scholarly journals Artificial Intelligence-Based Deep Fusion Model for Pan-Sharpening of Remote Sensing Images

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ahmed I. Iskanderani ◽  
Ibrahim M. Mehedi ◽  
Abdulah Jeza Aljohani ◽  
Mohammad Shorfuzzaman ◽  
Farzana Akhter ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing ◽  
Spatial Resolution ◽  
Remote Sensing Images ◽  
Fusion Model ◽  
Gradient Information ◽  
Gradient Channel ◽  
Remote Sensing Image Fusion ◽  
Fused Image ◽  
Benchmark Datasets

During the past two decades, many remote sensing image fusion techniques have been designed to improve the spatial resolution of the low-spatial-resolution multispectral bands. The main objective is fuse the low-resolution multispectral (MS) image and the high-spatial-resolution panchromatic (PAN) image to obtain a fused image having high spatial and spectral information. Recently, many artificial intelligence-based deep learning models have been designed to fuse the remote sensing images. But these models do not consider the inherent image distribution difference between MS and PAN images. Therefore, the obtained fused images may suffer from gradient and color distortion problems. To overcome these problems, in this paper, an efficient artificial intelligence-based deep transfer learning model is proposed. Inception-ResNet-v2 model is improved by using a color-aware perceptual loss (CPL). The obtained fused images are further improved by using gradient channel prior as a postprocessing step. Gradient channel prior is used to preserve the color and gradient information. Extensive experiments are carried out by considering the benchmark datasets. Performance analysis shows that the proposed model can efficiently preserve color and gradient information in the fused remote sensing images than the existing models.

A robust mosaicking procedure for high spatial resolution remote sensing images

2015 ◽  
Vol 109 ◽  
pp. 108-125 ◽  
Author(s):  
Xinghua Li ◽  
Nian Hui ◽  
Huanfeng Shen ◽  
Yunjie Fu ◽  
Liangpei Zhang
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remote Sensing Images

scholarly journals Mapping Urban Functional Zones by Integrating Very High Spatial Resolution Remote Sensing Imagery and Points of Interest: A Case Study of Xiamen, China

2018 ◽  
Vol 10 (11) ◽  
pp. 1737 ◽  
Author(s):  
Jinchao Song ◽  
Tao Lin ◽  
Xinhu Li ◽  
Alexander V. Prishchepov
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remote Sensing Images ◽  
Remote Sensing Imagery ◽  
Points Of Interest ◽  
Very High Spatial Resolution ◽  
Functional Zones ◽  
Very High

Fine-scale, accurate intra-urban functional zones (urban land use) are important for applications that rely on exploring urban dynamic and complexity. However, current methods of mapping functional zones in built-up areas with high spatial resolution remote sensing images are incomplete due to a lack of social attributes. To address this issue, this paper explores a novel approach to mapping urban functional zones by integrating points of interest (POIs) with social properties and very high spatial resolution remote sensing imagery with natural attributes, and classifying urban function as residence zones, transportation zones, convenience shops, shopping centers, factory zones, companies, and public service zones. First, non-built and built-up areas were classified using high spatial resolution remote sensing images. Second, the built-up areas were segmented using an object-based approach by utilizing building rooftop characteristics (reflectance and shapes). At the same time, the functional POIs of the segments were identified to determine the functional attributes of the segmented polygon. Third, the functional values—the mean priority of the functions in a road-based parcel—were calculated by functional segments and segmental weight coefficients. This method was demonstrated on Xiamen Island, China with an overall accuracy of 78.47% and with a kappa coefficient of 74.52%. The proposed approach could be easily applied in other parts of the world where social data and high spatial resolution imagery are available and improve accuracy when automatically mapping urban functional zones using remote sensing imagery. It will also potentially provide large-scale land-use information.

scholarly journals Performance Evaluation of Single-Label and Multi-Label Remote Sensing Image Retrieval Using a Dense Labeling Dataset

2018 ◽  
Vol 10 (6) ◽  
pp. 964 ◽  
Author(s):  
Zhenfeng Shao ◽  
Ke Yang ◽  
Weixun Zhou
Keyword(s):  
Remote Sensing ◽  
Performance Evaluation ◽  
Deep Learning ◽  
Image Retrieval ◽  
Semantic Segmentation ◽  
Semantic Content ◽  
Remote Sensing Image ◽  
Remote Sensing Images ◽  
Benchmark Datasets ◽  
Feature Based

Benchmark datasets are essential for developing and evaluating remote sensing image retrieval (RSIR) approaches. However, most of the existing datasets are single-labeled, with each image in these datasets being annotated by a single label representing the most significant semantic content of the image. This is sufficient for simple problems, such as distinguishing between a building and a beach, but multiple labels and sometimes even dense (pixel) labels are required for more complex problems, such as RSIR and semantic segmentation.We therefore extended the existing multi-labeled dataset collected for multi-label RSIR and presented a dense labeling remote sensing dataset termed "DLRSD". DLRSD contained a total of 17 classes, and the pixels of each image were assigned with 17 pre-defined labels. We used DLRSD to evaluate the performance of RSIR methods ranging from traditional handcrafted feature-based methods to deep learning-based ones. More specifically, we evaluated the performances of RSIR methods from both single-label and multi-label perspectives. These results demonstrated the advantages of multiple labels over single labels for interpreting complex remote sensing images. DLRSD provided the literature a benchmark for RSIR and other pixel-based problems such as semantic segmentation.

scholarly journals Using Remote Sensing to Monitor the Spring Phenology of Acadia National Park across Elevational Gradients

2019 ◽  
Author(s):  
Yan Liu ◽  
Caitlin McDonough MacKenzie ◽  
Richard B. Primack ◽  
Michael J. Hill ◽  
Xiaoyang Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
National Park ◽  
Vegetation Index ◽  
Landsat 8 ◽  
Mountainous Area ◽  
Acadia National Park ◽  
Fusion Model ◽  
Elevational Gradients ◽  
Spring Phenology

Abstract. Greenup dates of the mountainous Acadia National Park, were monitored using remote sensing data (including Landsat 8 surface reflectances (at a 30 m spatial resolution) and VIIRS reflectances adjusted to a nadir view (gridded at a 500 m spatial resolution)) during the 2013–2016 growing seasons. Ground-level leaf-out monitoring in the areas alongside the north-south-oriented hiking trails on three of the park's tallest mountains (466 m, 418 m, and 380 m) was used to evaluate satellite derived greenup dates in this study. While the 30 m resolution would be expected to provide a better scale for phenology detection in this mountainous region than the 500 m resolution, the daily temporal resolution of the 500 m data would be expected to offer vastly superior monitoring of the rapid variations experienced during vegetation greenup along elevational gradients. Therefore, the greenup dates derived from the Landsat 8 Enhanced Vegetation Index (EVI) data, augmented with Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) simulated EVI values, does provide more spatial details than VIIRS data alone and agree well with field monitored leaf out dates. Satellite derived greenup dates from the 30 m of Acadia National Park vary among different elevational zones, although the date of greenup is not always the most advanced at the lowest elevation. This indicates that the spring phenology is not only determined by microclimates associated with different elevations in this mountainous area, but is also possibly affected by the species mixture, localized temperatures, and other factors in Acadia.

scholarly journals Assessing the Impacts of Species Composition on the Accuracy of Mapping Chlorophyll Content in Heterogeneous Ecosystems

2021 ◽  
Vol 13 (22) ◽  
pp. 4671
Author(s):  
Bing Lu ◽  
Yuhong He
Keyword(s):  
Remote Sensing ◽  
Species Composition ◽  
Chlorophyll Content ◽  
Spatial Resolution ◽  
Ecosystem Management ◽  
Remote Sensing Images ◽  
Species Classification ◽  
Growth Status ◽  
Species Specific ◽  
Estimation Models

Chlorophyll is an essential vegetation pigment influencing plant photosynthesis rate and growth conditions. Remote sensing images have been widely used for mapping vegetation chlorophyll content in different ecosystems (e.g., farmlands, forests, grasslands, and wetlands) for evaluating vegetation growth status and productivity of these ecosystems. Compared to farmlands and forests that are more homogeneous in terms of species composition, grasslands and wetlands are more heterogeneous with highly mixed species (e.g., various grass, forb, and shrub species). Different species contribute differently to the ecosystem services, thus, monitoring species-specific chlorophyll content is critical for better understanding their growth status, evaluating ecosystem functions, and supporting ecosystem management (e.g., control invasive species). However, previous studies in mapping chlorophyll content in heterogeneous ecosystems have rarely estimated species-specific chlorophyll content, which was partially due to the limited spatial resolution of remote sensing images commonly used in the past few decades for recognizing different species. In addition, many previous studies have used one universal model built with data of all species for mapping chlorophyll of the entire study area, which did not fully consider the impacts of species composition on the accuracy of chlorophyll estimation (i.e., establishing species-specific chlorophyll estimation models may generate higher accuracy). In this study, helicopter-acquired high-spatial resolution hyperspectral images were acquired for species classification and species-specific chlorophyll content estimation. Four estimation models, including a universal linear regression (LR) model (i.e., built with data of all species), species-specific LR models (i.e., built with data of each species, respectively), a universal random forest regression (RFR) model, and species-specific RFR models, were compared to determine their performance in mapping chlorophyll and to evaluate the impacts of species composition. The results show that species-specific models performed better than the universal models, especially for species with fewer samples in the dataset. The best performed species-specific models were then used to generate species-specific chlorophyll content maps using the species classification results. Impacts of species composition on the retrieval of chlorophyll content were further assessed to support future chlorophyll mapping in heterogeneous ecosystems and ecosystem management.

scholarly journals Ship Target Automatic Detection Based on Hypercomplex Flourier Transform Saliency Model in High Spatial Resolution Remote-Sensing Images

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2536 ◽  
Author(s):  
Jian He ◽  
Yongfei Guo ◽  
Hangfei Yuan
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Detection Efficiency ◽  
Detection System ◽  
Automatic Detection ◽  
Sea Surface ◽  
Detection Methods ◽  
Remote Sensing Images ◽  
Ship Detection ◽  
Saliency Model

Efficient ship detection is essential to the strategies of commerce and military. However, traditional ship detection methods have low detection efficiency and poor reliability due to uncertain conditions of the sea surface, such as the atmosphere, illumination, clouds and islands. Hence, in this study, a novel ship target automatic detection system based on a modified hypercomplex Flourier transform (MHFT) saliency model is proposed for spatial resolution of remote-sensing images. The method first utilizes visual saliency theory to effectively suppress sea surface interference. Then we use OTSU methods to extract regions of interest. After obtaining the candidate ship target regions, we get the candidate target using a method of ship target recognition based on ResNet framework. This method has better accuracy and better performance for the recognition of ship targets than other methods. The experimental results show that the proposed method not only accurately and effectively recognizes ship targets, but also is suitable for spatial resolution of remote-sensing images with complex backgrounds.

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