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Using relevance feedback for classifying remote sensing images

This paper presents an interactive technique for remote sensing image classification. In our proposal, users are able to interact with the classification system, indicating regions which are of interest. Furthermore, a genetic programming approach is used to learn user preferences and combine image region descriptors that encode spectral and texture properties. The approach to classify images can be divided into four main steps: (i) image partition and region feature extraction, (ii) identification of the partitions which are of interest, (iii) image segmentation, and (iv) region vectorization. This work describes the obtained results from the first two main steps: partition/extraction of image features and recognition of partitions of interest. So, in the first step the image are partitioned into tiles. Each tile is considered as an independent image and this process starts by the indication of a query image by the user. This query image is assumed to present the same texture and spectral properties of the RSI regions which are of interest. A similarity search is performed and the most similar tiles are returned to the user. The user then indicates if the returned tiles are relevant or non-relevant. By using this feedback, the classification system learns the user needs and tunes itself in order to improve the results in the next iteration. This process is repeated until the user is satisfied with the result. Experiments demonstrate that the proposed method is effective and suitable for image classification tasks.