Remote sensing satellite image processing techniques for image classification: a comprehensive survey

This paper is a brief survey of advance technological aspects of Digital Image Processing which are applied to remote sensing images obtained from various satellite sensors. In remote sensing, the image processing techniques can be categories in to four main processing stages: Image preprocessing, Enhancement, Transformation and Classification. Image pre-processing is the initial processing which deals with correcting radiometric distortions, atmospheric distortion and geometric distortions present in the raw image data. Enhancement techniques are applied to preprocessed data in order to effectively display the image for visual interpretation. It includes techniques to effectively distinguish surface features for visual interpretation. Transformation aims to identify particular feature of earth’s surface and classification is a process of grouping the pixels, that produces effective thematic map of particular land use and land cover

Post classification change detection based on feature-based ensemble classifiers

Change detection is a challenging task in the field of remote sensing. Mainly, the change map is used for disaster assessment, monitoring deforestation and urban studies. In this paper, we present a novel method for post classification change detection. Google Earth images of 2011 and 2016 of Bangalore East are used for the study. Multiple features such as texture features, morphological features are extracted using grey level co-occurrence matrix (GLCM) and morphological operations respectively. Linear discriminant analysis (LDA) is used to reduce the dimension of the selected features for the training set. The proposed ensemble classifier system (ECS) exploits K-nearest neighbour (KNN), support vector machine (SVM) and maximum likelihood classifier (MLC). The proposed method adopts the subsample kernel-based

Feature-based Land Use/Land Cover Classification of Google Earth Imagery

In this paper, we presented a novel method to classify land use/land cover objects of earth surface using google earth imagery. The Gray Level Co-occurrence Matrix (GLCM) is used to extract second order statistical features. Opening, closing, and reconstruction operators are used to extract morphological features of objects. The extracted texture features and morphological features are fused to improve the classification accuracy. The spectral reflectance of the endmember classes is calculated using Linear Spectral Mixture Model (LSMM). Multiple thresholds are generated for each class. The generated spectral properties and thresholding are used to classify features set. The classification accuracy of the resulted thematic map of the specified geographical area is compared with the generic KNN method

Generation of digital elevation map for steep terrain region using Landsat-7 ETM+ imagery

Generation of Digital Elevation Map (DEM) is a challenging task, because it is an extraction of 3-dimensional information from 2-dimensional image. In the proposed method, we have generated DEM for steep terrain region using Landsat 7 ETM+ imagery. The Rational Polynomial Coefficients (RPCs) are used to generate epipolar line, epipolar resampling generates template image. Normalized Correlation Coefficient (NCC) is used as template matching technique to match template image with target image. The RMSE at elevation points are calculated against Ground Control Points (GCPs). The proposed approach is a replacement of physical sensor model which are used to generate DEM

Land Use/Land Cover Segmentation of Satellite Imagery to Estimate the Utilization of Earth’s Surface

Land Use/Land Cover (LULC) mapping plays a major role in land management applications such as to generate community map, proper urban planning, and disaster risk management. Proposed algorithm efficiently segments different land use/land cover classes such as buildings, trees, bare land, and water body. RMS value based multi-thresholding technique is used to segment various land use/land cover classes and consequently using the binning technique to accurately estimate the utilization of earth’s surface. The proposed algorithm is tested on two different data sets of Bengaluru city, India. The percentage utilization of surface objects for grid 7 image of dataset I is found to be 96.68% building, 1.05% vegetation, and 0.22% barren land, the area covered in grid 7 of dataset I is identified as overutilized land. Percentage utilization of surface objects for grid 8 of dataset II is found to be 68.95% building

Land Use/Land Cover Classification of Google Earth Imagery

Google Earth is a source of high spatial resolution images. The freely available Google Earth (GE) images are utilized to generate Land use/Land cover thematic map of the highly heterogeneous landscape of typical urban scene. In this paper, we have presented Euclidean Distance and Average Pixel Intensity based K-NN classification to classify five different land objects. The classification accuracy of the proposed method is compared against generic K-NN. The overall classification accuracy and the kappa value of generic K-NN are found to be 75.04% and 0.74 respectively. Whereas, proposed method results with 76.38% and 0.78. Both the methods exhibits classification error because of poor spectral reflectance properties of google earth imagery