by the deep learning models were formatted into meta-features, which then formed the feature space together with the morphological and geographical features of the parcel. Fourth, based on the constructed parcel feature space, a random forests (RF) model was used to classify candidate parcels. The experiment was carried out in the town of Guanli, southwest of Qixia city, Shandong Province, China: 21,123 apple orchard parcels were extracted from 31,235 candidate parcels. The overall accuracy (OA) of the parcel-level mapping result was 0.919. The parcel features were combined according to their types, and the performance of different feature combinations for parcel classification was further compared, demonstrating that the proposed meta-features had a stronger spatial information description capability than traditional features. Moreover, the mean decrease in the accuracy (MDA) index was used to evaluate the importance of each feature. And spatial-information-related meta-features were revealed to play the most important role in parcel classification. This method provides methodological references for parcel-level orchard mapping in hilly areas where smallholder management predominates and can be applied to improve the monitoring of orchards in such areas.

Multiscale Diagnosis of Mangrove Status in Data-Poor Context Using Very High Spatial Resolution Satellite Images: A Case Study in Pichavaram Mangrove Forest, Tamil Nadu, India

Highlighting spatiotemporal changes occurring within mangrove habitats at the finest possible scale could contribute fundamental knowledge and data for local sustainable management. This study presents the current situation of the Pichavaram mangrove area, a coastal region of Southeast India prone to both cyclones and reduced freshwater inflow. Based on the supervised classification and visual inspection of very high spatial resolution (VHSR) satellite images provided with a pixel size of <4 m, we generated time-series maps to analyze the changes that occurred in both the natural and planted mangroves between 2003 and 2019. We achieved a high mapping accuracy (>85%), which confirmed the potential of classification techniques applied to VHSR images in capturing changes in mangroves on a very fine scale. Our diagnosis reveals variable expansion rates in plantations made by the local authorities. We also report an ongoing mangrove dieback and confirm progressive shoreline erosion along the coastline. Despite a lack of field data, VHSR images allowed for the multiscale diagnosis of the ecosystem situation, thus constituting the first fine-scale assessment of the fragile Pichavaram mangrove area upon which the coastal community is dependent.

A robust and adaptive spatial-spectral fusion model for PlanetScope and Sentinel-2 imagery

ABSTRACT Satellite sensors usually compromise between their spatial and spectral resolutions due to the limitations of data volume and signal-to-noise ratio, such as the Very High spatial Resolution (VHR) PlanetScope (PS, four or five 3-m bands) and medium spatial resolution (med-resolution) Sentinel-2 (S2, ten 10-m or 20-m bands) constellations. Concomitant with the growing demand for satellite images with ample spatial details and spectral signatures, Spatial-Spectral image Fusion (SSF) is important for blending the spatial resolution of PS and the spectral resolution of S2 to produce synthetic 3-m image products in the ten bands for different applications. However, the existing studies conducted for fusing PS and S2 data present limited spatial and spectral fidelities to original PS and S2 bands. Hence, this study presents a new SSF method named Robust and Adaptive Spatial-Spectral image Fusion Model (RASSFM) for that purpose. RASSFM improves the spatial and spectral fidelities of the results through: (1) combining the spectral mapping and the spectral correlation to obtain the high-quality spatial information sources for the S2 bands, (2) utilizing the neighborhood information considering both spatial and spectral constraints to improve the spectral fidelities of the fused bands. We conducted the SSF tests at the degraded and original spatial resolutions to comprehensively evaluate the proposed RASSFM method. Furthermore, we examined the performance of RASSFM in four study sites with highly mixed regular or desert urban, heterogeneous agricultural, and vegetation-dominated landscapes. Moreover, we compared our method with twenty representative SSF methods developed for similar purposes. The results demonstrate that RASSFM not only outperforms the other methods but has robust performance in different landscapes and comparable accuracies in different bands, thereby advancing the fusion of VHR and med-resolution images from PS and S2, respectively.

Land cover classification from very high spatial resolution images via multiscale object-driven CNNs and automatic annotation

Very high spatial resolution (VHR) images are accessible for land-cover classification. Because of the complex information and deep features associated with increased spatial resolution, it is difficult to find an effective method for improving the classification accuracy of finer-resolution remote sensing images. We propose a scheme for sample selection and sample autoannotation to establish a new labeled benchmark using vector maps. In addition, a suitable multiscale object-driven convolutional neural networks (multi-OCNNs) were developed for the new benchmark. We trained multi-OCNNs to capture the deep and contextual information contained in samples of the VM-RSI benchmark to predict the land cover categories from target region images with no labels. Furthermore, an object-driven voting strategy applied in multi-OCNNs provides clear boundary information of ground objects and less noise in CNN predictions. Experiments on multisource VHR images, including SPOT-6, Gaofen-2, and ALOS satellite images, show that the proposed approach outperforms other object-based algorithms by achieving good performance results, verifying its feasibility, and effectiveness for land cover classification.

Comparison of Image Endmember- and Object-Based Classification of Very-High-Spatial-Resolution Unmanned Aircraft System (UAS) Narrow-Band Images for Mapping Riparian Forests and Other Land Covers

Riparian forests are critical for carbon storage, biodiversity, and river water quality. There has been an increasing use of very-high-spatial-resolution (VHR) unmanned aircraft systems (UAS)-based remote sensing for riparian forest mapping. However, for improved riparian forest/zone monitoring, restoration, and management, an enhanced understanding of the accuracy of different classification methods for mapping riparian forests and other land covers at high thematic resolution is necessary. Research that compares classification efficacies of endmember- and object-based methods applied to VHR (e.g., UAS) images is limited. Using the Sequential Maximum Angle Convex Cone (SMACC) endmember extraction algorithm (EEA) jointly with the Spectral Angle Mapper (SAM) classifier, and a separate multiresolution segmentation/object-based classification method, we map riparian forests/land covers and compare the classification accuracies accrued via the application of these two approaches to narrow-band, VHR UAS orthoimages collected over two river reaches/riparian areas in Austria. We assess the effect of pixel size on classification accuracy, with 7 and 20 cm pixels, and evaluate performance across multiple dates. Our findings show that the object-based classification accuracies are markedly higher than those of the endmember-based approach, where the former generally have overall accuracies of >85%. Poor endmember-based classification accuracies are likely due to the very small pixel sizes, as well as the large number of classes, and the relatively small number of bands used. Object-based classification in this context provides for effective riparian forest/zone monitoring and management.

Effects of Mixture Mode on the Canopy Bidirectional Reflectance of Coniferous–Broadleaved Mixed Plantations

One of the main initiatives for China to achieve the goal of being carbon neutral before 2060 is transforming monocultures into mixed plantations in subtropical China, because mixed forests possess a higher quality than monocultures in various ways. Very high spatial resolution (VHR) satellite imagery is very promising to precisely monitor the transformation process under the premise of clarifying the canopy reflectance anisotropy of mixed plantations. However, it is almost impossible to understand the canopy reflectance anisotropy of mixed plantations with real satellite data due to the extreme lack of multiangular VHR satellite images. In this study, the effects of the mixture mode on the canopy bidirectional reflectance factor (BRF) were comprehensively analyzed with simulated VHR images. The three-dimensional (3D) Discrete Anisotropic Radiative Transfer model (DART) was used to construct a pure coniferous scene, a pure broadleaved scene, and 27 coniferous–broadleaved mixed plantation scenes containing 3 mixture patterns (i.e., mixed by single trees, mixed by stripes, and mixed by patches) and 9 mixing proportions (i.e., from 10% to 90% with the interval of 10%), and to simulate red (R) and near-infrared (NIR) VHR images for these 3D scenes at both the solar principal plane (SPP) and perpendicular plane (PP) under different solar-viewing geometries. Negative correlations were generally found between the canopy BRF and the ratio of conifers in a mixed stand. The anisotropy of conifer dominated plantations is more prominent than broadleaf dominated plantations, especially for the single tree mixture. Although the level of anisotropy is much lower for PP than SPP, it should not be ignored, especially for the R band. Observations under large viewing zenith angles at PP are more preferred to study the effect of mixing proportions, followed by forward observations at SPP. The R band image has higher potential to distinguish mixture patterns for broadleaf-dominated situations, while the NIR band image has a higher potential for conifer-dominated situations. Furthermore, the canopy BRF generally increases with the solar zenith angle, and one meter can be considered as the optimal spatial resolution for the optical monitoring of the mixture mode. The findings of the current study add some valuable theoretical knowledge for the accurate monitoring of coniferous–broadleaved mixed plantations with VHR imagery.

Bipartite Graph Attention Autoencoders for Unsupervised Change Detection Using VHR Remote Sensing Images

Detecting land cover change is an essential task in very-high-spatial-resolution (VHR) remote sensing applications. However, because VHR images can capture the details of ground objects, the scenes of VHR images are usually complex. For example, VHR images usually show distinct appearances or features of the same object, aroused by noise, climate conditions, imaging angles, etc. To address this issue, this paper proposes a novel unsupervised approach named bipartite graph attention autoencoders (BGAAE) for VHR image change detection. BGAAE, a further improved way of using dual convolutional autoencoders based on the architecture of image translation, equips the encoder layers with a graph attention mechanism (GAM). To generate an effective difference image, it consists of two additional loss terms: the domain correlation and semantic consistency losses, in addition to the reconstruction loss. The domain correlation loss is designed based on the encoder layers, aiming to enforce the spatial alignment of deep feature representations of the unchanged objects and mitigate the influence of pixel changes on the learning objective. The semantic consistency loss focuses on ensuring the semantic feature consistency of the bitemporal images after transcoding and allows for more flexible transformations. Experimental results on four VHR image datasets demonstrate the superiority of the proposed method.

Improving Parcel-Level Mapping of Smallholder Crops from VHSR Imagery: An Ensemble Machine-Learning-Based Framework

Explicit spatial information about crop types on smallholder farms is important for the development of local precision agriculture. However, due to highly fragmented and heterogeneous cropland landscapes, fine-scale mapping of smallholder crops, based on low- and medium-resolution satellite images and relying on a single machine learning (ML) classifier, generally fails to achieve satisfactory performance. This paper develops an ensemble ML-based framework to improve the accuracy of parcel-level smallholder crop mapping from very high spatial resolution (VHSR) images. A typical smallholder agricultural area in central China covered by WorldView-2 images is selected to demonstrate our approach. This approach involves the task of distinguishing eight crop-level agricultural land use types. To this end, six widely used individual ML classifiers are evaluated. We further improved their performance by independently implementing bagging and stacking ensemble learning (EL) techniques. The results show that the bagging models improved the performance of unstable classifiers, but these improvements are limited. In contrast, the stacking models perform better, and the Stacking #2 model (overall accuracy = 83.91%, kappa = 0.812), which integrates the three best-performing individual classifiers, performs the best of all of the built models and improves the classwise accuracy of almost all of the land use types. Since classification performance can be significantly improved without adding costly data collection, stacking-ensemble mapping approaches are valuable for the spatial management of complex agricultural areas. We also demonstrate that using geometric and textural features extracted from VHSR images can improve the accuracy of parcel-level smallholder crop mapping. The proposed framework shows the great potential of combining EL technology with VHSR imagery for accurate mapping of smallholder crops, which could facilitate the development of parcel-level crop identification systems in countries dominated by smallholder agriculture.

Change Detection From Very-High-Spatial-Resolution Optical Remote Sensing Images: Methods, applications, and future directions

Change detection is a vibrant area of research in remote sensing. Thanks to increases in the spatial resolution of remote sensing images, subtle changes at a finer geometrical scale can now be effectively detected. However, change detection from very-high-spatial-resolution (VHR) (≤5 m) remote sensing images is challenging due to limited spectral information, spectral variability, geometric distortion, and information loss. To address these challenges, many change detection algorithms have been developed. However, a comprehensive review of change detection in VHR images is lacking in the existing literature. This review aims to fill the gap and mainly includes three aspects: methods, applications, and future directions.

Understanding the synergies of deep learning and data fusion of multispectral and panchromatic high resolution commercial satellite imagery for automated ice-wedge polygon detection

The utility of sheer volumes of very high spatial resolution (VHSR) commercial imagery in mapping the Arctic region is new and actively evolving. Commercial satellite sensors typically record image data in low-resolution multispectral (MS) and high-resolution panchromatic (PAN) mode. Spatial resolution is needed to accurately describe feature shapes and textural patterns, such as ice-wedge polygons (IWPs) that are rapidly transforming surface features due to degrading permafrost, while spectral resolution allows capturing of land-use and land-cover types. Data fusion, the process of combining PAN and MS images with complementary characteristics often serves as an integral component of remote sensing mapping workflows. The fusion process generates spectral and spatial artifacts that may affect the classification accuracies of subsequent automated image analysis algorithms, such as deep learning (DL) convolutional neural nets (CNN). We employed a detailed multidimensional assessment to understand the performances of an array of eight application-oriented data fusion algorithms when applied to VHSR image scenes for DLCNN-based mapping of ice-wedge polygons. Our findings revealed the scene dependency of data fusion algorithms and emphasized the need for careful selection of the proper algorithm. Results suggested that the fusion algorithms that preserve spatial character of original PAN imagery favor the DLCNN model performances. The choice of fusion approach needs to be considered of equal importance to the required training dataset for successful applications using DLCNN on VHRS imagery in order to enable an accurate mapping effort of permafrost thaw across the Arctic region.

Fine-Grained Building Change Detection From Very High-Spatial-Resolution Remote Sensing Images Based on Deep Multitask Learning

Building change detection from very high-spatial-resolution (VHR) remote sensing images has gained increasing popularity in a variety of applications, such as urban planning and damage assessment. Detecting fine-grained “from–to” changes (change transition from one land cover type to another) of buildings from the VHR images is still challenging as multitemporal representation is complicated. Recently, fully convolutional neural networks (FCNs) have been proven to be capable of feature extraction and semantic segmentation of VHR images, but its ability in change detection is untested and unknown. In this letter, we leverage the semantic segmentation of buildings as an auxiliary source of information for the fine-grained “from–to” change detection. A deep multitask learning framework for change detection (MTL-CD) is proposed for detecting building changes from the VHR images. MTL-CD adopts the encoder–decoder architecture and solves the main task of change detection and the auxiliary tasks of semantic segmentation simultaneously. Accordingly, the change detection loss function is constrained by the auxiliary semantic segmentation tasks and enables the back-propagation of the building footprints’ detection errors for the improvement of change detection. A building change detection data set named the Guangzhou data set is also developed for model evaluation, in which the bitemporal R–G–B images were collected by airplane (2009) and unmanned aerial vehicle (UAV, 2019) with different flight heights. Experiments on the Guangzhou data set demonstrate that the MTL-CD method effectively detects fine-grained “from–to” changes and outperforms the postclassification methods and the direct change detection methods.

Unsupervised Scale-Driven Change Detection With Deep Spatial–Spectral Features for VHR Images

The rapid development of remote sensing technology has enabled the acquisition of very high spatial resolution (VHR) multitemporal images in Earth observation. However, how to effectively exploit these existing data to accurately monitor land surface changes is still a challenging task. In this article, we propose an unsupervised scale-driven change detection (CD) framework for VHR images by jointly analyzing the spatial–spectral change information, which combines the advantages of deep feature learning and multiscale decision fusion. First, a well pretrained deep fully convolutional network (FCN) is used to automatically extract the deep spatial context information from the acquired images. Then, the uncertainty analysis incorporating the deep spatial feature and the image spectral feature is implemented to generate a pseudobinary change map. On this basis, it is easy to choose suitable samples to train an excellent support vector machine (SVM) classifier, thus detecting changes occurred on the ground. In addition, the multiscale superpixel segmentation technique is introduced to make full use of the spatial structural information, which takes an image-object as the basic analysis unit. Finally, a robust binary change map with high detection precision can be achieved by merging the CD results obtained at different scales. The impressive experimental results on four real data sets demonstrate the effectiveness and flexibility of the proposed framework.

UAV-based canopy textures assess changes in forest structure from long-term degradation

Degraded tropical forests dominate agricultural frontiers and their management is becoming an urgent priority. This calls for a better understanding of the different forest cover states and cost-efficient techniques to quantify the impact of degradation on forest structure. Canopy texture analyses based on Very High Spatial Resolution (VHSR) optical imagery provide proxies to assess forest structures but the mechanisms linking them with degradation have rarely been investigated. To address this gap, we used a lightweight Unmanned Aerial Vehicle (UAV) to map 739 ha of degraded forests and acquire both canopy VHSR images and height model. Thirty-three years of degradation history from Landsat archives allowed us to sample 40 plots in undisturbed, logged, over-logged and burned and regrowth forests in tropical forested landscapes (Paragominas, Pará, Brazil). Fourier (FOTO) and lacunarity textures were used to assess forest canopy structure and to build a typology linking degradation history and current states. Texture metrics capture canopy grain, heterogeneity and openness gradients and correlate with forest structure variability (R2 = 0.58). Similar structures share common degradation history and can be discriminated on the basis of canopy texture alone (accuracy = 55%). Over-logging causes a lowering in forest height, which brings homogeneous textures and of finer grain. We identified the major changes in structures due to fire following logging which changes heterogeneous and intermediate grain into coarse textures. Our findings highlight the potential of canopy texture metrics to characterize degraded forests and thus be used as indicators for forest management and degradation mitigation. Inexpensive and agile UAV open promising perspectives at the interface between field inventory and satellite characterization of forest structure using texture metrics.

DFCNN-Based Semantic Recognition of Urban Functional Zones by Integrating Remote Sensing Data and POI Data

The urban functional zone, as a special fundamental unit of the city, helps to understand the complex interaction between human space activities and environmental changes. Based on the recognition of physical and social semantics of buildings, combining remote sensing data and social sensing data is an effective way to quickly and accurately comprehend urban functional zone patterns. From the object level, this paper proposes a novel object-wise recognition strategy based on very high spatial resolution images (VHSRI) and social sensing data. First, buildings are extracted according to the physical semantics of objects; second, remote sensing and point of interest (POI) data are combined to comprehend the spatial distribution and functional semantics in the social function context; finally, urban functional zones are recognized and determined by building with physical and social functional semantics. When it comes to building geometrical information extraction, this paper, given the importance of building boundary information, introduces the deeper edge feature map (DEFM) into the segmentation and classification, and improves the result of building boundary recognition. Given the difficulty in understanding deeper semantics and spatial information and the limitation of traditional convolutional neural network (CNN) models in feature extraction, we propose the Deeper-Feature Convolutional Neural Network (DFCNN), which is able to extract more and deeper features for building semantic recognition. Experimental results conducted on a Google Earth image of Shenzhen City show that the proposed method and model are able to effectively, quickly, and accurately recognize urban functional zones by combining building physical semantics and social functional semantics, and are able to ensure the accuracy of urban functional zone recognition.

An Approach for the Pan Sharpening of Very High Resolution Satellite Images Using a CIELab Color Based Component Substitution Algorithm

Recent very high spatial resolution (VHR) remote sensing satellites provide high spatial resolution panchromatic (Pan) images in addition to multispectral (MS) images. The pan sharpening process has a critical role in image processing tasks and geospatial information extraction from satellite images. In this research, CIELab color based component substitution Pan sharpening algorithm was proposed for Pan sharpening of the Pleiades VHR images. The proposed method was compared with the state-of-the-art Pan sharpening methods, such as IHS, EHLERS, NNDiffuse and GIHS. The selected study region included ten test sites, each of them representing complex landscapes with various land categories, to evaluate the performance of Pan sharpening methods in varying land surface characteristics. The spatial and spectral performance of the Pan sharpening methods were evaluated by eleven accuracy metrics and visual interpretation. The results of the evaluation indicated that proposed CIELab color-based method reached promising results and improved the spectral and spatial information preservation.

CNN-Based Land Cover Classification Combining Stratified Segmentation and Fusion of Point Cloud and Very High-Spatial Resolution Remote Sensing Image Data

Traditional and convolutional neural network (CNN)-based geographic object-based image analysis (GeOBIA) land-cover classification methods prosper in remote sensing and generate numerous distinguished achievements. However, a bottleneck emerges and hinders further improvements in classification results, due to the insufficiency of information provided by very high-spatial resolution images (VHSRIs). To be specific, the phenomenon of different objects with similar spectrum and the lack of topographic information (heights) are natural drawbacks of VHSRIs. Thus, multisource data steps into people’s sight and shows a promising future. Firstly, for data fusion, this paper proposed a standard normalized digital surface model (StdnDSM) method which was actually a digital elevation model derived from a digital terrain model (DTM) and digital surface model (DSM) to break through the bottleneck by fusing VHSRI and cloud points. It smoothed and improved the fusion of point cloud and VHSRIs and thus performed well in follow-up classification. The fusion data then were utilized to perform multiresolution segmentation (MRS) and worked as training data for the CNN. Moreover, the grey-level co-occurrence matrix (GLCM) was introduced for a stratified MRS. Secondly, for data processing, the stratified MRS was more efficient than unstratified MRS, and its outcome result was theoretically more rational and explainable than traditional global segmentation. Eventually, classes of segmented polygons were determined by majority voting. Compared to pixel-based and traditional object-based classification methods, majority voting strategy has stronger robustness and avoids misclassifications caused by minor misclassified centre points. Experimental analysis results suggested that the proposed method was promising for object-based classification.