A Geostatistical Approach to Spatial Quality Assessment of Coarse Spatial Resolution Remote Sensing Products

A geostatistical framework for spatial quality assessment framework of coarse resolution remote sensing products is presented that can account for either the scale difference or the uncertainty of reference value prediction at coarse resolutions. A set of multiple reference field realizations is first generated at a fine spatial resolution using geostatistical simulation to explore the uncertainty in the true unknown reference field. The upscaling of multiple reference field realizations to coarse resolution is then followed to match the spatial resolution of the target remote sensing product and create coarse resolution reference fields. The simulated reference values at each coarse pixel are compared to the corresponding reported value from the coarse resolution remote sensing product, yielding alternative error values, from which several location-dependent statistics such as mean error, mean absolute error, and probability of overestimation can be computed. An experiment involving monthly Tropical Rainfall Measuring Mission (TRMM) precipitation products and point-level rain gauge data over South Korea illustrates the applicability of the proposed approach. The spatially distributed error statistics are useful to identify areas with larger errors and the degree of overestimation in the study area, leading to the identification of areas with unreliable estimates within the TRMM precipitation products. Therefore, it is expected that the geostatistical assessment framework presented in this paper can be effectively used to evaluate the spatial quality of coarse resolution remote sensing products.

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A REVIEW ON SPATIAL QUALITY ASSESSMENT METHODS FOR EVALUATION OF PAN-SHARPENED SATELLITE IMAGERY

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w18-255-2019 ◽

2019 ◽

Vol XLII-4/W18 ◽

pp. 255-261

Author(s):

F. Dadras Javan ◽

F. Samadzadegan ◽

S. Mehravar ◽

A. Toosi

Keyword(s):

Remote Sensing ◽

Quality Assessment ◽

Satellite Imagery ◽

Urban Areas ◽

Large Scale ◽

Spatial Distortion ◽

Sensing Applications ◽

Fused Image ◽

Spatial Quality

Abstract. Nowadays, high-resolution fused satellite imagery is widely used in multiple remote sensing applications. Although the spectral quality of pan-sharpened images plays an important role in many applications, spatial quality becomes more important in numerous cases. The high spatial quality of the fused image is essential for extraction, identification and reconstruction of significant image objects, and will result in producing high-quality large scale maps especially in the urban areas. This paper introduces the most sensitive and effective methods in detecting the spatial distortion of fused images by implementing a number of spatial quality assessment indices that are utilized in the field of remote sensing and image processing. In this regard, in order to recognize the ability of quality assessment indices for detecting the spatial distortion quantity of fused images, input images of the fusion process are affected by some intentional spatial distortions based on non-registration error. The capabilities of the investigated metrics are evaluated on four different fused images derived from Ikonos and WorldView-2 initial images. Achieved results obviously explicate that two methods namely Edge Variance Distortion and the spatial component of QNR metric called Ds are more sensitive and responsive to the imported errors.

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REVIEW OF METHODS FOR DETERMINING THE SPATIAL RESOLUTION OF UAV SENSORS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-1-w4-391-2015 ◽

2015 ◽

Vol XL-1/W4 ◽

pp. 391-395 ◽

Cited By ~ 8

Author(s):

A. Orych

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Quality Assessment ◽

Spatial Resolution ◽

High Frequency ◽

Frame Size ◽

Flight Stability ◽

Amount Of Information ◽

Imaging Sensor

The ground resolved distance (GRD) of an imaging sensor, i.e. the size of the smallest element distinguishable on acquired imagery, is one of the most important sensor quality assessment factors, as it is directly linked to the amount of information that can be derived from the end product. The paper is a review of a wide variety of calibration targets used for determining the spatial resolution of remote sensing sensors. The author provides a description of calibration targets used historically and then moves on to high-frequency targets used for high-resolution remote sensing imaging equipment. As analysis is made which of these types of targets are best suited for UAV sensors, taking into account parameters very specific to UAVs: frame size, small GSD values and low flight stability.

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High spatial resolution remote sensing image segmentation based on the multiclassification model and the binary classification model

Neural Computing and Applications ◽

10.1007/s00521-020-05561-8 ◽

2021 ◽

Author(s):

Xiaoxiong Zheng ◽

Tao Chen

Keyword(s):

Remote Sensing ◽

Image Segmentation ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Binary Classification ◽

Remote Sensing Image ◽

Classification Model

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Development of an Image Registration Technique for Fluvial Hyperspectral Imagery Using an Optical Flow Algorithm

Sensors ◽

10.3390/s21072407 ◽

2021 ◽

Vol 21 (7) ◽

pp. 2407

Author(s):

Hojun You ◽

Dongsu Kim

Keyword(s):

Remote Sensing ◽

Image Registration ◽

Optical Flow ◽

Hyperspectral Imaging ◽

Spatial Resolution ◽

Template Matching ◽

Hyperspectral Images ◽

Spatial Accuracy ◽

Flow Algorithm ◽

Fluvial Remote Sensing

Fluvial remote sensing has been used to monitor diverse riverine properties through processes such as river bathymetry and visual detection of suspended sediment, algal blooms, and bed materials more efficiently than laborious and expensive in-situ measurements. Red–green–blue (RGB) optical sensors have been widely used in traditional fluvial remote sensing. However, owing to their three confined bands, they rely on visual inspection for qualitative assessments and are limited to performing quantitative and accurate monitoring. Recent advances in hyperspectral imaging in the fluvial domain have enabled hyperspectral images to be geared with more than 150 spectral bands. Thus, various riverine properties can be quantitatively characterized using sensors in low-altitude unmanned aerial vehicles (UAVs) with a high spatial resolution. Many efforts are ongoing to take full advantage of hyperspectral band information in fluvial research. Although geo-referenced hyperspectral images can be acquired for satellites and manned airplanes, few attempts have been made using UAVs. This is mainly because the synthesis of line-scanned images on top of image registration using UAVs is more difficult owing to the highly sensitive and heavy image driven by dense spatial resolution. Therefore, in this study, we propose a practical technique for achieving high spatial accuracy in UAV-based fluvial hyperspectral imaging through efficient image registration using an optical flow algorithm. Template matching algorithms are the most common image registration technique in RGB-based remote sensing; however, they require many calculations and can be error-prone depending on the user, as decisions regarding various parameters are required. Furthermore, the spatial accuracy of this technique needs to be verified, as it has not been widely applied to hyperspectral imagery. The proposed technique resulted in an average reduction of spatial errors by 91.9%, compared to the case where the image registration technique was not applied, and by 78.7% compared to template matching.

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