Selection of Unlabeled Source Domains for Domain Adaptation in Remote Sensing

In the context of supervised learning techniques, it can be desirable to utilize existing prior knowledge from a source domain to estimate a target variable in a target domain by exploiting the concept of domain adaptation. This is done to alleviate the costly compilation of prior knowledge, i.e., training data. Here, our goal is to select a single source domain for domain adaptation from multiple potentially helpful but unlabeled source domains. The training data is solely obtained for a source domain if it was identified as being relevant for estimating the target variable in the corresponding target domain by a selection mechanism. From a methodological point of view, we propose unsupervised source selection by voting from (an ensemble of) similarity metrics that follow aligned marginal distributions regarding image features of source and target domains. Thereby, we also propose an unsupervised pruning heuristic to solely include robust similarity metrics in an ensemble voting scheme. We provide an evaluation of the methods by learning models from training data sets created with Level-of-Detail-1 building models and regress built-up density and height on Sentinel-2 satellite imagery. To evaluate the domain adaptation capability, we learn and apply models interchangeably for the four largest cities in Germany. Experimental results underline the capability of the methods to obtain more frequently higher accuracy levels with an improvement of up to almost 10 percentage points regarding the most robust selection mechanisms compared to random source-target domain selections

Temporal - spatial recognizer for multi-label data

Pattern recognition is an important artificial intelligence task with practical applications in many fields such as medical and species distribution. Such application involves overlapping data points which are demonstrated in the multi- label dataset. Hence, there is a need for a recognition algorithm that can separate the overlapping data points in order to recognize the correct pattern. Existing recognition methods suffer from sensitivity to noise and overlapping points as they could not recognize a pattern when there is a shift in the position of the data points. Furthermore, the methods do not implicate temporal information in the process of recognition, which leads to low quality of data clustering. In this study, an improved pattern recognition method based on Hierarchical Temporal Memory (HTM) is proposed to solve the overlapping in data points of multi- label dataset. The imHTM (Improved HTM) method includes improvement in two of its components; feature extraction and data clustering. The first improvement is realized as TS-Layer Neocognitron algorithm which solves the shift in position problem in feature extraction phase. On the other hand, the data clustering step, has two improvements, TFCM and cFCM (TFCM with limit- Chebyshev distance metric) that allows the overlapped data points which occur in patterns to be separated correctly into the relevant clusters by temporal clustering. Experiments on five datasets were conducted to compare the proposed method (imHTM) against statistical, template and structural pattern recognition methods. The results showed that the percentage of success in recognition accuracy is 99% as compared with the template matching method (Featured-Based Approach, Area-Based Approach), statistical method (Principal Component Analysis, Linear Discriminant Analysis, Support Vector Machines and Neural Network) and structural method (original HTM). The findings indicate that the improved HTM can give an optimum pattern recognition accuracy, especially the ones in multi- label dataset