In the realm of remote sensing, scene classification has gained prominence as a fundamental application of pre-existing systems in varied contexts. Essentially, scene classification implies the algorithmic identification and characterization of earth observation imagery from satellite platforms or aerial reconnaissance. This methodology leverages technological advancements to categorize, predict, and study different areas accurately.

Unquestionably, scene classification in remote sensing facilitates efficient analysis of geographical areas and their alterations over time. This exploratory tool could decode complex features, weather conditions, vegetation patterns, landforms and even human activities.

Reliable Data Source - Remote sensing platforms, like drones, aeroplanes, satellites, collect meticulous data which form the core of scene classification. This data is constantly updated and readily available for interpretation and analysis.

Advanced Algorithms - Leveraging advancements in machine learning and Artificial Intelligence, scene classification boasts of sophisticated algorithms capable of characterizing and cataloguing diverse geographic environments immensely accurately.

Dynamic Adaptability - This system acknowledges varied geographic needs, and significantly adapts to changing conditions. Leveraging scene classification within remote sensing enhances its ability to monitor shifting environmental conditions, track geographical changes and predict natural disasters accurately.

Efficient Interpretation - Scene classification in remote sensing drastically improves the ability to interpret vast data sets, derive inferences and predict trends much quicker than manual interpretations.

Budget-friendly - Compared with costly field research, scene classification in remote sensing proves a cost-effective solution. This reduces expenditure on collecting geographical data over time and across vast geographies.

Many advantages from using scene classification in remote sensing applications have been noted. These include:

• High Efficiency: Scene classification reduces the cost and time for data collection and interpretation by completely eliminating the need for physically visiting the geographical location.
• Accurate Predictions: The advanced algorithms used in scene classification are capable of analyzing trends and predicting changes effectively.
• Extensive Area Coverage: Satellites and aerial platforms can cover enormous geographical areas, providing comprehensive data for analysis, inaccessible by any other means.
• Up-to-Date Information: Regular data collection ensures the availability of the most recent information about the observed area, vital for mapping, monitoring, and decision-making processes.
• Versatility: The range of applications for scene classification in remote sensing is inclusive but not limited to agriculture, forestry, geology, ecology, climatology, disaster management, and urban planning.
• Improved Disaster Response: Effective scene classification could accelerate disaster response by identifying hard-hit areas, mapping the extent of the damage, tracking changes and aiding formulation of remedial action plans.

Despite its numerous advantages, scene classification in remote sensing does have certain limitations:

• Environmental Factors: Weather conditions like clouds, haze, or smoke can distort or obstruct the data collected.
• Difficulty in Classification: Complex scenes with mixed features could be challenging to classify precisely.
• Temporal Limitations: Some remote sensing platforms may have limited revisit times, which might not coincide with the area's optimal viewing conditions or changes.
• Investment: Initial investment on advanced sensing platforms, regular data collection, software, and skilled manpower can be significant.

Implementing scene classification in remote sensing requires a well-thought-out approach that aligns with the objectives of a particular project or study. An in-depth understanding of the area of interest, the purpose of the study, selection of the best classification method, accurate data collection, and efficient interpretation of results are all crucial steps in a successful scene classification implementation.

In addition, continuous monitoring and updating processes, accounting for possible anomalies and changes, and cross-verification through ground truthing methods are crucial for enhancing the accuracy and reliability of scene classification in remote sensing.

Thus, scene classification in remote sensing is an exploratory tool providing comprehensive geospatial data that aids in the effective management and understanding of our environment. Despite its limitations, considered strategic planning and skilled interpretation can help overcome these and employ this tool to its fullest potential.