Land cover refers to the surface cover of the land, whether vegetation, urban infrastructure, water, bare soil or other. Land cover identification, delineation and mapping are important for global monitoring studies, resource management and planning activities. Information from crop monitoring is very important for food security and contributes to improving our knowledge on the role of agriculture in climate change and the identification of crop types. This work focuses on an automated KNN classification system to identify land cover and crop type in synthetic aperture radar (SAR) images. In the first module, an unsupervised Kohonen Self-Organizing Mapping (SOM) neural network is used to identify the terrain type. Say no to plagiarism. Get a tailor-made essay on “Why Violent Video Games Should Not Be Banned”? Get an original essay In the second module, features based on local binary pattern (LBP) are extracted to identify the crop type in the area covered by the crops. The extracted features are fed to the KNN classifier which classifies the crop type. Introduction Agriculture is the main pillar of the Indian economy, where around 70% of the population depends on agriculture. In agriculture, parameters such as canopy, yield and product quality were important measures from the farmers' point of view (Viraj et al, 2012). India is the leading producing country of many crops. The major crops in India can be divided into four categories, viz. Food grains, cash crops, plantation crops and horticultural crops. Learning deep, multi-step representations to classify remote sensing images (Zhao et al 2016) Land cover refers to the ground surface coverage, whether vegetation, urban infrastructure, water , bare ground or other. Land cover identification, delineation and mapping are important for global monitoring studies, resource management and planning activities. Land cover identification establishes the baseline from which monitoring activities (change detection) can be carried out and provides land cover information for thematic reference maps. Information from crop monitoring is very important for food security and helps improve our knowledge on the role of agriculture in climate change, identification of crop types, land cover, etc. (Ajay et al 2012). Measuring crop types leads to numerical descriptions of the harvest, this helps determine a problem large enough to solve or small enough to ignore. CNN-based 3D FE model with combined regularization to extract effective spectral-spatial features from hyperspectral imagery. The proposed 3D deep CNN provides excellent classification performance under conditions of limited training samples. Designing suitable deep CNN models is quite difficult. Nataliia Kussul1 et al (2016) proposed the methodology to solve large-scale classification and area estimation problems in the field of remote sensing based on the deep learning paradigm. It is based on a hierarchical model that includes self-organizing maps (SOM) for preprocessing anddata segmentation (clustering), a set of multi-layer perceptions (MLP) for data classification and fusion of heterogeneous data and geospatial analysis for post-processing. . A set of methods (“mix of experts” approach) should be exploited to take advantage of different treatment methods and techniques. Processing the kernel function in clustering is more complex in terms of time. Christopher McCool et al (2016) proposed a novel crop detection system applied to the difficult task of sweet pepper (capsicum) detection. Growing field-grown sweet peppers presents several challenges for robotic systems, such as the high degree of occlusion and the fact that the crop may have a similar color to the background (green on green). To overcome these problems, they proposed a two-step system that performs pixel-wise segmentation followed by region detection. This approach has the advantage of providing robustness against occlusion (since features are only extracted from a small region) as well as minimizing the amount of laborious annotations (since only the crop class needs to be annotated) . The accuracy of crop segmentation is low. Adriana Romero et al (2016) proposed unsupervised pre-training by Greedy layer coupled with a highly efficient algorithm for unsupervised learning of sparse features. The algorithm is based on the sparse representation and simultaneously applies the population and lifetime sparsity of the extracted features. The advantage of using spatial information is that the combination of a high number of output features and maximum pooling steps in deep architectures is crucial to achieve excellent results. To access the generalization of features encoded in multi-temporal and multi-year image parameters J. Théau et at (2016) describes this overview of change detection techniques applied to Earth observation and he used methodologies such as image differentiation, principal component analysis, post-classification comparison, change detection technology. The main takeaway from this article is that change detection algorithms have their own merits and no single approach is optimal and applicable to all cases. Data selection is a critical step in detecting changes. Abstract Traditional unsupervised classification algorithms, such as maximum likelihood classification, use clustering techniques to identify spectrally distinct groups of data and are the first approach to automatic land cover classification using pattern recognition techniques . The disadvantage of these algorithms is that the accuracy of land cover classification is not guaranteed and the land cover classifications are arbitrary. Supervised classification methods require substantial expertise and human participation for the selection of training samples. Therefore, the result of land cover classification is greatly influenced by the classification participants, and it is impossible to automatically classify land cover with these methods. Additionally, algorithms such as neural network classification and fuzzy logic classification are very complex in their algorithmic basis, making them difficult to understand and apply on a large scale. Decision tree classification methods are widely used in large areas, such as global coverage mapping..