Paradigm shifts in super-resolution techniques for remote sensing applications

Rohith, G.; Kumar, Lakshmi Sutha

doi:10.1007/s00371-020-01957-8

Cited by 21 publications

(14 citation statements)

References 133 publications

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“…Conventionally, these algorithms were tested earlier for the real-world datasets (scenery, faces, buildings, etc.). With the inspiration from the successful results of real-world datasets, these techniques imparted for the remotely sensed images reported in [34] and [35]. The test images mentioned in Section-2 are tested with the SR algorithms.…”

Section: A Experimentation Results For Sr Techniques (I) Qualitative Analysismentioning

confidence: 99%

“…The quality of structures after algorithmic processing is characterized by Feature Similarity (FSIM) [35]. The FSIM index for full reference Image quality assessment (IQA) is proposed because the human visual system (HVS) understands an image mainly according to its low-level features by Phase Congruency (PC).…”

Section: ) Feature Similarity (Fsim)mentioning

confidence: 99%

“…The lower the value, the greater the independence of the image from blocking objects and noise. Typically, the range values 0-20, 21-35, 36-50, 51-80, and 81-100 indicate different quality scales, namely, "Excellent", "Good", "Fair", "Poor", and "Bad", respectively [34,35]. It is seen from Table .6 (Column-6) that the input image has "Good" quality, and on applying SR algorithms, the image is elevated to the "Excellent" quality scale.…”

Section: ) Perception-based Image Quality Evaluator (Piqe)mentioning

confidence: 99%

“…Table .8 shows the FVC calculation in two aspects, which are experimented with to validate the effectiveness of FVC. One is grating-based, and the other is using another vegetative defining metric-LAI [35]. In the Grading method, FVC using NDVI value is calculated as shown in Eq.…”

Section: Fvc Calculation For Both Phasesmentioning

confidence: 99%

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Effectiveness of Super-Resolution Technique on Vegetation Indices

Rohith

Kumar

2021

IEEE Access

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The identification and interpretation of remote sensed (RS) objects in an image depend on how well the sensor captures the region. In rare cases, RS images may be vulnerable to a lack of interpretability issues in some parts of the image due to the sensor's limits and preprocessing techniques. Conventionally, the interpretation of the land cover pattern's shape and size is apparent when the distance between the sensor and object is closer to the visualization level of objects and viable with digital airborne imagery. In this paper, integration of the Super-Resolution (SR) technique in the high-resolution imagery to achieve the closer visualization level for mapping the vegetation is proposed. This approach enables the higher interpretive potential to define the land pattern's shape and size with very high spatial resolution, closer proximity, detailed and distinguishable patterns. This approach helps to precisely predict the total vegetated study area for land use and land cover changes (LULCC) and chlorophyll-rich vegetation applications. The proposed algorithm is carried out in two phases. In the first phase, the SR technique applied test images are tested for vegetation detection and mapping the vegetation information in a region with fourteen vegetation metrics. In the second phase, similar testing is done without applying SR for input test images. The experiment revealed that test images using the SR technique yielded higher average values of 5 percent and 1.1 percent for the Normalized Difference Vegetation Index (NDVI) and Fractional Vegetation Cover (FVC), respectively, as compared to images tested using the non-SR technique.

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Section: A Experimentation Results For Sr Techniques (I) Qualitative Analysismentioning

confidence: 99%

Section: ) Feature Similarity (Fsim)mentioning

confidence: 99%

Section: ) Perception-based Image Quality Evaluator (Piqe)mentioning

confidence: 99%

Section: Fvc Calculation For Both Phasesmentioning

confidence: 99%

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Effectiveness of Super-Resolution Technique on Vegetation Indices

Rohith

Kumar

2021

IEEE Access

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“…Since their development in 2014, generative adversarial training algorithms have been widely used in various unimodal applications such as scene generation [17], imageto-image translation [18], and image super-resolution [224,225]. To obtain the latest advances in super-resolution algorithms for a variety of remote sensing applications, we invite the reader to refer to the excellent survey article by Rohith et al [226].…”

Section: Generative Adversarial Network Basedmentioning

confidence: 99%

A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets

et al. 2021

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The research progress in multimodal learning has grown rapidly over the last decade in several areas, especially in computer vision. The growing potential of multimodal data streams and deep learning algorithms has contributed to the increasing universality of deep multimodal learning. This involves the development of models capable of processing and analyzing the multimodal information uniformly. Unstructured real-world data can inherently take many forms, also known as modalities, often including visual and textual content. Extracting relevant patterns from this kind of data is still a motivating goal for researchers in deep learning. In this paper, we seek to improve the understanding of key concepts and algorithms of deep multimodal learning for the computer vision community by exploring how to generate deep models that consider the integration and combination of heterogeneous visual cues across sensory modalities. In particular, we summarize six perspectives from the current literature on deep multimodal learning, namely: multimodal data representation, multimodal fusion (i.e., both traditional and deep learning-based schemes), multitask learning, multimodal alignment, multimodal transfer learning, and zero-shot learning. We also survey current multimodal applications and present a collection of benchmark datasets for solving problems in various vision domains. Finally, we highlight the limitations and challenges of deep multimodal learning and provide insights and directions for future research.

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