2 minute read
Red - Landsat 7 Band
But now it falls under the category of the fastest growing city in the north-eastern region. So, it becomes important to detect and monitor the nature and frequency of changes to predict and analyze the direction of future expansion. With these aims, we have come up with the following objectives listed below. 1) Collecting and selecting Landsat Satellite images of GM region 2) Preprocessing of collected satellite images. 3) Preparing Labels for supervised learning and validating class labels from field surveys and high-resolution maps in an unsupervised way. 4) Land use Land cover detection, prediction, and classification. 5) Accuracy assessment. 6) Analysis 7) Comparison with other techniques The rest of the paper is organized into sections. Section II will discuss the related literature. Section III will give an introduction of the study area and its importance and Section IV discuss the motivation and methodology. Section V focus on the results and analysis. Section VI will discuss the comparison of our model with other techniques and lastly, section VII has the conclusion.
TABLE I
Advertisement
LAND COVER INFLUENCING FACTORS Direct Influencing Factors Indirect Influencing Factors Increasing flood Weak society economy Soil erosion in the Brahmaputra River Improper Planning Habitat destruction Sport and communication disruption. Landslide activity Monitoring Problems
II. LITERATURE STUDY
The classification of satellite images involves the classification of the binary class of categorical classes. The binary class classification is generally done for Built-up area or Water bodies segmentation from satellite images. With the recent enhancement of various machine learning techniques, the LULC mapping for multiple classes has been attracting considerable attention. Yansheng et al [13] proposed Multi-Label Remote Sensing Image Scene Classification by Combining a Convolutional Neural Network and a Graph Neural Network. [14] included NDVI, MNDWI, NDBI, and Sentinel 2A bands to classify vegetation, water, and built-up areas. He used a multilayered feed-forward deep neural network to classify different Land Cover classes. Literature also suggested the use of pre-trained models like VGG16, Inception-ResNet-V2, Inception-V3, and DenseNet201 to extract features from satellite data [15]. The multispectral data can be classified using a supervised or unsupervised learning approach. The supervised classification can be done either traditional pixel-wise or patch-based. In pixelwise classification, each pixel is individually mapped with the corresponding spectral data. Many machines learning models like random forest, support vector machines, and self-organizing maps use this approach for learning. But this approach does not consider spatial information completely in learning as cand cover pixels may correlate with neighboring pixels [16]. Another approach is patch-based that uses spatial patterns in classification. Here instead of using individual pixels for learning, it also includes the neighboring pixels to extract information to learn. 2D-CNN can be used to extract features from neighboring pixels. It uses multiple layers to learn. This study includes multispectral data, i.e., the dataset will consist of multiple bands to form a 3D structure. This structure is fed to CNN to extract features. Many pieces of the literature suggested the use of spectral indices to classify land cover classes [17, 18]. [17] used water indies TCW, NDVI, MNDWI, AwEisnh, Aweish, and WRI for mapping urban areas and watersheds. They have used a supervised classification-based Minimum Distance algorithm in ArcGIS to map the water bodies. Indices like Automated Built-up Extraction Index (ABEI) [19], Normalized Difference built-ups Index (NDBI) [20], Index-Based Built-up Index (IBI) [21], and Modified Built-Up Index (MBUI) were developed to classify built-up class and other class in satellite images [18].
