A Semantic-Preserving Deep Hashing Model for Multi-Label Remote Sensing Image Retrieval

Conventional remote sensing image retrieval (RSIR) systems perform single-label retrieval with a single label to represent the most dominant semantic content for an image. Improved spatial resolution dramatically boosts the remote sensing image scene complexity, as a remote sensing image always contains multiple categories of surface features. In this case, a single label cannot comprehensively describe the semantic content of a complex remote sensing image scene and therefore results in poor retrieval performance in practical applications. As a result, researchers have begun to pay attention to multi-label image retrieval. However, in the era of massive remote sensing data, how to increase retrieval efficiency and reduce feature storage while preserving semantic information remains unsolved. Considering the powerful capability of hashing learning in overcoming the curse of dimensionality caused by high-dimensional image representation in Approximate Nearest Neighbor (ANN) search problems, we propose a new semantic-preserving deep hashing model for multi-label remote sensing image retrieval. Our model consists of three main components: (1) a convolutional neural network to extract image features; (2) a hash layer to generate binary codes; (3) a new loss function to better maintain the multi-label semantic information of hash learning contained in context remote sensing image scene. As far as we know, this is the first attempt to apply deep hashing into the multi-label remote sensing image retrieval. Experimental results indicate the effectiveness and promising of the introduction of hashing methods in the multi-label remote sensing image retrieval.

Rescuing Deep Hashing from Dead Bits Problem

Deep hashing methods have shown great retrieval accuracy and efficiency in large-scale image retrieval. How to optimize discrete hash bits is always the focus in deep hashing methods. A common strategy in these methods is to adopt an activation function, e.g. sigmoid() or tanh(), and minimize a quantization loss to approximate discrete values. However, this paradigm may make more and more hash bits stuck into the wrong saturated area of the activation functions and never escaped. We call this problem "Dead Bits Problem (DBP)". Besides, the existing quantization loss will aggravate DBP as well. In this paper, we propose a simple but effective gradient amplifier which acts before activation functions to alleviate DBP. Moreover, we devise an error-aware quantization loss to further alleviate DBP. It avoids the negative effect of quantization loss based on the similarity between two images. The proposed gradient amplifier and error-aware quantization loss are compatible with a variety of deep hashing methods. Experimental results on three datasets demonstrate the efficiency of the proposed gradient amplifier and the error-aware quantization loss.