Optimization of DTM Interpolation Using SFS with Single Satellite Imagery

Rajabi, Majid; Blais, J. A. R.

doi:10.1023/b:supe.0000020178.66165.f3

Cited by 18 publications

(7 citation statements)

References 20 publications

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“…Rajabi, J.A.R. Blais (14), a Classifier that would take into account the technique of stereo measurements on the remotely sensed images using the Shape For Shading method to derive geometric information about the objects in the images. (17,18).…”

Section: Digital Terrain Model (Dtm)mentioning

confidence: 99%

Review of Advanced Image Processing Techniques: Digital Elevation Model

Prasad¹

2022

IJRASET

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This paper gives an in depth insight into the current as well as the previous systems or techniques that have been used in order to perform advanced image processing. These systems or techniques have been implemented or tested in the areas of Germany, The United States of America, India, China, Indonesia and many more. The review aims to explore challenges and opportunities in the field computational and image processing areas of the industry.

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Section: Digital Terrain Model (Dtm)mentioning

confidence: 99%

Review of Advanced Image Processing Techniques: Digital Elevation Model

Prasad¹

2022

IJRASET

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“…DSMs derived from suitable historical aerial photographs, therefore, represent dependable sources of 3D data that can be used to better analyze long-term geomorphic changes in coastal dune areas that have undergone retreat. [10] The paper describes that Digital Terrain Models (DTMs) are simply regular grids of elevation measurements over the land surface. DTMs are mainly produced by applying the technique of stereo measurements on images from photogrammetry and remote sensing.…”

Section: Structure-from-motion-derived Digital Surface Models From Hi...mentioning

confidence: 99%

Visualization of a Digital Elevation Model using Advanced Image Processing

Prasad¹,

Vidhate²,

Patiyat³

2022

IJRASET

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“…The researches on height estimation in remote sensing is relatively few. Early efforts employed the shadow shape to estimate the height of the artificial satellite image with only several control points [60] or a sparse DTM [61]. Following the CNN-based depth estimation methods, [36] predicted height map by introducing a encoder-decoder network encompassing residual learning and a skip connection.…”

Section: B Height Estimationmentioning

confidence: 99%

HECR-Net: Height-Embedding Context Reassembly Network for Semantic Segmentation in Aerial Images

Liu¹,

Zhang²,

Sun³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Semantic segmentation in aerial images has become an indispensable part in remote sensing image understanding for its extensive application prospects. It is crucial to jointly reason the 2D appearance along with 3D information and acquire discriminative global context to achieve better segmentation. However, previous approaches require accurate elevation data (e.g., nDSM and DSM) as additional inputs to segment semantics which sorely limits their applications. On the other hand, due to the various forms of objects in complex scenes, the global context is generally dominated by features of salient patterns (e.g., large objects) and tends to smooth inconspicuous patterns (e.g., small stuff and boundaries). In this article, a novel joint framework named Height-Embedding Context Reassembly Network (HECR-Net) is proposed. First, considering the fact that the corresponding elevation data is insufficient while we still want to exploit the serviceable height information. To alleviate the above data constraint, our method simultaneously predicts semantic labels and height maps from single aerial images by distilling height-aware embeddings implicitly. Second, we introduce a novel context-aware Reorganization (CAR) module to generate a discriminative feature with global context appropriately assigned to each local position. It benefits from both the global context aggregation module for ambiguity eliminating and local feature redistribution module for detail refinement. Third, we make full use of the learning height-aware embeddings to promote the performance of semantic segmentation via introducing a modality-affinitive propagation (MAP) block. Finally, without bells and whistles, the segmentation results on ISPRS Vaihingen and Potsdam data set illustrate that the proposed HECR-Net achieves state-of-the-art performance.

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Optimization of DTM Interpolation Using SFS with Single Satellite Imagery

Cited by 18 publications

References 20 publications

Review of Advanced Image Processing Techniques: Digital Elevation Model

Review of Advanced Image Processing Techniques: Digital Elevation Model

Visualization of a Digital Elevation Model using Advanced Image Processing

HECR-Net: Height-Embedding Context Reassembly Network for Semantic Segmentation in Aerial Images

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