The time-series changes that are affected by a specific alteration cannot be accurately found using change detection techniques. However, there are not many techniques available for identifying seasonal changes. This study develops a new deep model for identifying reflectance materials depending on optimization. At time instances “t” and “t-1” the images are accumulated in this instance. The Type 2 Fuzzy and Cuckoo Search-Based Filter (T2FCS) is used for the pre-processing of two images. Then, Fuzzy Local Information C-Means (FLICM) is used to segment both images. On the image accumulated at time “t” the feature extraction is performed to extract statistical features like mean, variance, kurtosis, energy, skewness, and standard deviation as well as imperative features like Local Optimal Oriented Pattern (LOOP), Local Ternary Pattern (LTP), Local Gabor XOR Pattern (LGXP), and others. The reflectance material is then categorized using a Deep Neuro-Fuzzy Network (DNFN) with segments created from images collected at time instance “t-1” and features collected from “t”. The proposed Jaya Student psychology-based optimization (JSPO), which was created by fusing the Jaya algorithm and the Student psychology-based optimization method(SPBO), is used here to train the DNFN. The segmented output that was collected at times “t” and “t-1” is then used to do the change detection. With the highest accuracy (94.1%) and maximum sensitivity (93.8%), the proposed JSPO-based DNFN demonstrated improved performance. For applications involving optical imagery, the Landsat program is crucial.