A Perceptually-Informed Approach to Vegetation Identification: Integrating Visual Characteristics and Contextual Information

A Perceptually-Informed Approach to Vegetation Identification: Integrating Visual Characteristics and Contextual Information

*¹Mustapha Aliyu, ²Isa Yunusa Chedi,

¹National Space Research & Development Agency, Obasanjo Space Centre, Musa Yar’Adua Way, Lugbe Abuja.

²National Oil Spill Detection & Response Agency, Abuja, Nigeria

*Corresponding Author

DOI: https://doi.org/10.51584/IJRIAS.2025.100700099

Received: 08 July 2025; Accepted: 15 July 2025; Published: 15 August 2025

ABSTRACT

This study explores the role of visual characteristics in vegetation identification using optical satellite images, with a focus on color information, texture analysis, shape and pattern recognition, and the integration of contextual information. Traditional pixel-based methods relying on isolated spectral signatures have limitations, particularly in complex and heterogeneous landscapes. Recent advances in remote sensing technology have enabled the collection of high-resolution data, providing a wealth of visual information that can be leveraged for more accurate vegetation identification. The study demonstrates the potential of advanced processing techniques, such as Object-based Image Analysis and deep learning models, in extracting and utilizing visual characteristics from high-resolution remote sensing data. These techniques can provide valuable insights into vegetation health, structure, and composition, enabling more informed decision-making. The findings of this study highlight the significance of visual characteristics in vegetation identification and demonstrate the potential of advanced processing techniques in improving land cover classification accuracy. The integration of visual characteristics with contextual information provides a more holistic approach to image analysis, enabling more accurate and robust classifications. This study contributes to the development of more effective and efficient methods for vegetation identification and classification using optical satellite images, and has implications for remote sensing applications in environmental monitoring and management.

Keywords: Vegetation Identification, Visual Characteristics, Remote Sensing, Object-based Image Analysis, Deep Learning

INTRODUCTION

The identification and classification of vegetation in optical satellite images have long been reliant on quantitative analysis of spectral signatures. However, with the increasing availability of high-resolution remote sensing data, qualitative visual characteristics such as color, texture, shape, and pattern have emerged as equally crucial information for accurate vegetation identification. This study explores the role of visual characteristics in vegetation identification, with a focus on color information, texture analysis, shape and pattern recognition, and the integration of contextual information.

Traditional pixel-based methods for vegetation classification have primarily relied on isolated spectral signatures. However, this approach has limitations, particularly in complex and heterogeneous landscapes. Recent advances in remote sensing technology have enabled the collection of high-resolution data, which provides a wealth of visual information that can be leveraged for more accurate vegetation identification.

Visual characteristics, including color, texture, shape, and pattern, provide valuable information for vegetation identification. Color information, particularly when analyzed in different spectral bands, can be used to distinguish between different vegetation types. Texture analysis techniques, such as Gray-Level Co-occurrence Matrix (GLCM) and Local Binary Patterns (LBP), can quantify the spatial arrangement of pixel values, providing detailed information on vegetation patterns and health. Shape and pattern recognition, particularly in high-resolution imagery, can provide critical discriminative information on vegetation structure and composition.

This study analyses the role of visual characteristics in vegetation identification, with a focus on studying the effectiveness of color information, texture analysis, shape and pattern recognition, and contextual information in vegetation identification. Further analysis in the study involves evaluating the potential of advanced processing techniques, such as Object-based Image Analysis (OBIA) and deep learning models, in extracting and utilizing visual characteristics from high-resolution remote sensing data.

MATERIALS AND METHOD

This study is a review of existing literature on the role of visual characteristics in vegetation identification. The research investigates the significance of visual attributes in identifying vegetation, emphasizing the potential of color information, texture analysis, shape and pattern recognition, and contextual integration to enhance accurate vegetation classification.

A systematic literature review was conducted, utilizing Google Scholar as the primary search engine. The search was initially customized to retrieve publications from the past five years; this is from 2020 to date. Twenty-one studies were obtained within this time frame. However, few earlier studies i.e. four studies, were included where relevant. This approach enabled better investigation to explore the application of visual attributes that include color, texture, shape, and pattern, in vegetation identification, with an emphasis on integrating contextual information for improved accuracy.

Role of Color Information

Color plays a fundamental role in human visual interpretation of images, and its systematic analysis enhances automated vegetation identification. When individual spectral bands are viewed separately, images appear in grayscale. However, combining the Red, Green, and Blue (RGB) bands creates a colorful image that closely resembles human perception. This true-color composite is often the initial point of analysis for contextual understanding and object identification. False coloring is a powerful technique that assigns artificial colors to different bands of the electromagnetic spectrum, thereby enhancing interpretation and facilitating the distinction of various features, including vegetation, water bodies, or urban areas. For instance, a common false-color composite combines Near-Infrared, Red, and Green bands, which effectively highlights vegetation in vibrant red hues, making it readily discernible.

Beyond simple RGB composites, the Hue-Saturation-Value (HSV) color space offers a more robust representation of color under variable illumination conditions compared to the direct RGB model. Specifically, the hue coordinate (H) has been identified as providing highly useful information for image segmentation in certain studies, particularly for differentiating vegetation from bare ground or shadowed areas. This capability is especially valuable in photogrammetric processes where the high cost of multi-spectral cameras might be prohibitive, allowing for effective segmentation using only standard RGB data.

Texture Analysis Techniques

As discussed by Sawyer et al. (2018), Matarira et al. (2022) and Erdem & Bayrak (2023), texture analysis involves quantifying the spatial arrangement of pixel values within an image to identify patterns and features that are not discernible through spectral values alone. This technique provides detailed information critical for characterizing land cover types, assessing soil moisture, and evaluating vegetation health.

Several common techniques are employed for texture analysis:

• GLCM: As reviewed from Iqbal et al. (2021), Zhou et al. (2021), Baek & Yun (2022), Mohammadpour et al. (2022). Chaudhary & Kumar (2024), Alzhanov & Nugumanova (2024), GLCM is a statistical method that analyzes the frequency of co-occurring pixel values within a defined window, making it one of the most widely used texture analysis techniques in remote sensing. Features extracted from GLCM, such as contrast, dissimilarity, homogeneity, Angular Second Moment (ASM), and entropy, provide quantitative measures of image texture.
• LBP. This was studied from some literature that include Azeez et al. (2021), Chairet et al. (2021), Li et al. (2021), Wang et al. (2022). It is a structural method that focuses on analyzing the spatial arrangement of pixel values, capturing local texture characteristics.
• Gabor Filters: As reviewed from Bhatti et al. (2021), Cruz-Ramos et al. (2021), Jia et al. (2021), Oppong et al. (2022) and Kumar and Garg (2023), are a type of band-pass filter particularly effective for analyzing texture at different frequencies and orientations, proving useful for images exhibiting periodic patterns.
• Wavelet Transforms: This technique allows for the analysis of texture across multiple scales and orientations, providing a multi-resolution perspective on image patterns.

Texture analysis has been shown to significantly improve land cover classification accuracy, in some cases by up to 20% compared to traditional methods. It is frequently employed for characterizing vegetation patterns and serves as a valuable input for various vegetation classification tasks.

Shape and Pattern Recognition

Shape and pattern recognition of land cover features in satellite image was discussed by Chen et al. (2018), Rajbhandari (2019) and Ma et al. (2024). The geometric properties of objects and the spatial arrangement of vegetation elements provide critical discriminative information.

• Object-based Geometric Features: In OBIA, beyond the spectral values, explicit use is made of spatial properties such as the size, shape, compactness, and density of delineated objects as features for classification. These geometric features are particularly valuable for high-resolution imagery, where individual vegetation units or land cover patches are visually composed of numerous pixels, allowing for detailed shape analysis.
• Spatial Pattern Analysis: In vegetation science, spatial patterns are recognized beyond simple randomness, including regular and aggregated dispersion. These patterns can be influenced by underlying environmental gradients, such as zonation observed in mangrove forests due to varying salinity tolerance levels. Remote sensing offers a powerful means to detect and analyze differences in spatial and temporal patterns, such as those resulting from grazing impacts on vegetation. Pattern recognition, as a broader concept, is an important aspect of digital image analysis in remote sensing, significantly enhancing classification and analytical capabilities.

Integration of Contextual Information

Contextual information, which describes the relationships and associations of neighboring pixel values or objects, has been demonstrated to substantially improve image classification results (Li et al., 2014). Remote sensing images are characterized by their distinct spectral, spatial, radiometric, and temporal properties, all of which contribute to effective vegetation mapping. The capacity of remote sensing to capture the diverse traits of vegetation and geodiversity is rooted in the fact that the spectral reflectance and absorption of pixels in an optical image are a direct outcome of complex interactions between incoming light (influenced by the atmosphere) and a multitude of vegetation characteristics, including phylogenetic, biophysical, biochemical, morphological, physiological, and phenotypic attributes (Tian et al., 2022; Lausch et al., 2024; Torresani et al., 2024).

Deep learning methods, particularly Convolutional Neural Networks (CNNs), are highly effective in representing and exploiting these intricate spatial patterns and contextual information from very high spatial resolution data. The integration of visual characteristics, such as color, texture, shape, and pattern, along with contextual information, signifies a fundamental shift from purely spectral classification towards a more holistic, perceptually-informed approach that closely mirrors human interpretation. Traditional pixel-based methods primarily rely on isolated spectral signatures. However, the analysis highlights that texture analysis improves accuracy by quantifying spatial arrangements, OBIA effectively leverages the shape and size of objects, and the inclusion of contextual information, such as neighborhood relationships, enhances overall classification results.

The application of the HSV color space, with a particular emphasis on hue for segmentation, further demonstrates the move beyond raw RGB values. The strength of deep learning lies in its inherent ability to automatically extract and learn from these complex spatial patterns and higher-level features. This evolution suggests a progression in remote sensing interpretation that increasingly emulates how humans perceive and categorize objects, by considering not just the individual pixel’s color but also its spatial arrangement, form, and relationship to its surroundings. This leads to more robust and accurate classifications, especially within complex and heterogeneous landscapes.

Furthermore, the value of visual characteristics is significantly amplified with higher spatial resolution imagery, necessitating the development of advanced processing techniques that can effectively extract and utilize these intricate features. OBIA is specifically highlighted as a promising approach for “handling high-resolution imagery” and for addressing the “H-resolution problem” that arises with finer spatial detail. Similarly, the capacity of Convolutional Neural Networks (CNNs) to exploit “spatial patterns particularly facilitates the value of very high spatial resolution data”. The demonstrated effectiveness of texture analysis in improving land cover classification accuracy also becomes more pronounced as the level of detail in the imagery increases. This establishes a direct relationship: as spatial resolution improves, the richness of visual characteristics, including fine-scale texture, precise shape, and intricate patterns, becomes more apparent and provides greater discriminative power. However, extracting and effectively utilizing this information requires sophisticated algorithms, such as OBIA’s segmentation capabilities, GLCM for texture quantification, or the hierarchical feature learning inherent in deep learning models. These advanced techniques are essential for moving beyond simplistic pixel-wise spectral analysis and fully leveraging the complex spatial relationships present in high-resolution remote sensing data.

CONCLUSION

This study highlights the significance of visual characteristics, including color, texture, shape, and pattern, in vegetation identification using optical satellite images. Color information plays a fundamental role in human visual interpretation and can be systematically analyzed to enhance automated vegetation identification. Texture analysis provides detailed information on vegetation patterns and health, and can significantly improve land cover classification accuracy. Shape and pattern recognition offer critical discriminative information on vegetation structure and composition, particularly in high-resolution imagery. The integration of these visual characteristics with contextual information provides a more holistic approach to image analysis, enabling more accurate and robust classifications.

The study demonstrates the potential of advanced processing techniques, such as OBIA and deep learning models, in extracting and utilizing visual characteristics from high-resolution remote sensing data. These techniques are essential for fully leveraging the complex spatial relationships present in high-resolution data. Advanced processing techniques can provide valuable insights into vegetation health, structure, and composition, enabling more informed decision-making. Overall, this study contributes to the development of more effective and efficient methods for vegetation identification and classification using optical satellite images.

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