Line-based registration for UAV remote sensing imagery of wide-spanning river basin

Zang, Wenqian; Lin, Jin‐Ming; Zhang, Baosen; Tao, Heping; Wang, Zhongmei

doi:10.1109/geoinformatics.2011.5980864

Cited by 9 publications

(5 citation statements)

References 7 publications

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“…(3) CNN Fully Dense Layer In the end, the Softmax classifier (activation function) was used to predict the exclusive classes and the characteristics of the CNN code were combined into a fully dense layer to classify each pixel in the image to the most likely label [18,41]. Softmax classifier discriminates the category of each pixel by weighting the distance between validation data and training datasets from that class [43].…”

Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

Land Cover Classification from fused DSM and UAV Images Using Convolutional Neural Networks

et al. 2019

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

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Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

Land Cover Classification from fused DSM and UAV Images Using Convolutional Neural Networks

et al. 2019

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“…However, the spectral and spatial resolution of multispectral satellite remote sensing is not precise enough to distinguish small-sized ground objects, and it is difficult to deal with the task of accurate extraction of urban water bodies with the characteristics of complex surrounding environments and areas that are not large enough for accurate detection. Compared with multispectral satellite remote sensing, uncrewed aerial vehicle (UAV) hyperspectral remote sensing has a higher spectral and spatial resolution, and because of its relatively low flying altitude, it greatly reduces the influence of atmospheric radiation on the water spectrum and is more suitable for urban rivers with complex and changeable water environments [29,30]. Because there are many bands in hyperspectral data, we cannot completely correspond the multispectral bands in the NDWI model with the hyperspectral bands.…”

Section: Introductionmentioning

confidence: 99%

The PCA-NDWI Urban Water Extraction Model Based on Hyperspectral Remote Sensing

Zhao,

Yang,

Wang

et al. 2024

Water

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Accurate extraction of water bodies is the basis of remote sensing monitoring of water environments. Due to the complex types of ground objects around urban water bodies, high spectral and spatial resolution are needed to achieve accurate extraction of water bodies. Addressing the limitation that most spectral index methods used for water body extraction are more suitable for open waters such as oceans and lakes, this study proposes a PCA-NDWI accurate extraction model for urban water bodies based on hyperspectral remote sensing, which combines Principal Component Analysis (PCA) with Normalized Difference Water Index (NDWI). Furthermore, aiming at the common water shadow problem in urban hyperspectral remote sensing images, the advantages of the PCA-NDWI model were further verified by experiments. By comparing the accuracy and F1-Measure of the PCA-NDWI, NDWI, HDWI, and K-means models, the results demonstrated that the PCA-NDWI model was better than the other tested methods. The accuracy and F1-Measure of the PCA-NDWI model water extraction data were 0.953 and 0.912, respectively, and the accuracy and F1-Measure of the PCA-NDWI model water shadow extraction data were 0.858 and 0.872, respectively. Therefore, the PCA-NDWI model can effectively separate shadows and the surrounding features of urban water bodies, accurately extract water body information, and has great application potential in water resources management.

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“…Over the last decade, uncrewed aerial vehicles (UAVs) have emerged as promising remote sensing platforms. The use of UAVs is motivated by their low cost, ease of deployment, autonomous operation, ability to fly under cloud cover, and ability to fill an important gap between airborne and terrestrial sensing modalities [13][14][15][16][17]. The recent availability of miniaturized sensing modalities and direct georeferencing technologies, together with the improved payload capacity of modern UAVs, are other factors that promote the use of such platforms in a wide range of applications, including archaeology [4].…”

Section: Introductionmentioning

confidence: 99%

UAV-Based Remote Sensing for Detection and Visualization of Partially-Exposed Underground Structures in Complex Archaeological Sites

Shin

Rastiveis

et al. 2023

Remote Sensing

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The utilization of remote sensing technologies for archaeology was motivated by their ability to map large areas within a short time at a reasonable cost. With recent advances in platform and sensing technologies, uncrewed aerial vehicles (UAV) equipped with imaging and Light Detection and Ranging (LiDAR) systems have emerged as a promising tool due to their low cost, ease of deployment/operation, and ability to provide high-resolution geospatial data. In some cases, archaeological sites might be covered with vegetation, which makes the identification of below-canopy structures quite challenging. The ability of LiDAR energy to travel through gaps within vegetation allows for the derivation of returns from hidden structures below the canopy. This study deals with the development and deployment of a UAV system equipped with imaging and LiDAR sensing technologies assisted by an integrated Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) for the archaeological mapping of Dana Island, Turkey. Data processing strategies are also introduced for the detection and visualization of underground structures. More specifically, a strategy has been developed for the robust identification of ground/terrain surface in a site characterized by steep slopes and dense vegetation, as well as the presence of numerous underground structures. The derived terrain surface is then used for the automated detection/localization of underground structures, which are then visualized through a web portal. The proposed strategy has shown a promising detection ability with an F1-score of approximately 92%.

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Line-based registration for UAV remote sensing imagery of wide-spanning river basin

Cited by 9 publications

References 7 publications

Land Cover Classification from fused DSM and UAV Images Using Convolutional Neural Networks

Land Cover Classification from fused DSM and UAV Images Using Convolutional Neural Networks

The PCA-NDWI Urban Water Extraction Model Based on Hyperspectral Remote Sensing

UAV-Based Remote Sensing for Detection and Visualization of Partially-Exposed Underground Structures in Complex Archaeological Sites

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