Object-Based Shadow Index via Illumination Intensity from High Resolution Satellite Images over Urban Areas

Fu, Haoyu; Zhou, Tingting; Sun, Chenglin

doi:10.3390/s20041077

Cited by 8 publications

(8 citation statements)

References 47 publications

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“…Multispectral (MS) image is of great importance in typical remote sensing applications, such as land utilization [ 1 ], urban interpretation [ 2 ], urban monitoring [ 3 ], and scene understanding [ 4 ] tasks. However, owing to the physical constraints of the optical satellite sensor and its communication bandwidth, the spectral diversity and the high spatial resolution cannot be obtained at the same time.…”

Section: Introductionmentioning

confidence: 99%

An Improved Pulse-Coupled Neural Network Model for Pansharpening

Yan

Xie

et al. 2020

Sensors

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Pulse-coupled neural network (PCNN) and its modified models are suitable for dealing with multi-focus and medical image fusion tasks. Unfortunately, PCNNs are difficult to directly apply to multispectral image fusion, especially when the spectral fidelity is considered. A key problem is that most fusion methods using PCNNs usually focus on the selection mechanism either in the space domain or in the transform domain, rather than a details injection mechanism, which is of utmost importance in multispectral image fusion. Thus, a novel pansharpening PCNN model for multispectral image fusion is proposed. The new model is designed to acquire the spectral fidelity in terms of human visual perception for the fusion tasks. The experimental results, examined by different kinds of datasets, show the suitability of the proposed model for pansharpening.

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Section: Introductionmentioning

confidence: 99%

An Improved Pulse-Coupled Neural Network Model for Pansharpening

Yan

Xie

et al. 2020

Sensors

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“…The first distribution is called the "U" type and is characteristic of coves with NE and NW exposures. The shadow minimum at noon eliminates the impact of high relief or rich bottom vegetation during mid-day flights [48]. The "L" distribution is the second type, characteristic of glaciers with W/NW exposures, and it features a minimum in the late afternoon/evening hours.…”

Section: Discussionmentioning

confidence: 98%

“…The third type, called "the reflected L" distribution, is characteristic of glaciers with SE exposure with a minimum in the early morning hours. Flights in the early morning and late evening are suitable for "L" and "reflected L" types and minimize the glare effect [48].…”

Section: Discussionmentioning

confidence: 99%

“…In the existing algorithms dealing with shadows within water bodies it is required that the water be homogeneous [31]; however, even in the case of pure water in the area with topographic shadows, the reflection in the entire shadow area is not uniformly reduced. It can be assumed that the occurrence of 'strong shadow and weak shadow' also applies to water areas [48], which in this case is enhanced by low values of shortwave downward radiation and a small supply of solar energy both in the sunny area and in the shade. This is characterized by the fact that in the area of the cove furthest from the shadow-casting obstacle, the difference between the amount of radiation reflected in the nonshaded and shaded parts is greater than in the shallower area near this obstacle.…”

Section: Discussionmentioning

confidence: 99%

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The Influence of Shadow Effects on the Spectral Characteristics of Glacial Meltwater

Wójcik-Długoborska

Bialik

2020

Remote Sensing

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The phenomenon of shadows due to glaciers is investigated in Antarctica. The observed shadow effect disrupts analyses conducted by remote sensing and is a challenge in the assessment of sediment meltwater plumes in polar marine environments. A DJI Inspire 2 drone equipped with a Zenmuse x5s camera was used to generate a digital surface model (DSM) of 6 King George Island glaciers: Ecology, Dera, Zalewski, Ladies, Krak, and Vieville. On this basis, shaded areas of coves near glaciers were traced. For the first time, spectral characteristics of shaded meltwater were observed with the simultaneous use of a Sequoia+ spectral camera mounted on a Parrot Bluegrass drone and in Landsat 8 satellite images. In total, 44 drone flights were made, and 399 satellite images were analyzed. Among them, four drone spectral images and four satellite images were selected, meeting the condition of a visible shadow. For homogeneous waters (deep, low turbidity, without ice phenomena), the spectral properties tend to change during the approach to an obstacle casting a shadow especially during low shortwave downward radiation. In this case, in the shade, the amount of radiation reflected in the green spectral band decreases by 50% far from the obstacle and by 43% near the obstacle, while in near infrared (NIR), it decreases by 42% and 21%, respectively. With highly turbid, shallow water and ice phenomena, this tendency does not occur. It was found that the green spectral band had the highest contrast in the amount of reflected radiation between nonshaded and shaded areas, but due to its high sensitivity, the analysis could have been overestimated. The spectral properties of shaded meltwater differ depending on the distance from the glacier front, which is related to the saturation of the water with sediment particles. We discovered that the pixel aggregation of uniform areas caused the loss of detailed information, while pixel aggregation of nonuniform, shallow areas with ice phenomena caused changes and the loss of original information. During the aggregation of the original pixel resolution (15 cm) up to 30 m, the smallest error occurred in the area with a homogeneous water surface, while the greatest error (over 100%) was identified in the places where the water was strongly cloudy or there were ice phenomena.

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“…The observation of the object reflectance by multispectral images is a key technique to model the physical process of light interacting with materials [11]. Moreover, the multispectral images have been used for the shadow removal in urban spaces [12] and image segmentation of materials [13,14]. In the field of computer vision, some previous works propose different approaches for plant phenotyping considering the spectral response [15], the recognition of different geological formations using a multispectral camera [16] and the detection of urban materials [17,18].…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation of Natural Materials on a Point Cloud Using Spatial and Multispectral Features

Jurado

Cárdenas

Ogáyar

et al. 2020

Sensors

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The characterization of natural spaces by the precise observation of their material properties is highly demanded in remote sensing and computer vision. The production of novel sensors enables the collection of heterogeneous data to get a comprehensive knowledge of the living and non-living entities in the ecosystem. The high resolution of consumer-grade RGB cameras is frequently used for the geometric reconstruction of many types of environments. Nevertheless, the understanding of natural spaces is still challenging. The automatic segmentation of homogeneous materials in nature is a complex task because there are many overlapping structures and an indirect illumination, so the object recognition is difficult. In this paper, we propose a method based on fusing spatial and multispectral characteristics for the unsupervised classification of natural materials in a point cloud. A high-resolution camera and a multispectral sensor are mounted on a custom camera rig in order to simultaneously capture RGB and multispectral images. Our method is tested in a controlled scenario, where different natural objects coexist. Initially, the input RGB images are processed to generate a point cloud by applying the structure-from-motion (SfM) algorithm. Then, the multispectral images are mapped on the three-dimensional model to characterize the geometry with the reflectance captured from four narrow bands (green, red, red-edge and near-infrared). The reflectance, the visible colour and the spatial component are combined to extract key differences among all existing materials. For this purpose, a hierarchical cluster analysis is applied to pool the point cloud and identify the feature pattern for every material. As a result, the tree trunk, the leaves, different species of low plants, the ground and rocks can be clearly recognized in the scene. These results demonstrate the feasibility to perform a semantic segmentation by considering multispectral and spatial features with an unknown number of clusters to be detected on the point cloud. Moreover, our solution is compared to other method based on supervised learning in order to test the improvement of the proposed approach.

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Object-Based Shadow Index via Illumination Intensity from High Resolution Satellite Images over Urban Areas

Cited by 8 publications

References 47 publications

An Improved Pulse-Coupled Neural Network Model for Pansharpening

An Improved Pulse-Coupled Neural Network Model for Pansharpening

The Influence of Shadow Effects on the Spectral Characteristics of Glacial Meltwater

Semantic Segmentation of Natural Materials on a Point Cloud Using Spatial and Multispectral Features

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