Transparent Peer Review By Scholar9
MULTICLASS CLASSIFICATION OF REMOTE SENSING USING ALEX NET
Abstract
With over 160 publications, this article offers a thorough analysis of deep learning techniques for remote sensing image scene classification. It addresses the primary drawbacks of these techniques, which include generative adversarial networks, autoencoder-based techniques, and convolutional neural networks. Along with introducing benchmarks for remote sensing image scene categorization, the study provides an overview of over two dozen methods' performance on three datasets. It also talks about interesting directions for future study.Deforestation in the Amazon rainforest leads to reduced biodiversity, habitat loss, and climate change. A novel remote sensing image classification framework is proposed to manage deforestation effectively. The framework uses an attention module to separate features from CNN and LSTM networks, and a loss function to calculate co-occurrence matrix and assign weights to labels. Experimental results show improved multi-label image classification performancePatch-based multi-scale completed local binary pattern (MS-CLBP) features are used in the suggested remote sensing picture scene classification method, and local patch descriptors are extracted using a Fisher vector (FV). These attributes are encoded into a discriminative representation by the method using Fisher vector encoding. Several FVs are generated using different settings, and classification is handled using a kernel-based extreme learning machine (KELM). The approach performs better on two benchmark datasets.
Sandhyarani Ganipaneni Reviewer
11 Oct 2024 12:47 PM
Approved
Relevance and Originality
This article addresses a significant issue—remote sensing image scene classification—especially in the context of deforestation and its environmental impacts. With over 160 publications, it contributes to the existing body of knowledge by offering a comprehensive analysis of deep learning techniques, their drawbacks, and proposing a novel framework for effective classification. The focus on using an attention module to combine CNN and LSTM networks is original, showcasing innovative methods to enhance classification accuracy in remote sensing applications.
Methodology
The methodology section appears to be robust, detailing the use of generative adversarial networks, autoencoder-based techniques, and convolutional neural networks (CNNs) alongside the proposed framework. However, more specifics about the datasets used for experimentation would strengthen the methodology. For instance, providing details about the characteristics of the three datasets (e.g., size, variety of scenes, geographical relevance) and the preprocessing steps applied would be beneficial. The explanation of using patch-based multi-scale completed local binary pattern (MS-CLBP) features and Fisher vector (FV) encoding is compelling but could use additional clarity on how these techniques enhance feature extraction and classification.
Validity & Reliability
The article provides experimental results that suggest improved performance in multi-label image classification. However, it would be essential to discuss the validation process used to ensure the results' reliability. For example, were cross-validation techniques implemented? How were performance metrics calculated? Including details on how the performance of the proposed framework compares with existing methods, particularly in real-world scenarios, would also enhance the assessment of its validity.
Clarity and Structure
The article is generally well-organized, moving from a discussion of existing techniques to the introduction of the novel framework. However, some sentences could be clearer, particularly in explaining complex concepts such as the attention module and co-occurrence matrix. Visual aids, such as diagrams or flowcharts illustrating the framework and its components, would significantly enhance understanding and engagement. Clear section headings and subheadings could also help guide the reader through the article's content.
Result Analysis
The experimental results indicate that the proposed framework performs better on benchmark datasets, which is promising. However, a more in-depth discussion regarding the implications of these findings in real-world applications—particularly concerning deforestation management—would be valuable. The article could benefit from a discussion of potential limitations or challenges in applying the framework in practice, such as the computational requirements or the necessity of high-quality training data. Additionally, identifying areas for future research, such as integrating other data sources (e.g., socioeconomic factors or climate data) or enhancing the framework's scalability, would provide a more comprehensive conclusion to the study.
IJ Publication Publisher
ok madam
Sandhyarani Ganipaneni Reviewer