Remote Sensing Satellite Image Processing Techniques for Image Classification: A Comprehensive Survey

Sowmya, D R; Shenoy, P. Deepa; Venugopal, K R

doi:10.5120/ijca2017913306

Cited by 29 publications

(7 citation statements)

References 77 publications

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“…The Omo Delta was classified using version 4.6.14 of the ‘randomForest’ package (Liaw & Wiener, 2002) of the R statistical software (R Core Team, 2020) version 4.0.1. Prior to classification, preprocessing steps, such as mosaicking, clipping and feature‐based image‐to‐image registration method (Moigne et al, 2011; Sowmya et al, 2017), were applied to the images which were finally projected to WGS84 zone 37N UTM, ensuring that the images were spatially aligned in the same projection system and that delta extent could be estimated in square kilometers.…”

Section: Methodsmentioning

confidence: 99%

Variation of the Omo Delta between 1990 and 2018: What remote sensing data reveal and models explain

Kangi,

Dondeyne,

Kleinschroth

et al. 2023

Land Degrad Dev

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Deltas, being areas where river sediments accumulate, are sensitive to changes in climate and anthropogenic processes that affect sediment generation. The Omo Delta, on the Ethiopia–Kenya border, is constantly changing due to fluctuating lake levels, a variable climate, and rapidly changing land use. Due to field data scarcity and the region's limited accessibility, we relied on remote sensing (RS) data to investigate delta extent between 1990 and 2018 and attempted to disentangle the effect of climatic from anthropogenic variables. We obtained a time series of delta extent from Landsat imagery using Random Forest (RF) classification. Using parametric and non‐parametric regression techniques, we regressed the delta extent on Lake Turkana levels, Omo River discharges, Omo‐Gibe Basin rainfall, tree cover loss, and irrigation extent. The RF algorithm distinguished water from land with high (>90%) accuracies and revealed Omo Delta extent fluctuations ranging from 949 km2 in 1993 to 651 km2 in 2000. Lake water level, which depends on the rainfall over the Omo‐Turkana Basin, emerged as the best predictor of delta extent. However, the annual rainfall over the Omo‐Gibe Basin showed no correlation with delta extent. The regression models further show a connection between delta extent, irrigation extent, and tree cover loss. We conclude that rainfall indirectly influences delta extent across the Omo‐Turkana Basin. Regression models indicate additional cumulative effects of human activity in the Omo‐Gibe Basin but fall short in explaining delta dynamics.

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Section: Methodsmentioning

confidence: 99%

Variation of the Omo Delta between 1990 and 2018: What remote sensing data reveal and models explain

Kangi,

Dondeyne,

Kleinschroth

et al. 2023

Land Degrad Dev

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“…Image classification, the process of categorizing all pixels in an image to obtain a specific set of labels for land cover themes, was applied [ 28 ]. Supervised classification method was used to classify the images.…”

Section: Methodsmentioning

confidence: 99%

Land Use and Land Cover Dynamics: Deriving Forces and Perceptions of Local Community in Derashe, Southern Ethiopia

Taye,

Anbacha,

Girma

2023

The Scientific World Journal

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Changes in land use and land cover (LULC) reached a point where we must reconcile. Thus, this study was conducted to assess the LULC changes, deriving forces, and the local community perceptions in Derashe, Southern Ethiopia. Landsat Series sensors’ 4, 5, and 8 imagery was used to analyze the LULC over the year. Focus group discussions (FGDs) and key informant interviews (KIIs) were used to assess the deriving force and perceptions. Maximum likelihood of the supervised digital image classification technique was employed. Postclassification was used to detect and quantify changes. Deriving force and perception data were analyzed through descriptive statistics and narration. Five major LULC classes were identified. A major increase was observed in settlement and agricultural land by displacing forest and bush lands. However, the water body was fluctuated. According to the local community, the agricultural land expansion and growing population pressure are the major proximate and underlying deriving forces, respectively. Therefore, proper design for efficient use is necessary.

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“…In a general sense, image classification can be defined as -the process of categorizing all pixels in an image or raw remotely sensed satellite data to obtain a given set of labels or land cover themes [141]. In supervised classification, available known pixels are used by the analyst to generate representative parameters for each class of interest.…”

Section: ) Classificationmentioning

confidence: 99%

Applications and Challenges of Artificial Intelligence in Space Missions

2024

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Artificial Intelligence (AI) is increasingly finding acceptance in the space community, especially Machine Learning (ML), a subdomain of AI. ML algorithms now find numerous applications in autonomous navigation, spacecraft health monitoring and operational management of satellite constellations. However, a large number of surveys on the applications of AI in space missions can be classified into two categories. The first category suffers from the limitation of being old and not covering some crucial and recent developments in the field; such as the contributions of Deep Learning (DL) and bioinspired AI algorithms. The challenge with the second category lies in its being too detailed with respect to the development and application of specific AI techniques or algorithms. These limitations have necessitated the need to have a concise survey with a wider scope for those interested in the applications and challenges of AI in the space industry, especially those with technical backgrounds in other fields. In this paper, we surveyed the use of traditional AI techniques in various domains of space missions without delving into formal methods. Some bioinspired AI algorithms were also surveyed and their potential application areas highlighted. Unlike similar surveys that focus only on technological challenges, we also addressed some crucial legal drawbacks that emanate from the reliance and use of AI in space. Summarily, while discussing future directions we reviewed some advancements in Internet of Things (IoT) and Blockchain technologies. Our review prioritized three application domains positioned to benefit immensely from the inevitable AI-Blockchain convergence in the space community. These include the Internet of Space Things (IoST), Satellite Communication and Spacecraft Data Security.

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Remote Sensing Satellite Image Processing Techniques for Image Classification: A Comprehensive Survey

Cited by 29 publications

References 77 publications

Variation of the Omo Delta between 1990 and 2018: What remote sensing data reveal and models explain

Variation of the Omo Delta between 1990 and 2018: What remote sensing data reveal and models explain

Land Use and Land Cover Dynamics: Deriving Forces and Perceptions of Local Community in Derashe, Southern Ethiopia

Applications and Challenges of Artificial Intelligence in Space Missions

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