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Land cover refers to the surface cover on the ground, whether vegetation, urban infrastructure, water, bare soil or other. Identifying, delineating and mapping land cover is important for global monitoring studies, resource management, and planning activities. The information of crop monitoring is most important for food security and it helps to improve our knowledge about the role of agriculture on climate change and crop type identification. This work focuses on an automated KNN classification system for identifying land cover and crop type in Synthetic Aperture Radar (SAR) images. In the first module an unsupervised Kohonen’s Self-Organizing Mapping (SOM) neural network is used for identifying the land type.
In the second module, the local binary pattern (LBP) based features are extracted for identifying the crop type in the crop covered area. The extracted features are given to KNN classifier which classifies the type of crop. Introduction Agriculture is the primary backbone of Indian economy where in about 70% of the population depends on agriculture. In agriculture the parameters like canopy, yield and quality of product were the important measures from the Farmers point of view (Viraj et al,2012).India is top producer country of many crops. The major crops in India can be divided into four categories viz. Food grains, Cash Crops, Plantation Crops and Horticulture crops. Learning multistage and deep representations for classifying remotely sensed imagery (Zhao et al 2016) Land cover refers to the surface cover on the ground, whether vegetation, urban infrastructure, water, bare soil or other. Identifying, delineating and mapping land cover is important for global monitoring studies, resource management, and planning activities. Identification of land cover establishes the baseline from which monitoring activities (change detection) can be performed, and provides the ground cover information for baseline thematic maps. The information of crop monitoring is most important for food security and it helps to improve our knowledge about the role of agriculture on climate change, crop type identification, land cover etc. (Ajay et al 2012)
Measurement of crop types leads to numerical descriptions of the crop, it helps to determine a problem that is big enough to fix or small enough to ignore. 3-D CNN-based FE model with combined regularization to extract effective spectral–spatial features of hyper spectral imagery. The proposed 3-D deep CNN to provide excellent classification performance under the condition of limited training samples.The design of proper deep CNN models is quite difficult. Nataliia Kussul1 et al(2016) proposed the methodology for solving the large scale classification and area estimation problems in the remote sensing domain on the basis of deep learning paradigm. It is based on a hierarchical model that includes self-organizing maps (SOM) for data pre-processing and segmentation (clustering), ensemble of multi-layer perceptions (MLP) for data classification and heterogeneous data fusion and geospatial analysis for post-processing.
An ensemble of methods (“mixture of experts” approach) should be exploited to take advantage of different processing methods and techniques. The processing of kernel function in clustering has more time complex. Christopher McCool et al (2016) proposed a novel crop detection system applied to the challenging task of field sweet pepper (capsicum) detection. The field-grown sweet pepper crop presents several challenges for robotic systems such as the high degree of occlusion and the fact that the crop can have a similar color to the background (green on green). To overcome these issues, They proposed a two-stage system that performs per-pixel segmentation followed by region detection. This approach has the advantage of providing robustness against occlusion (since features are only taken from a small region) as well as minimizing the amount of laborious annotation (as only the crop class needs to be annotated).The accuracy of the harvest segmentation is low.
Adriana Romero et al (2016) Proposed the Greedy layer wise unsupervised pre training coupled with a highly efficient algorithm for unsupervised learning of sparse features .The algorithm is rooted on Sparse Representation and enforces both population and life time scarcity of the extracted features simultaneously. The advantage of using spatial information is combining high number of output features and max-pooling steps in deep architectures is crucial to achieve excellent result .To access the generalization of the encoded features in multitemporal and multiannual image settings J. Théau et at (2016) describes that overview of change detection techniques applied to Earth observation and he used methodology such as Image differencing, principal component analysis, post-classification comparison, Change Detection technology. The main advantage of the paper is change detection algorithms have their own merits and no single approach is optimal and applicable to all cases.
The data selection is a critical step in change detection. Summary Traditional unsupervised classification algorithms, such as maximum likelihood classification, use clustering techniques to identify spectrally distinct groups of data and are the earliest approach of land cover automatic classification that has employed pattern recognition techniques. The drawback 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 selecting training samples. Therefore, the result of land cover classification is influenced greatly by classification participants, and it is impossible to classify land cover automatically with these methods.
Furthermore, the algorithms such as neural network classification and fuzzy logic classification are highly complicated in their algorithm basis which makes them difficult to understand and apply widely. Decision tree classification methods are widely used in large areas, such as global land cover mapping. The main problem presented by decision tree classification is the construction of the decision tree and the assignment of thresholds for each sub nodes, which heavily depends on human experience and varies spatially and temporally. Proposed Work. Proposed System Architecture The proposed architecture of land cover and crop type classification is shown in Figure3.1.The various step of the proposed work are explained in this section. The input images captured from SAR satisfy the quality requirements necessary for land cover and crop type identification. In the first stage of the proposed work the input image is segmented using the self-organization map (SOM) based technique.
The self-organization map techniques is a type of artificial neural network that is trained using unsupervised learning to produce a low dimensional representation of the input space of the training samples. It is differ from other artificial neural network as they apply competitive learning as opposed to error–correction learning and in the sense that they use a neighborhood function to preserve the topological properties of the input space. The Self- Organizing Map is an unsupervised learning technique, which forms a non-linear mapping of a high dimensional input space into typically two-dimensional grid of artificial neural networks. In picSOM, a separate SOM is trained for each feature type. Though this mapping, feature vectors that reside near each other in the input space are mapped into nearby units on the map. Consequently image lets that are mutually similar in respect to given features have located near each other in the SOM.
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